# Artificial Intelligence Sponge City Wisdom Management Model

Authors: Dan Nie; Hualiang Fang
Journal: Security and Communication Networks
 (2022)

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## Abstract

Sponge city is currently a new urban rain flood management concept. It refers to the natural disaster that adapts to environmental changes and the natural disasters that is brought by rainwater. It is also known as the sponge, and the alias can also be called as “water elastic city.” The purpose of this article is, based on artificial intelligence, to study the analysis of wisdom management models in sponges. This paper first introduces the artificial intelligence algorithm and sponge city, then compares the traditional sponge city and the wisdom sponge city, then creates a LSTM neural network model, introducing artificial intelligence into sponge city intelligent dynamics in the analysis, and finally compares the rainfall data analysis to the ground. The experimental results show that, at different time points, the training results of rainfall data have shown significant regularity. The entire rainfall process exhibits a rise-decline-rise-increase trend of repetition. The maximum rainfall appeared at the 18th hour, and 7 obvious peaks occurred in 7, 11, 14, 16, 18, 21, and 23 hours.


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## Body

## 1. Introduction

Under the current new situation of urban management, the concept of “sponge city” is put forward for innovative management and construction. Sponge city is an innovative performance that can achieve green building construction, create low-carbon city development, and create smart city formation. It is also the background of this era characteristics and the current green new technology, society, human geography, and organic combination. The addition of artificial intelligence to the urban management mode brings more convenience and optimization to the urban management, to form a sponge city intelligent management mode based on artificial intelligence. This will be a good challenge and change for city management.As today’s urbanization management is very superior, the study of how managers use artificial intelligence to manage a city quickly and effectively and how to control the various problems in the city, which is also of profound significance for the development and expansion of artificial intelligence. The intelligent management mode of the sponge city means to use artificial intelligence to comprehensively and effectively manage the city. In recent years, scholars have studied the theme of sponge city, but relatively few of them have applied artificial intelligence to study the management mode of the sponge city. Therefore, the application of artificial intelligence to the research of sponge city has both theoretical and practical significance.The innovation of this paper lies in the following: (1) In the management of cities, that is, in the sponge city management mode, artificial intelligence can be used to make a comprehensive and effective choice. The application of artificial intelligence to the research of sponge city, in other applications, artificial intelligence is often used as a high-tech research, and this paper is committed to deeply explore the characteristics of artificial intelligence applied in the sponge city management mode. (2) It used the LSTM neural network model to study the research and analysis of wisdomized sponges, thus obtaining more scientifically accurate data.


## 2. Related Work

Due to the proposal of the concept of smart cities, the concept of sponge city is also coming. At present, the intelligence of urban management has become a very hot topic, and many researchers have launched them. Among them, Warnecke et al. introduce the concept and implementation of smart urban maturity evaluation and benchmark tools, which follows scientific design methods. The definition of the application target is based on documentary review and related reference test tools [1]. However, he studied the lack of new ideas and did not have actual effective experimental data. Xia et al. studied investigating urban issues in China's rapid urbanization and related demands and problems related to water problems in sponge urban construction and proposed the opportunities and challenges of sponge urban construction [2]. However, his research did not add realistic data, so it is difficult to apply to practical applications. Wang et al. have different insights, and they summarize and discuss the connotations, goals, and characteristics of sponge cities in a comprehensive summary of sponge cities [3]. However, their model theory is relatively backward, so the data is not very pleasant. Later, Wang et al. analyzed and studied the sponge in Pingxiang city construction and used the onsite survey and literature data. However, the sample taken in his experiment is too small and does not represent the overall data [4]. According to the above scholars, Cheng et al. built the InfoWorks ICM (integrated catchment modeling, integrated basin model), a rainwater network model in the Xinglong sponge city test area, and simulated the flood in the sponge city test area under different rainfall scenes [5]. However, his research lacks theoretical knowledge as a base paving. Chen et al. used self-developed penetration factor test equipment and the effects of two permeable asphalt mixes (PAM) and three permeable cement concrete (PCC) structures on different structural waterproof pavement attenuation [6]. However, he did not combine the actual research, so the experiment application range is relatively small. Later, Feng and Yamamoto determined the difference between the differential items in the study to understand the main design form and concept of the China’s sponge city concept (SCC) project. He also aimed to review the ten pilot projects of Shanghai to infer their main features and the process required to effectively implement the SCC project [7]. However, his research report is basically all data-based results, lacking a certain textual summary.


## 3. Sponge City Wisdom Management Model Method

### 3.1. Artificial Intelligence Algorithm

#### 3.1.1. Box–Jenkins Model Theory

In the existing urban management field, three research methods dominated in urban management prediction research: time sequence model, machine learning method, and depth learning method [8]. The essence of machine learning and depth learning is a method of manual intelligence. Machine learning and deep learning are both artificial intelligence methods in nature. Because of its complexity and the extent of the current research, this paper will come out separately.The Box–Jenkins model is a time sequence model that is used to predict the time series, often used in demand forecasting and planning [9].According to the average movement of the white noise in the Box–Jenkins model, it still maintains a steady state. It can be obtained from the following lag-weighted average formula:(1)YR=μ+μR+θ2μR−2+⋯⋯+θOμR−W,YR−μ=1+θ2K+θ2K2+⋯+θOKW,andIR=ϑKIR.When it returns the current value, the representative formula is(2)YR=δ+∅1YR−1+∅2YR−2+⋯⋯+∅OYR−W+IR,δ−IR=1−∅1K−∅2K2−⋯−∅OKW, andYR=∅KYR.When the differential is returned, the formula obtained is(3)∅KΔFYR=δ+ϑKIR.When the self-return is processed, the formula also changes:(4)YR=δ+Q1YR−1+Q2YR−2+⋯⋯+QOYR−O+IR+Q1IR−1+Q2IR−2+⋯⋯+QWIR−W,QKΔFYR=ϑKIR.The conversion process can be expressed as a formula:(5)ΔYR=YR−YR−1=YR−KYR=1−kYR,Δ2YR=YR−YR−1=1−KYR−KYR−1=1−k2YR,ΔFYR=1−KFYR.Squaring sequence can be obtained by the following formula:(6)ER=ΔFYR=1−KFYR.The model form is(7)Q1=WR−1WR−2+⋯⋯+WOWR−O+δ+θ1μ1+θ1μR−1+⋯⋯+θWμR−W,QKΔFYR=δ+ϑKIR.According to this, the next value can be predicted as follows:(8)GY=ω⋅Y+A,ωεR.If the prediction is widely used, the core formula is(9)YL=LL⋅XL+1−LL⋅YL−1.You can get an output child by activation of the following formula:(10)JE,NY=GEFY=G∑O=1LEOY0+A.The formula of the output signal is also revealed as follows:(11)JIK=GEIKYIK+NIK,sigmoidY=11+RY,tanhY=RY−R−YRY+R−Y,ReLUY=BY,Y≥0,0,Y≤0.Among them, the stationary tests mainly rely on the ADF unit root inspection, the white noise test is mainly to prove that the original sequence is a nonwhite noise sequence, and the residual is a white noise sequence [10].


