# Analysis of an Economic Coupling Relationship Model of the Coastal Ecological Fragile Zone Based on a Machine Learning Model

Authors: Pei Wang; Xiaotao Sun
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

This study is based on a decision tree learning model and integrated system. In order to improve the classification accuracy of the base learner, the diversity of the results is analyzed. Accuracy and diversity are the focus of decision tree classifier research. The balance between decision tree classifiers is a key issue in creating a good ensemble classifier. By reducing the redundant features and training the model with the optimal feature subset, the accuracy of the model can be improved and the running time can be reduced. Normal distribution and generalized error distribution are better. The clustering algorithm based on theK-means partition is a number of compact and independent classification clusters based on the K-means partition algorithm. In order to objectively and accurately treat the machine learning model, an analysis model and economic coupling relationship model were used to evaluate the coastal ecological fragile zone. The error of the result of the classifier after training is smaller.


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## Body

## 1. Introduction

The machine learning model is an integrated learning system that combines a variety of learning methods [1–3]. It is not like the previous data mining decision tree algorithm. Machine learning models gradually appear as decision tree-based classifiers, and data mining-based classifiers are slowly playing an irreplaceable role in the public’s field of vision. At present, machine learning has the research direction of multiple attribute sets. There are not only machine learning algorithms with a single attribute set such as decision trees and logistic regression algorithms but also machine learning algorithms integrated with subsets of attribute sets, such as random forests, attribute set, subset, BDT and so on. The integrated learning algorithm is a method in which the integrated system, the Bagging algorithm, several independent self-service data sets, the data set prediction, the classification results, and the decision tree classifier jointly complete the model work. When the errors of the learners of each integrated system are independent of each other, as the number of independent self-service data sets that make up the integrated system increases, the prediction and classification results of the data sets caused by the integrated system will drop sharply. It even tends to 0 [4–6]. Therefore, the integrated system has a more accurate model work effect than a single Bagging algorithm learner. Since each independent self-service data set-based learner deals with the same work problem at the same time, it cannot achieve completely independent data set predictions. Generally speaking, the higher the accuracy of the ensemble learner, the diversity of the classification results of the base learner will decrease. Accuracy and diversity are the focus of decision tree classifiers. How to balance the relationship between the decision tree classifier and the integrated system and create a good integrated classifier is a key issue. The statistical model learners have a strong dependent relationship; the integrated system represented by Bagging has good applicability and high matching degree and can be used to explain a variety of economic conditions. The statistical model of the base learner is parallelized, so there is no strong dependence between the statistical models of the base learner. When measuring the marine economy and resource environment, the statistical model uses a sampling method with replacement and selects objective and practical classifiers to train and test the data set. Using the balanced decision tree classifier and ensemble system, the error in the classification test set of the marine industry-based classifier in the large coastal marine ecosystem is calculated. The relationship between marine economy and marine resources and environment in various regions should be classified correctly when the model weight is updated. Finally, the Bagging algorithm is used to experimentally analyze the updated weights, which will contribute to the next prediction of the fragile economic coupling relationship of coastal ecology. Using quantitative methods such as index analysis, comprehensive evaluation, principal component analysis, BP neural network, general equilibrium model, or system dynamics, let the base classifier focus on training and classification. The most important thing about the principle of the Bagging algorithm is that various variables should be absolutely independent, and the sampling method with replacement should be used. The independent variables and dependent variables are strictly defined [7–10]. The statistical model of the base learner randomly selects n data from the original data set D with the number of N to form a traditional statistical model self-service data set. Through repeated definitions and requirements, several independent self-service data sets with low accuracy can be obtained. China has vast sea areas and abundant marine resources. It is one of the world’s maritime powers. It faces a situation of weak domestic economic growth, shortage of various resources, and serious environmental damage. Using statistical models to predict the country’s marine resources to achieve the coordinated development of marine economy and marine resources and the environment is not accurate. The country’s emphasis on marine economy has reached an unprecedented height. Decision tree algorithm modeling can help human beings develop marine resources. Machine learning models derived from exchange, distribution, and exchange tend to be overfitting. Through neural network algorithm modeling, we must insist on land and sea coordination, accelerate the construction of a machine learning model for a maritime power, and determine that China will highly match the local economic conditions of the land through the development of the marine economy in the new century. A boosting algorithm expounds the concept of marine economy and regards the learning algorithm as part of marine economic equivalence. The status of the marine economy has been greatly improved [11]. The BP neural network algorithm has greatly improved the strategic position of the marine economy in the decision-making of regional national policies. Compared with the traditional neural network algorithm, the prediction accuracy of the BP neural network algorithm is greatly improved, and there is no overfitting problem with a high probability; the calculation amount is relatively small, and the requirements for independent variables are not as strict as the traditional model. The BP neural network algorithm incorporates the marine economy into an important part of the national economy for application [12–15]. But so far, only a small number of researchers have applied this algorithm to the early warning assessment of marine resource development from the perspective of the social production process. And the concept of marine economy was elaborated. Based on the analysis of the economic coupling relationship model of the coastal ecological fragile zone based on the machine learning model, it was creatively set out to believe that the marine economy is the sum of the production, exchange, distribution, and exchange of the development of marine resources by humans.