#### 3.1.2. Basic Model of Neural Network

A large number of simple neurons form a relatively complex neural network, and each neuron has several inputs and a unique output. The neurons are connected by varying weights [11]. The mathematical model is shown in Figure 1.Figure 1
Mathematical model of the neuron.Figure 2
Interconnect network.Figure 3
Layered feedforward network.Figure 4
Single-layer fully connected feedback neural network.Artificial neural network is divided into the following types:(1)
Interconnect Artificial Neural Network. Interconnect artificial neural networks represent each neuron of the practice exercises. The information exchange program of each neuron is created according to the switching on weight. When in the network training process, the total neural network is constantly strengthened based on the connection weight of each neuron. After the specified time, the neural network can rise to relatively smooth conditions [12]. The main advantage of this type of neural network is that each neuron is associated, and the neural network can reach a better condition through multiple test training. An amazing drawback is that the network training speed is very slow, as shown in Figure 2, each neuron has a connection to each other, and there is a relationship between interworking information, and the links between them are very convenient.(2)
Hierarchical Feedforward Artificial Neural Network. The feedforward artificial neural network, you can know what it means through its name. Its neurons are listed in a layer, which is a one-way transmission network model with an input and output signal [13]. This model is a single-layer multidirectional arrangement, which is divided into three levels. The first is the writing layer, then the implicit layer, and the final is the output layer, but there is a hidden layer, which can be one layer or multiple layers [14]. Each neuron can receive the input information of the previous layer, and then send the output radar to the next layer, and so on, one by one. It is different from the interconnected neural network, and the feedforward neural network does not have the reverse transmission between the next layer and the upper layer. (shown in Figure 3). The feedforward neural network is divided into a list of arrangements. The transfer of the message is transmitted downward, and a layer of neurons will only have the output behavior on the next layer and can only receive writing from the previous layer network. In the research design, the use rate is the BP neural network and the RBF network.(3)
Feedback Forward Network. Feedback forward network means that there is a forward network, which is given to the input layer. As shown in Figure4, the neural unit of this network is in place of order, which is clearly based on the network template obtained from the feedforward artificial neural network. In the process of learning, the written layer neurons of this network continue to obtain output layer neurons, and some will output the feedback writing layer neurons of the network. This network outputs data by feedback [15]. It is different from the feedforward neural network to feedback all nodes in the front network. However, in the feedforward neural network, the node is divided into writing units and computing units. Before the neurons in the network obtain the writings of the upper network neurons, the calculation can be transmitted to the following neural unit.


### 3.2. Sponge City

#### 3.2.1. Sponge City Concept

Sponge city is a new concept of urban rain flood management. Specifically, the city has good adaptability when facing environmental changes and ecological natural disasters due to precipitation. It sufficiently absorbs savings and purifies rain water during precipitation and then releases the stored water for a certain need and integration. The internationally similar concept is called “low influence development rainwater system” [16].


#### 3.2.2. Related Research Abroad

Foreign research and exploration of “sponge city” construction can be divided into two aspects: (1) one aspect is the research and practice of water system assessment and repair; (2) on the other hand, it is a summary and excavation refining of sponge city construction in different regions [17]. Representative theory and practice achievements: sources from the United States, the best management measures and green infrastructure concept, and low influence development mode from the EU’s water frame command framework, Germany’s natural open drainage system [18], British sustainable drainage system model, etc. In addition, there are urban design and development theories from New Zealand’s attention to low impact and Australia on water sensitivity urban design. The main developed countries or regions have made different measures and strategies in the management of water systems. It is not completely compiled, including the following aspects as shown in Table 1. As shown in Table 1, it can be seen that the measures and strategies of these countries basically have no difference, but it is applicable to their local countries.Table 1
Strategical pite on water system management.
CountriesName of the strategyContent of the strategyUnited StatesBest management practices, BMPs(A) Strengthening the control of storm runoff(B) General calculation methodsGermanyNatural drainage system, NDS(A) Reduce stormwater runoff(B) Connect stormwater corridorsUnited KingdomSustainable drainage system, SUDS(A) Attaches great importance to the control of stormwater runoff from the sources(B) Permeable pavementUnited StatesGreen infrastructure environment system(A) An ecological network structure consisting of nodes, linked corridors, and small open spaces in the network, which mixes natural and artificial facilitiesAustraliaWater sensitive urban design(A) Actively protect the existing natural features and protect the ecological environmentThe European UnionWater framework Directive, WFD(A) Restore the ecological health of the water body(B) Reduce or even eliminate the discharge of harmful substancesFigure 5
Structure of smart sponge city construction architecture.


### 3.3. Intelligent Urban Management Mode

(1)
In the existing research, many scholars analyzed the factors of smart cities from information technology, people, environment, organization, management, institutions, policy, and economy [19]. In smart cities, many of the above elements have different nature and characteristics, play different functions, their interconnection, interaction, and build a smart city.In the formula calculating the OCHIIA coefficient between keywords, the coefficients in the matrix diagonal representation represent the degree of correlation with itself, and its value is 1. After calculating, the total word matrix can transition into a keyword of similar matrix and has a value between 0 and 1, as shown in Table2. In the table, the size of the value reflects the distance between the two keywords. The larger the value, the closer the distance between the two keywords, the better the similarity, and the smaller the value, the farther the distance between the two keywords, the worse the similarity [20].According to multivariate statistics, the network characteristics of subnets relate to high-frequency keywords in five dimensions. According to multistatistical analysis, the division of high-frequency keyword categories is divided into strategic dimension, social dimension, economic dimension, support dimension, and social networks related to spatial dimension [21]. The network characteristic indicators reflected are shown in Table 3. It can be seen that the keywords in the five dimensions have a high degree of closeness, strong concentration, proximity, and intermediary. It can be seen that there is a strong link between high-frequency keywords in different dimensions, and 57 high-frequency keywords have certain rationality in these five dimensions.(2)
In 2008, IBM proposed “smart city,” which is another leap-forward revolution in the history of urban development in the world, providing a new urban reform transformation development path [22]. Big data age, “smart city,” theory refers to the opening of various systems and services in the city by using various information technology or innovation concepts. It is deeply integrated with informationization, industrialization, and urbanization to enhance the efficiency of data resource utilization. It optimizes urban management and public services to create a happy living environment, realizes interactive management, improves urban management efficiency, and improves public quality of life. Under the background of the current big data era, “smart city” small theory provides theoretical support to solve the new urban problem and optimized urban resource allocation, which also collides with the new network identification and provides high-quality modern life for the public. Optimizing the social environment of urban life is a driving force for urban sustainable development. Compared with the traditional theory, the theory of “smart city” is the benign interaction between the government, society, and citizens. It has the advantages of high technology, efficient government, and a series of theoretical advantages - smart citizens.(3)
The smart sponge city is monitored by the important position of the sponge facilities, the discharge of the rainwater entrance, the construction project, and the drainage pipe network key node, and it used actual monitoring data to reach with the construction of sponge city. This provides the basis for the analysis of urban water resources, water environment, water safety monitoring, and effect assessment. To manage sponge cities in a more fine and dynamic manner, eliminate the information between departmental information that cannot be shared and communicated and avoid retries in construction management to improve sponge city construction and management efficiency [23]. Compared with traditional sponge cities, the wisdom sponge city has the following features, as shown in Table 4.(4)
According to the abovementioned, the structure of the wisdom sponge city construction architecture is drawn, as shown in Figure5. In the construction of a smart sponge project, establishing a real-time monitoring and management system and puting three aspects of intelligence management measures are necessary. Real-time monitoring and management system provides real-time monitoring, program simulation, and effectiveness assessment, and sponge projects provide the basic data required for real-time monitoring management systems. Real-time monitoring management systems provide technical support for smart management measures and also provide information feedback for the system during administering.Table 2
Keywords for smart urban research.
Information communication technologyInfrastructureIntelligent applicationCity innovationInformatizationInformation communication technology1.0000000.2571720.1878120.2659080.233285Infrastructure0.2571731.0000000.304290.2666980.188982Intelligent application0.1878120.304291.0000000.2472070.184017City innovation0.2659080.2666980.2472071.0000000.209359Informatization0.2332850.1889820.1840170.2093591.000000Table 3
Network characteristic indicators of different dimension-related subnets.
Strategic subnetSocial subnetThe subnetSupport the subnetSpace subnetNetwork size2095185Network correlation0.76320.75000.80000.53590.9000Network degree center potential76.31675.00090.00054.24890.000Network proximity center potential82.06681.33892.00069.79092.000Network center potential1.3163.5713.3332.8593.333Table 4
Differences of traditional sponge cities and wisdom sponge cities.
Compare the projectTraditional sponge citySmart sponge cityAcquisition of informationThe collection of information data is scattered and incomplete, and the real-time information is poorThe information data collection is comprehensive and systematic, and the information is in real-timeSharing of informationCommunication between departments is not smooth, and there is information isolationEstablish a unified data sharing platform to realize real-time information feedback among departmentsPlan formulationMost decisions are made based on experience, lacking scientific basis and technical supportCloud computing, GIS, hydrological model, and other comprehensive technology support and can simulate the operation of the scheme, making the decision more accurate and intelligentMaintenance of facilitiesIt is inefficient to rely on manpower inspectionThe information platform can monitor the running status of the equipment in real time, so that faults can be detected in time and corresponding measures can be taken


## 3.1. Artificial Intelligence Algorithm

### 3.1.1. Box–Jenkins Model Theory

In the existing urban management field, three research methods dominated in urban management prediction research: time sequence model, machine learning method, and depth learning method [8]. The essence of machine learning and depth learning is a method of manual intelligence. Machine learning and deep learning are both artificial intelligence methods in nature. Because of its complexity and the extent of the current research, this paper will come out separately.The Box–Jenkins model is a time sequence model that is used to predict the time series, often used in demand forecasting and planning [9].According to the average movement of the white noise in the Box–Jenkins model, it still maintains a steady state. It can be obtained from the following lag-weighted average formula:(1)YR=μ+μR+θ2μR−2+⋯⋯+θOμR−W,YR−μ=1+θ2K+θ2K2+⋯+θOKW,andIR=ϑKIR.When it returns the current value, the representative formula is(2)YR=δ+∅1YR−1+∅2YR−2+⋯⋯+∅OYR−W+IR,δ−IR=1−∅1K−∅2K2−⋯−∅OKW, andYR=∅KYR.When the differential is returned, the formula obtained is(3)∅KΔFYR=δ+ϑKIR.When the self-return is processed, the formula also changes:(4)YR=δ+Q1YR−1+Q2YR−2+⋯⋯+QOYR−O+IR+Q1IR−1+Q2IR−2+⋯⋯+QWIR−W,QKΔFYR=ϑKIR.The conversion process can be expressed as a formula:(5)ΔYR=YR−YR−1=YR−KYR=1−kYR,Δ2YR=YR−YR−1=1−KYR−KYR−1=1−k2YR,ΔFYR=1−KFYR.Squaring sequence can be obtained by the following formula:(6)ER=ΔFYR=1−KFYR.The model form is(7)Q1=WR−1WR−2+⋯⋯+WOWR−O+δ+θ1μ1+θ1μR−1+⋯⋯+θWμR−W,QKΔFYR=δ+ϑKIR.According to this, the next value can be predicted as follows:(8)GY=ω⋅Y+A,ωεR.If the prediction is widely used, the core formula is(9)YL=LL⋅XL+1−LL⋅YL−1.You can get an output child by activation of the following formula:(10)JE,NY=GEFY=G∑O=1LEOY0+A.The formula of the output signal is also revealed as follows:(11)JIK=GEIKYIK+NIK,sigmoidY=11+RY,tanhY=RY−R−YRY+R−Y,ReLUY=BY,Y≥0,0,Y≤0.Among them, the stationary tests mainly rely on the ADF unit root inspection, the white noise test is mainly to prove that the original sequence is a nonwhite noise sequence, and the residual is a white noise sequence [10].