## 2. Machine Learning and Coastal Ecology

### 2.1. Random Forest

The research of marine economy is more of an adjunct to the advanced complex random forest developed by the decision tree. It mainly focuses on basic issues such as the definition, characteristics, and subject orientation when selecting the attributes of the marine economy. The theory of marine economy will automatically select the best attributes. The base classifier of the random forest algorithm is a decision tree. And it is worth noting that it is not only the decision tree algorithm used but also random attribute selection during model training, as shown in Figure1 [16–18].Figure 1
Flow chart of the random forest.


### 2.2. Extreme Random Tree

Extra tree does not use the best points when selecting branch points but uses the updated random weights to pick out a feature value to divide the tree classifier [19–20]. Let the base classifier focus on training classification, so the whole algorithm is very random, as shown in Figure 2.Figure 2
Extra tree flow chart.


### 2.3. Measurement of the Development of Marine Economy and Resources and Environment

When the marine economy and resource environment are measured, the tuple data is sampled with replacement, and the training set of measurement results is selected to a certain extent. Use the objective and practical base classifierq to train the test data set A, and calculate the error of the marine industry-based classifier in the large coastal marine ecosystem. Correspondingly, the relationship between marine economy and marine resources and environment in each region is classified correctly during model training, and the development theory and research methods of the marine eco-economic system should be expanded in the next weight update to reduce the weight of this tuple. Finally, using selective experimental analysis, nonlinear utility functions, establishing statistical models, and updated weights, exponential analysis, comprehensive evaluation, principal component analysis, BP neural network, and general equilibrium models or quantitative methods such as system dynamics allow the base classifier to focus on training classification.


## 2.1. Random Forest

The research of marine economy is more of an adjunct to the advanced complex random forest developed by the decision tree. It mainly focuses on basic issues such as the definition, characteristics, and subject orientation when selecting the attributes of the marine economy. The theory of marine economy will automatically select the best attributes. The base classifier of the random forest algorithm is a decision tree. And it is worth noting that it is not only the decision tree algorithm used but also random attribute selection during model training, as shown in Figure1 [16–18].Figure 1
Flow chart of the random forest.


## 2.2. Extreme Random Tree

Extra tree does not use the best points when selecting branch points but uses the updated random weights to pick out a feature value to divide the tree classifier [19–20]. Let the base classifier focus on training classification, so the whole algorithm is very random, as shown in Figure 2.Figure 2
Extra tree flow chart.


## 2.3. Measurement of the Development of Marine Economy and Resources and Environment

When the marine economy and resource environment are measured, the tuple data is sampled with replacement, and the training set of measurement results is selected to a certain extent. Use the objective and practical base classifierq to train the test data set A, and calculate the error of the marine industry-based classifier in the large coastal marine ecosystem. Correspondingly, the relationship between marine economy and marine resources and environment in each region is classified correctly during model training, and the development theory and research methods of the marine eco-economic system should be expanded in the next weight update to reduce the weight of this tuple. Finally, using selective experimental analysis, nonlinear utility functions, establishing statistical models, and updated weights, exponential analysis, comprehensive evaluation, principal component analysis, BP neural network, and general equilibrium models or quantitative methods such as system dynamics allow the base classifier to focus on training classification.


## 3. Application of the Machine Learning Model in the Economic Coupling Relationship of the Coastal Ecological Fragile Zone

(a)
AIC algorithm model [21–23](1)dSt=μsStdt+σStdWt+HtdNt,dSt=μsStdt+σSβ/2tdWt.Machine learning model:(2)W0=0;0≤β≤2,drt=θt−artdt+VtdWtv.Coastal ecological forecast:(3)dvt=αβ/α−Vtdt+σvVtdWtv,dSt=μStdt+VtStdWt1.Marine resources are produced, exchanged, distributed, and exchanged:(4)dVt=kθ−Vtdt+σVtdWt2,dWt1dWt2=ρdt,xt=εtσt.(b)
SC algorithm model [24–26](5)ht2=α0+∑i=1pαiXt−12+∑j=1qβiht−j2,∑i=1maxp,qαi+βj≤1.Economic coupling relationship model:(6)Dx=α01−∑i=1maxp,qαi+βj,EWtFs=Ws.Economic coupling relationship of the coastal ecological fragile zone:(7)EWt=EW0=0,X=eY,Y~Nμ,σ2,dXt=μtdt+σtdWt.(c)
HQ algorithm model [27–30](8)dStSt=μdt+σdBt,St=S0expμ−12σ2t+σBt.Prediction of the relationship between marine economy and resources:(9)dlogSt=1StStμdt+StσdBt−12σ2dt,dStSt=μdt+σdBt,df=∂f∂t+∂f∂tSμ+12σ2S2∂2f∂Sdt+∂f∂SSσdB.Result test training:(10)ΔS=μSΔt+σSΔB,Δf=∂f∂t+∂f∂tSμ+12σ2S2∂2f∂SΔt+∂f∂SSσΔB,ΔΠ=−Δf+∂f∂SΔS.The classifier is training(11)ΔΠ=−∂f∂t−12σ2S2∂2f∂SΔt.Compare the result error after training(12)ΔΠ=er△t−1Π=rΔtΠ,∂f∂t+rS∂f∂s+12σ2S2∂2f∂S=rf.


## 4. Simulation Experiment

### 4.1. Feature Screening

Feature selection is to use the method of changing the weight of the base classifier to associate the scope of the ocean economy with the economy directly related to the ocean after the learning algorithm of ocean economics is determined. The research on feature selection of marine economy can improve the accuracy of ensemble classifiers. When using the base classifier training samples, it mainly focuses on the research of basic issues such as the definition, characteristics, and subject positioning of marine economy. By relatively focusing on error-prone classifiers, by reducing redundant features, using the best feature subset to train the model can improve the accuracy of the model and reduce the running time, as shown in Table1.Table 1
Feature screening.
GARCH(1,1)AICSCHQNormal distribution5.1695.2125.186Student distribution4.5094.5674.532G-E distribution4.94.9434.917S-T fixed parameters4.9324.9754.949Generalized4.0744.1324.098From Table1, we can conclude that the GARCH model under the generalized error distribution with fixed parameters is the smallest under the three information criteria, and all the coefficients are significant. Normal distribution and generalized error distribution feature screening results are better. In the normal distribution, AIC=5.169, SC=5.212, and HQ=5.186; in the generalized error distribution, AIC=4.9, SC=4.943, and HQ=4.917, as shown in Figure 3.Figure 3
Feature screening.