### 3.1.2. Basic Model of Neural Network

A large number of simple neurons form a relatively complex neural network, and each neuron has several inputs and a unique output. The neurons are connected by varying weights [11]. The mathematical model is shown in Figure 1.Figure 1
Mathematical model of the neuron.Figure 2
Interconnect network.Figure 3
Layered feedforward network.Figure 4
Single-layer fully connected feedback neural network.Artificial neural network is divided into the following types:(1)
Interconnect Artificial Neural Network. Interconnect artificial neural networks represent each neuron of the practice exercises. The information exchange program of each neuron is created according to the switching on weight. When in the network training process, the total neural network is constantly strengthened based on the connection weight of each neuron. After the specified time, the neural network can rise to relatively smooth conditions [12]. The main advantage of this type of neural network is that each neuron is associated, and the neural network can reach a better condition through multiple test training. An amazing drawback is that the network training speed is very slow, as shown in Figure 2, each neuron has a connection to each other, and there is a relationship between interworking information, and the links between them are very convenient.(2)
Hierarchical Feedforward Artificial Neural Network. The feedforward artificial neural network, you can know what it means through its name. Its neurons are listed in a layer, which is a one-way transmission network model with an input and output signal [13]. This model is a single-layer multidirectional arrangement, which is divided into three levels. The first is the writing layer, then the implicit layer, and the final is the output layer, but there is a hidden layer, which can be one layer or multiple layers [14]. Each neuron can receive the input information of the previous layer, and then send the output radar to the next layer, and so on, one by one. It is different from the interconnected neural network, and the feedforward neural network does not have the reverse transmission between the next layer and the upper layer. (shown in Figure 3). The feedforward neural network is divided into a list of arrangements. The transfer of the message is transmitted downward, and a layer of neurons will only have the output behavior on the next layer and can only receive writing from the previous layer network. In the research design, the use rate is the BP neural network and the RBF network.(3)
Feedback Forward Network. Feedback forward network means that there is a forward network, which is given to the input layer. As shown in Figure4, the neural unit of this network is in place of order, which is clearly based on the network template obtained from the feedforward artificial neural network. In the process of learning, the written layer neurons of this network continue to obtain output layer neurons, and some will output the feedback writing layer neurons of the network. This network outputs data by feedback [15]. It is different from the feedforward neural network to feedback all nodes in the front network. However, in the feedforward neural network, the node is divided into writing units and computing units. Before the neurons in the network obtain the writings of the upper network neurons, the calculation can be transmitted to the following neural unit.


## 3.1.1. Box–Jenkins Model Theory

In the existing urban management field, three research methods dominated in urban management prediction research: time sequence model, machine learning method, and depth learning method [8]. The essence of machine learning and depth learning is a method of manual intelligence. Machine learning and deep learning are both artificial intelligence methods in nature. Because of its complexity and the extent of the current research, this paper will come out separately.The Box–Jenkins model is a time sequence model that is used to predict the time series, often used in demand forecasting and planning [9].According to the average movement of the white noise in the Box–Jenkins model, it still maintains a steady state. It can be obtained from the following lag-weighted average formula:(1)YR=μ+μR+θ2μR−2+⋯⋯+θOμR−W,YR−μ=1+θ2K+θ2K2+⋯+θOKW,andIR=ϑKIR.When it returns the current value, the representative formula is(2)YR=δ+∅1YR−1+∅2YR−2+⋯⋯+∅OYR−W+IR,δ−IR=1−∅1K−∅2K2−⋯−∅OKW, andYR=∅KYR.When the differential is returned, the formula obtained is(3)∅KΔFYR=δ+ϑKIR.When the self-return is processed, the formula also changes:(4)YR=δ+Q1YR−1+Q2YR−2+⋯⋯+QOYR−O+IR+Q1IR−1+Q2IR−2+⋯⋯+QWIR−W,QKΔFYR=ϑKIR.The conversion process can be expressed as a formula:(5)ΔYR=YR−YR−1=YR−KYR=1−kYR,Δ2YR=YR−YR−1=1−KYR−KYR−1=1−k2YR,ΔFYR=1−KFYR.Squaring sequence can be obtained by the following formula:(6)ER=ΔFYR=1−KFYR.The model form is(7)Q1=WR−1WR−2+⋯⋯+WOWR−O+δ+θ1μ1+θ1μR−1+⋯⋯+θWμR−W,QKΔFYR=δ+ϑKIR.According to this, the next value can be predicted as follows:(8)GY=ω⋅Y+A,ωεR.If the prediction is widely used, the core formula is(9)YL=LL⋅XL+1−LL⋅YL−1.You can get an output child by activation of the following formula:(10)JE,NY=GEFY=G∑O=1LEOY0+A.The formula of the output signal is also revealed as follows:(11)JIK=GEIKYIK+NIK,sigmoidY=11+RY,tanhY=RY−R−YRY+R−Y,ReLUY=BY,Y≥0,0,Y≤0.Among them, the stationary tests mainly rely on the ADF unit root inspection, the white noise test is mainly to prove that the original sequence is a nonwhite noise sequence, and the residual is a white noise sequence [10].


## 3.1.2. Basic Model of Neural Network

A large number of simple neurons form a relatively complex neural network, and each neuron has several inputs and a unique output. The neurons are connected by varying weights [11]. The mathematical model is shown in Figure 1.Figure 1
Mathematical model of the neuron.Figure 2
Interconnect network.Figure 3
Layered feedforward network.Figure 4
Single-layer fully connected feedback neural network.Artificial neural network is divided into the following types:(1)
Interconnect Artificial Neural Network. Interconnect artificial neural networks represent each neuron of the practice exercises. The information exchange program of each neuron is created according to the switching on weight. When in the network training process, the total neural network is constantly strengthened based on the connection weight of each neuron. After the specified time, the neural network can rise to relatively smooth conditions [12]. The main advantage of this type of neural network is that each neuron is associated, and the neural network can reach a better condition through multiple test training. An amazing drawback is that the network training speed is very slow, as shown in Figure 2, each neuron has a connection to each other, and there is a relationship between interworking information, and the links between them are very convenient.(2)
Hierarchical Feedforward Artificial Neural Network. The feedforward artificial neural network, you can know what it means through its name. Its neurons are listed in a layer, which is a one-way transmission network model with an input and output signal [13]. This model is a single-layer multidirectional arrangement, which is divided into three levels. The first is the writing layer, then the implicit layer, and the final is the output layer, but there is a hidden layer, which can be one layer or multiple layers [14]. Each neuron can receive the input information of the previous layer, and then send the output radar to the next layer, and so on, one by one. It is different from the interconnected neural network, and the feedforward neural network does not have the reverse transmission between the next layer and the upper layer. (shown in Figure 3). The feedforward neural network is divided into a list of arrangements. The transfer of the message is transmitted downward, and a layer of neurons will only have the output behavior on the next layer and can only receive writing from the previous layer network. In the research design, the use rate is the BP neural network and the RBF network.(3)
Feedback Forward Network. Feedback forward network means that there is a forward network, which is given to the input layer. As shown in Figure4, the neural unit of this network is in place of order, which is clearly based on the network template obtained from the feedforward artificial neural network. In the process of learning, the written layer neurons of this network continue to obtain output layer neurons, and some will output the feedback writing layer neurons of the network. This network outputs data by feedback [15]. It is different from the feedforward neural network to feedback all nodes in the front network. However, in the feedforward neural network, the node is divided into writing units and computing units. Before the neurons in the network obtain the writings of the upper network neurons, the calculation can be transmitted to the following neural unit.


## 3.2. Sponge City

### 3.2.1. Sponge City Concept

Sponge city is a new concept of urban rain flood management. Specifically, the city has good adaptability when facing environmental changes and ecological natural disasters due to precipitation. It sufficiently absorbs savings and purifies rain water during precipitation and then releases the stored water for a certain need and integration. The internationally similar concept is called “low influence development rainwater system” [16].


### 3.2.2. Related Research Abroad

Foreign research and exploration of “sponge city” construction can be divided into two aspects: (1) one aspect is the research and practice of water system assessment and repair; (2) on the other hand, it is a summary and excavation refining of sponge city construction in different regions [17]. Representative theory and practice achievements: sources from the United States, the best management measures and green infrastructure concept, and low influence development mode from the EU’s water frame command framework, Germany’s natural open drainage system [18], British sustainable drainage system model, etc. In addition, there are urban design and development theories from New Zealand’s attention to low impact and Australia on water sensitivity urban design. The main developed countries or regions have made different measures and strategies in the management of water systems. It is not completely compiled, including the following aspects as shown in Table 1. As shown in Table 1, it can be seen that the measures and strategies of these countries basically have no difference, but it is applicable to their local countries.Table 1
Strategical pite on water system management.
CountriesName of the strategyContent of the strategyUnited StatesBest management practices, BMPs(A) Strengthening the control of storm runoff(B) General calculation methodsGermanyNatural drainage system, NDS(A) Reduce stormwater runoff(B) Connect stormwater corridorsUnited KingdomSustainable drainage system, SUDS(A) Attaches great importance to the control of stormwater runoff from the sources(B) Permeable pavementUnited StatesGreen infrastructure environment system(A) An ecological network structure consisting of nodes, linked corridors, and small open spaces in the network, which mixes natural and artificial facilitiesAustraliaWater sensitive urban design(A) Actively protect the existing natural features and protect the ecological environmentThe European UnionWater framework Directive, WFD(A) Restore the ecological health of the water body(B) Reduce or even eliminate the discharge of harmful substancesFigure 5
Structure of smart sponge city construction architecture.


## 3.2.1. Sponge City Concept

Sponge city is a new concept of urban rain flood management. Specifically, the city has good adaptability when facing environmental changes and ecological natural disasters due to precipitation. It sufficiently absorbs savings and purifies rain water during precipitation and then releases the stored water for a certain need and integration. The internationally similar concept is called “low influence development rainwater system” [16].


## 3.2.2. Related Research Abroad

Foreign research and exploration of “sponge city” construction can be divided into two aspects: (1) one aspect is the research and practice of water system assessment and repair; (2) on the other hand, it is a summary and excavation refining of sponge city construction in different regions [17]. Representative theory and practice achievements: sources from the United States, the best management measures and green infrastructure concept, and low influence development mode from the EU’s water frame command framework, Germany’s natural open drainage system [18], British sustainable drainage system model, etc. In addition, there are urban design and development theories from New Zealand’s attention to low impact and Australia on water sensitivity urban design. The main developed countries or regions have made different measures and strategies in the management of water systems. It is not completely compiled, including the following aspects as shown in Table 1. As shown in Table 1, it can be seen that the measures and strategies of these countries basically have no difference, but it is applicable to their local countries.Table 1
Strategical pite on water system management.
CountriesName of the strategyContent of the strategyUnited StatesBest management practices, BMPs(A) Strengthening the control of storm runoff(B) General calculation methodsGermanyNatural drainage system, NDS(A) Reduce stormwater runoff(B) Connect stormwater corridorsUnited KingdomSustainable drainage system, SUDS(A) Attaches great importance to the control of stormwater runoff from the sources(B) Permeable pavementUnited StatesGreen infrastructure environment system(A) An ecological network structure consisting of nodes, linked corridors, and small open spaces in the network, which mixes natural and artificial facilitiesAustraliaWater sensitive urban design(A) Actively protect the existing natural features and protect the ecological environmentThe European UnionWater framework Directive, WFD(A) Restore the ecological health of the water body(B) Reduce or even eliminate the discharge of harmful substancesFigure 5
Structure of smart sponge city construction architecture.