### 4.2.K-Means Cluster Analysis

TheK-means algorithm is such a clustering algorithm based on centroid partition, using distance as the criterion, indicating the higher degree of similarity, as shown in Table 2 and Figure 4.Table 2
K-means cluster analysis.
ParameterCoefficientStandard deviationt-statisticsP valueμ-0.0370.0786-0.47110.0063ϕ10.05090.06730.75610.0049ϕ20.06270.07130.87850.0039ϕ30.03290.07030.46710.0064ϕ4-0.16640.0639-2.60580.0009α00.12850.11981.0730.0028β10.08370.09722.61290β20.09670.05381.79870.0072λ0.07210.25373.40340K-means clustering analysis selects feature parameters and reduces redundant features to obtain the optimal parametersμ=−0.037, ϕ1=0.0509, ϕ2=0.0627, ϕ3=0.0329, ϕ4=−0.1664, α0=0.1285, β1=0.0837, β2=0.0967, and λ=0.0721. The best statistics: μ=−0.4711, ϕ1=0.7561, ϕ2=0.8785, ϕ3=0.4671, ϕ4=−2.6058, α=1.073, β1=2.6129, β2=1.7987, and λ=3.4034.Figure 4
K-means cluster analysis.


### 4.3. Comparison of Inspection Methods

The test results of the residual sequence establishment with a fixed parameter column are shown in Table3. Both autocorrelation and heteroscedasticity are 5better resolved as shown in Figure 5.Table 3
Comparison of inspection methods.
Inspection typeNumber of lag periodsTest statisticsP valueARCH’s LM test10.010.9650.690.64101.210.28Ljung’s Box test10.050.3558.180.421015.480.51εLjung’s Box test10.030.9653.280.661013.370.2LM test, Ljung’s Box test, andεLjung’s Box.Figure 5
Comparison of inspection methods.The correlation test is based on an improved algorithm based on GBDT. Use the Taylor expansion to expand theLjungBox function to the first order. XGBoost uses Taylor’s formula to expand the objective function to a second-order column with fixed parameters. XGBoost stores a lot of objective function information, which can make the economic coupling relationship model of the coastal ecological fragile zone of the machine learning model have relatively less loss and lower variance, as shown in Figure 5.


### 4.4. Error Comparison

In order to objectively and accurately evaluate the economic coupling relationship model analysis model of the coastal ecological fragile zone of the machine learning model, compare the result error of the classifier after training and change the weight of the training tuple according to the classifier error. Each tuple of the machine learning model has a probability of being picked out, and the error training several times biases the model to being “error-prone.” At the beginning, the same “error-prone” weight of the biased model is set, so that the probability of training after any one of the biased models is selected as “error-prone” is set by the weight. Regarding the issue of iterative update weights of economic coupling relations in the coastal ecological fragile zone, the classification of the model is “error-prone” as much as possible, as shown in Table4.Table 4
Error comparison.
Volatility modelRMSEMAEMAPE600.19270.127113.4204900.19880.143912.67881800.23560.152411.5405GARCH0.20430.138610.5326As shown in Figure6, when the frequency is 60, MSE=0.1927, MAE=0.1271, and MAPE=13.4204; when the frequency is 90, RMSE=0.1988, MAE=0.1439, and MAPE=12.6788; and when the frequency is 180, RMSE=0.2356, MAE=0.1524, and MAPE=11.5405.Figure 6
Error comparison.