## 3.3. Intelligent Urban Management Mode

(1)
In the existing research, many scholars analyzed the factors of smart cities from information technology, people, environment, organization, management, institutions, policy, and economy [19]. In smart cities, many of the above elements have different nature and characteristics, play different functions, their interconnection, interaction, and build a smart city.In the formula calculating the OCHIIA coefficient between keywords, the coefficients in the matrix diagonal representation represent the degree of correlation with itself, and its value is 1. After calculating, the total word matrix can transition into a keyword of similar matrix and has a value between 0 and 1, as shown in Table2. In the table, the size of the value reflects the distance between the two keywords. The larger the value, the closer the distance between the two keywords, the better the similarity, and the smaller the value, the farther the distance between the two keywords, the worse the similarity [20].According to multivariate statistics, the network characteristics of subnets relate to high-frequency keywords in five dimensions. According to multistatistical analysis, the division of high-frequency keyword categories is divided into strategic dimension, social dimension, economic dimension, support dimension, and social networks related to spatial dimension [21]. The network characteristic indicators reflected are shown in Table 3. It can be seen that the keywords in the five dimensions have a high degree of closeness, strong concentration, proximity, and intermediary. It can be seen that there is a strong link between high-frequency keywords in different dimensions, and 57 high-frequency keywords have certain rationality in these five dimensions.(2)
In 2008, IBM proposed “smart city,” which is another leap-forward revolution in the history of urban development in the world, providing a new urban reform transformation development path [22]. Big data age, “smart city,” theory refers to the opening of various systems and services in the city by using various information technology or innovation concepts. It is deeply integrated with informationization, industrialization, and urbanization to enhance the efficiency of data resource utilization. It optimizes urban management and public services to create a happy living environment, realizes interactive management, improves urban management efficiency, and improves public quality of life. Under the background of the current big data era, “smart city” small theory provides theoretical support to solve the new urban problem and optimized urban resource allocation, which also collides with the new network identification and provides high-quality modern life for the public. Optimizing the social environment of urban life is a driving force for urban sustainable development. Compared with the traditional theory, the theory of “smart city” is the benign interaction between the government, society, and citizens. It has the advantages of high technology, efficient government, and a series of theoretical advantages - smart citizens.(3)
The smart sponge city is monitored by the important position of the sponge facilities, the discharge of the rainwater entrance, the construction project, and the drainage pipe network key node, and it used actual monitoring data to reach with the construction of sponge city. This provides the basis for the analysis of urban water resources, water environment, water safety monitoring, and effect assessment. To manage sponge cities in a more fine and dynamic manner, eliminate the information between departmental information that cannot be shared and communicated and avoid retries in construction management to improve sponge city construction and management efficiency [23]. Compared with traditional sponge cities, the wisdom sponge city has the following features, as shown in Table 4.(4)
According to the abovementioned, the structure of the wisdom sponge city construction architecture is drawn, as shown in Figure5. In the construction of a smart sponge project, establishing a real-time monitoring and management system and puting three aspects of intelligence management measures are necessary. Real-time monitoring and management system provides real-time monitoring, program simulation, and effectiveness assessment, and sponge projects provide the basic data required for real-time monitoring management systems. Real-time monitoring management systems provide technical support for smart management measures and also provide information feedback for the system during administering.Table 2
Keywords for smart urban research.
Information communication technologyInfrastructureIntelligent applicationCity innovationInformatizationInformation communication technology1.0000000.2571720.1878120.2659080.233285Infrastructure0.2571731.0000000.304290.2666980.188982Intelligent application0.1878120.304291.0000000.2472070.184017City innovation0.2659080.2666980.2472071.0000000.209359Informatization0.2332850.1889820.1840170.2093591.000000Table 3
Network characteristic indicators of different dimension-related subnets.
Strategic subnetSocial subnetThe subnetSupport the subnetSpace subnetNetwork size2095185Network correlation0.76320.75000.80000.53590.9000Network degree center potential76.31675.00090.00054.24890.000Network proximity center potential82.06681.33892.00069.79092.000Network center potential1.3163.5713.3332.8593.333Table 4
Differences of traditional sponge cities and wisdom sponge cities.
Compare the projectTraditional sponge citySmart sponge cityAcquisition of informationThe collection of information data is scattered and incomplete, and the real-time information is poorThe information data collection is comprehensive and systematic, and the information is in real-timeSharing of informationCommunication between departments is not smooth, and there is information isolationEstablish a unified data sharing platform to realize real-time information feedback among departmentsPlan formulationMost decisions are made based on experience, lacking scientific basis and technical supportCloud computing, GIS, hydrological model, and other comprehensive technology support and can simulate the operation of the scheme, making the decision more accurate and intelligentMaintenance of facilitiesIt is inefficient to rely on manpower inspectionThe information platform can monitor the running status of the equipment in real time, so that faults can be detected in time and corresponding measures can be taken


## 4. Fair City Management Experiment Based on Artificial Intelligence

### 4.1. LSTM Neural Network Model

The construction of the LSTM neural network model is designed to establish a sequence of rainfall times by learning and training for a historical rainfall in a certain manner to make up for the use of the P and C method. In this paper, for the extraction of rainfall field, 24 hours were selected as the duration of single rainfall by referring to relevant literature, which also conforms to the definition of long-term rainfall duration by most scholars.According to the LSTM neural network model completed by the abovementioned build, the data of the 4 rainstock stations can be entered into the model, and the 24-hour rain type of each rainstock station can be obtained, and one is analyzed as an example process and results. It is necessary to specifically explain that because the model predicts are large, the operation time is long, and the number of options is too much easier to appear. After repeated experiments, this paper sets the maximum number of selected generations to 50 times, which has improved the operational efficiency of the model, and it is also possible to reflect rainfall. It saves the result that has been saved 5 times after saving. The result is as shown in Figure6, which can be seen from the figure, 5 times of training results of the historical rainfall data of this site have shown a significant regularity. The entire rainfall process exhibits a rise-decline-rise-increase trend of repetition. The maximum rainfall appeared at the 18th hour, and 7 obvious peaks occurred in 7, 11, 14, 16, 18, 21, and 23 hours. By verifying twice, it is possible to determine that the resulting prediction has a better representation of the rainfall of the corresponding rainstock station.Figure 6
Five rainstock training results comparison. (a) First test and (b) second test.
(a)(b)Then, use the same method to train and output the other four rainstock data, as shown in Figure7. It can be seen that the peak value of 4 rainstock stations’ 24-hour rainfall is between 15 and 19 hours, and only multiple obvious peaks in the B station.Figure 7
Four rainstock training results. (a) A rainstock station, (b) B rainstorm station, (c) C rainstock station, and (d) D rainstorm station.
(a)(b)(c)(d)Through the training and prediction of the neural network model, the 24-hour time series characteristics of the rainfall of four rainstock stations can be obtained. The rainfall of the rainstock intensity formula is then calculated using a corresponding rainy feature to obtain a rainstock station under different reproduces. In 21 years, the 24-hour rainfall is distributed in accordance with the corresponding rainy feature, as shown in Figure8. It can be seen that the peak of 4 rainstocks 24-hour rainfall is more concentrated between 15 and 20 hours. There are only multiple obvious peaks in the B station and C station, and the other site rainfall is a trend that is rising and decline.Figure 8
Changes of the four rainstock stations under different references. (a) A rainstock station, (b) B rainstorm station, (c) C rainstock station, and (d) D rainstorm station.
(a)(b)(c)(d)


### 4.2. Wisdom Treatment Sponge City-Related Application Management Technology

The data acquisition module includes various types of sensors to collect data required for business functions such as internal warning and water quality assessment. For the construction goal of the sponge city, the actual construction conditions and environment of the construction site are analyzed, and the sensor is set in the position of data monitoring, for example, the rainwater discharge port, pipe network is easy to block and leak, and the indicators such as rainfall, water level, and water quality are required. Time is not less than 1 year, and the detection frequency is not less than 15 minutes/time.Water quality sensors use IMP integrated multiparameter water quality monitor to monitor sponge engineering-related water quality. The five model monitor conventional parameters such as water temperature, pH, dissolved oxygen, conductivity, and turbidity, and 2 extended ports are used to optionally select other sensors to perform data monitoring, such as chlorophyll and blue-green algae content, as shown in Table5.Table 5
Measurement parameters technical indicators for the integrated multiparameter water quality detector.
The measured parametersMeasurement rangeResolutionAccuracy24 h driftpH0∼14pH0.01pH±0.1pH±0.1pHDissolved oxygen (do)0∼20 mg/L0.01 mg/L±0.15 m/L±0.3 mg/LThe temperature−5°C∼50°C0.2°C±0.1°C±0.1°CElectrical conductivity0∼100 mS/cm0.1 mS/cm±1%±0.5%Chlorophyll0∼400μg/L0.1μg/L2%±2μg/LBlue-green algae0∼280000 cells/mL1 cells/mL2%±1%In terms of water quality, through the analysis of the rainfall data of a city, it was found that the city has three characteristics: first, the rainfall is less than evaporation, and the evaporation is about 2.5 times the rainfall; second, the annual decrease in precipitation is large, and the time and space distribution is extremely uneven; third, the rainfall distribution is uneven. The monthly precipitation and multiyear mean (2015 to 2019) are shown in Figure9.Figure 9
A city in the past five years of precipitation and multiyear mean. (a) Five-year precipitation and (b) multiyear mean.
(a)(b)


### 4.3. Risk Estimation of Internal Sponge City under Artificial Intelligence

The goals are internal risk assessment and its level division, and there is no unified standard. Therefore, to simplify the calculation, the complexity of the evaluation is reduced, and its operability is increased. By studying the actual situation and characteristics of a county, this paper learns the national flood risk assessment standard and uses hydraulic templates. Then, because of the need to consider water flooding depth and flow rate, the evaluation criteria are basically calculated in accordance with the hydraulic template assumptions and water flow rates to calculate the risk parameters. It is combined with the importance and sensitivity of urban areas. It is based on the coefficient of disaster and land loss in different regions. Finally, it will receive a risk planning, divided into low-risk zone, medium-risk zone, and high-risk zone. The internal risk assessment system is an evaluation system with a plurality of standards of population density, internal warning speed, down-faced land type, flood occurrence speed, and human vulnerability factors. This can create an internal risk comprehensive evaluation model. According to the hypothesis of the drainage model Infoworks ICM, in different design reference periods, in the 24-hour rainfall, it is used as the rainfall condition. The urban hydrological model and the two-dimensional surface model create a complete internal risk comprehensive evaluation model. RescDam study conducts a variation of DX (V + 0.5) coefficients in each age phase HR index according to the actual situation of the real person, which is shown in Figure 10, and got a range corresponding to the water depth, flow rate, and corresponding HR index, which is based on the value of the HR index. From Figure 10, you can clearly see the safety area corresponding to different ages. Data are very dangerous for those who have more than 2.0 or more.Figure 10
Distribution map of HR index in different age stages.


## 4.1. LSTM Neural Network Model

The construction of the LSTM neural network model is designed to establish a sequence of rainfall times by learning and training for a historical rainfall in a certain manner to make up for the use of the P and C method. In this paper, for the extraction of rainfall field, 24 hours were selected as the duration of single rainfall by referring to relevant literature, which also conforms to the definition of long-term rainfall duration by most scholars.According to the LSTM neural network model completed by the abovementioned build, the data of the 4 rainstock stations can be entered into the model, and the 24-hour rain type of each rainstock station can be obtained, and one is analyzed as an example process and results. It is necessary to specifically explain that because the model predicts are large, the operation time is long, and the number of options is too much easier to appear. After repeated experiments, this paper sets the maximum number of selected generations to 50 times, which has improved the operational efficiency of the model, and it is also possible to reflect rainfall. It saves the result that has been saved 5 times after saving. The result is as shown in Figure6, which can be seen from the figure, 5 times of training results of the historical rainfall data of this site have shown a significant regularity. The entire rainfall process exhibits a rise-decline-rise-increase trend of repetition. The maximum rainfall appeared at the 18th hour, and 7 obvious peaks occurred in 7, 11, 14, 16, 18, 21, and 23 hours. By verifying twice, it is possible to determine that the resulting prediction has a better representation of the rainfall of the corresponding rainstock station.Figure 6
Five rainstock training results comparison. (a) First test and (b) second test.
(a)(b)Then, use the same method to train and output the other four rainstock data, as shown in Figure7. It can be seen that the peak value of 4 rainstock stations’ 24-hour rainfall is between 15 and 19 hours, and only multiple obvious peaks in the B station.Figure 7
Four rainstock training results. (a) A rainstock station, (b) B rainstorm station, (c) C rainstock station, and (d) D rainstorm station.
(a)(b)(c)(d)Through the training and prediction of the neural network model, the 24-hour time series characteristics of the rainfall of four rainstock stations can be obtained. The rainfall of the rainstock intensity formula is then calculated using a corresponding rainy feature to obtain a rainstock station under different reproduces. In 21 years, the 24-hour rainfall is distributed in accordance with the corresponding rainy feature, as shown in Figure8. It can be seen that the peak of 4 rainstocks 24-hour rainfall is more concentrated between 15 and 20 hours. There are only multiple obvious peaks in the B station and C station, and the other site rainfall is a trend that is rising and decline.Figure 8
Changes of the four rainstock stations under different references. (a) A rainstock station, (b) B rainstorm station, (c) C rainstock station, and (d) D rainstorm station.
(a)(b)(c)(d)


## 4.2. Wisdom Treatment Sponge City-Related Application Management Technology

The data acquisition module includes various types of sensors to collect data required for business functions such as internal warning and water quality assessment. For the construction goal of the sponge city, the actual construction conditions and environment of the construction site are analyzed, and the sensor is set in the position of data monitoring, for example, the rainwater discharge port, pipe network is easy to block and leak, and the indicators such as rainfall, water level, and water quality are required. Time is not less than 1 year, and the detection frequency is not less than 15 minutes/time.Water quality sensors use IMP integrated multiparameter water quality monitor to monitor sponge engineering-related water quality. The five model monitor conventional parameters such as water temperature, pH, dissolved oxygen, conductivity, and turbidity, and 2 extended ports are used to optionally select other sensors to perform data monitoring, such as chlorophyll and blue-green algae content, as shown in Table5.Table 5
Measurement parameters technical indicators for the integrated multiparameter water quality detector.
The measured parametersMeasurement rangeResolutionAccuracy24 h driftpH0∼14pH0.01pH±0.1pH±0.1pHDissolved oxygen (do)0∼20 mg/L0.01 mg/L±0.15 m/L±0.3 mg/LThe temperature−5°C∼50°C0.2°C±0.1°C±0.1°CElectrical conductivity0∼100 mS/cm0.1 mS/cm±1%±0.5%Chlorophyll0∼400μg/L0.1μg/L2%±2μg/LBlue-green algae0∼280000 cells/mL1 cells/mL2%±1%In terms of water quality, through the analysis of the rainfall data of a city, it was found that the city has three characteristics: first, the rainfall is less than evaporation, and the evaporation is about 2.5 times the rainfall; second, the annual decrease in precipitation is large, and the time and space distribution is extremely uneven; third, the rainfall distribution is uneven. The monthly precipitation and multiyear mean (2015 to 2019) are shown in Figure9.Figure 9
A city in the past five years of precipitation and multiyear mean. (a) Five-year precipitation and (b) multiyear mean.
(a)(b)


## 4.3. Risk Estimation of Internal Sponge City under Artificial Intelligence

The goals are internal risk assessment and its level division, and there is no unified standard. Therefore, to simplify the calculation, the complexity of the evaluation is reduced, and its operability is increased. By studying the actual situation and characteristics of a county, this paper learns the national flood risk assessment standard and uses hydraulic templates. Then, because of the need to consider water flooding depth and flow rate, the evaluation criteria are basically calculated in accordance with the hydraulic template assumptions and water flow rates to calculate the risk parameters. It is combined with the importance and sensitivity of urban areas. It is based on the coefficient of disaster and land loss in different regions. Finally, it will receive a risk planning, divided into low-risk zone, medium-risk zone, and high-risk zone. The internal risk assessment system is an evaluation system with a plurality of standards of population density, internal warning speed, down-faced land type, flood occurrence speed, and human vulnerability factors. This can create an internal risk comprehensive evaluation model. According to the hypothesis of the drainage model Infoworks ICM, in different design reference periods, in the 24-hour rainfall, it is used as the rainfall condition. The urban hydrological model and the two-dimensional surface model create a complete internal risk comprehensive evaluation model. RescDam study conducts a variation of DX (V + 0.5) coefficients in each age phase HR index according to the actual situation of the real person, which is shown in Figure 10, and got a range corresponding to the water depth, flow rate, and corresponding HR index, which is based on the value of the HR index. From Figure 10, you can clearly see the safety area corresponding to different ages. Data are very dangerous for those who have more than 2.0 or more.Figure 10
Distribution map of HR index in different age stages.


## 5. Discussion

This paper is committed to studying the intelligent management model of sponge city under artificial intelligence and applies it to real-world management complex analysis and processing. This not only expands the application scope of artificial intelligence but also is a new attempt to study the complexity of the intelligent management mode of sponge city. By analyzing statistics and testing the rainfall data in a certain city, it digs up the artificial intelligence algorithm as an important tool to study the complexity of the system, which has certain potential in the research of the intelligent management mode of sponge city. In addition, on the basis of existing scholars’ in-depth research on intelligent sponge cities, the model is modified and combined with the theme of this paper to make the model applicable to the theme of this paper. For the study of AI algorithms, this paper starts from the various formula arguments of the most basic AI algorithms, analyzes the most appropriate formula of this paper, and improves them. It successfully combines the neuronal network and the intelligent sponge city in the artificial intelligence algorithm and carries out related experiments. In the empirical analysis stage, this paper uses the improved model to obtain multiple sets of data and analyzes the data of these two combinations in many aspects. The results show that the results meet the actual situation.Through the analysis of this case, it showed that the sponge city intelligent management mode based on the artificial intelligence algorithm is more effective for managing the city. It can analyze and study the urban drainage through artificial intelligence software and can also manage the types of projects in cities. At the same time, each project can be analyzed and summarized, so that it can greatly manage the time of the city and make multiproject combination decisions. In the specific sponge city management, different drainage combination strategies can be formulated according to different rainfall stations, and the application can be selected reasonably and flexibly. Replacing the AI algorithm into the sponge city intelligent management mode for computing and analysis, the most accurate and effective data can be obtained, so as to make the most effective decisions.This paper presents a case study at a local rainfall site. Firstly, through data statistics and qualitative analysis, the changes between the parameters of the rainfall sites were determined, and the model was used to study the parameters. It analyzed the data and yield changes over time points. Some proportions of the rainfall sites may fall first and then rise and then fall again, rather than showing a one-way growth or decrease.


## 6. Conclusions

Through the case studies, important conclusions have been made: in general, artificial intelligence algorithm applications are applicable in sponge city intelligent management model research, but this is not absolute. As time points increase, some rainfall sites may show up-down-rising-decline tendency. The rainfall site in some places in this case requires a set of models to make more detailed research and quantitative analysis to determine more efficient data. The project discussed in this paper is based on the management model of sponge city, and the selection of the project in this article is relatively limited. If the data are closer to real life, there is a wide range of regional research. Testing is tested, and the model also needs to be more comprehensive. However, things that can be appreciated are that we always believe that under the study of many scholars, and the research results will be more useful and more enjoyable, and will get closer to real life.



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*Source: 5113958-2022-06-27.xml*