### 4.5. Model Comparison

The machine learning models of the coastal ecological fragile zone economic coupling relationship model analysis of different models are compared, including the AIC algorithm model, SC algorithm model, and Hd algorithm model; prediction results are shown in Table5. The SC algorithm model is the optimal model. In SC, AR4=3.509, AR3=3.533, AR2=3.531, AR1=3.523, ARMA1,1=3.532, A1,1=3.532, A1,1=3.532, A1,1=3.532, A1,1=3.532, A1,1=3.532, and A1,12,1)=3.539; in the AIC algorithm model, AR4=3.596, AR3=3.605, AR2=3.588, AR1=3.567, ARMA1,1=3.596, AR2=3.5881,2=3.609, and ARMA(2,1) =3.611; in the algorithm model, AR4=3.544, AR3=3.562, AR2=3.554, AR,ARARMA1,1=3.555, ARMA1,2=3.566, and ARMA2,1=3.568, as shown in Table 5 and Figure 7.Table 5
Model comparison.
ModelTypeSCAICHQ1AR(4)3.5093.5963.5442AR(3)3.5333.6053.5623AR(2)3.5313.5883.5544AR(1)3.5233.5673.5415ARMA(1,1)3.5323.5893.5556ARMA(1,2)3.5373.6093.5667ARMA(2,1)3.5393.6113.568Figure 7
Model comparison.


## 4.1. Feature Screening

Feature selection is to use the method of changing the weight of the base classifier to associate the scope of the ocean economy with the economy directly related to the ocean after the learning algorithm of ocean economics is determined. The research on feature selection of marine economy can improve the accuracy of ensemble classifiers. When using the base classifier training samples, it mainly focuses on the research of basic issues such as the definition, characteristics, and subject positioning of marine economy. By relatively focusing on error-prone classifiers, by reducing redundant features, using the best feature subset to train the model can improve the accuracy of the model and reduce the running time, as shown in Table1.Table 1
Feature screening.
GARCH(1,1)AICSCHQNormal distribution5.1695.2125.186Student distribution4.5094.5674.532G-E distribution4.94.9434.917S-T fixed parameters4.9324.9754.949Generalized4.0744.1324.098From Table1, we can conclude that the GARCH model under the generalized error distribution with fixed parameters is the smallest under the three information criteria, and all the coefficients are significant. Normal distribution and generalized error distribution feature screening results are better. In the normal distribution, AIC=5.169, SC=5.212, and HQ=5.186; in the generalized error distribution, AIC=4.9, SC=4.943, and HQ=4.917, as shown in Figure 3.Figure 3
Feature screening.


## 4.2.K-Means Cluster Analysis

TheK-means algorithm is such a clustering algorithm based on centroid partition, using distance as the criterion, indicating the higher degree of similarity, as shown in Table 2 and Figure 4.Table 2
K-means cluster analysis.
ParameterCoefficientStandard deviationt-statisticsP valueμ-0.0370.0786-0.47110.0063ϕ10.05090.06730.75610.0049ϕ20.06270.07130.87850.0039ϕ30.03290.07030.46710.0064ϕ4-0.16640.0639-2.60580.0009α00.12850.11981.0730.0028β10.08370.09722.61290β20.09670.05381.79870.0072λ0.07210.25373.40340K-means clustering analysis selects feature parameters and reduces redundant features to obtain the optimal parametersμ=−0.037, ϕ1=0.0509, ϕ2=0.0627, ϕ3=0.0329, ϕ4=−0.1664, α0=0.1285, β1=0.0837, β2=0.0967, and λ=0.0721. The best statistics: μ=−0.4711, ϕ1=0.7561, ϕ2=0.8785, ϕ3=0.4671, ϕ4=−2.6058, α=1.073, β1=2.6129, β2=1.7987, and λ=3.4034.Figure 4
K-means cluster analysis.


## 4.3. Comparison of Inspection Methods

The test results of the residual sequence establishment with a fixed parameter column are shown in Table3. Both autocorrelation and heteroscedasticity are 5better resolved as shown in Figure 5.Table 3
Comparison of inspection methods.
Inspection typeNumber of lag periodsTest statisticsP valueARCH’s LM test10.010.9650.690.64101.210.28Ljung’s Box test10.050.3558.180.421015.480.51εLjung’s Box test10.030.9653.280.661013.370.2LM test, Ljung’s Box test, andεLjung’s Box.Figure 5
Comparison of inspection methods.The correlation test is based on an improved algorithm based on GBDT. Use the Taylor expansion to expand theLjungBox function to the first order. XGBoost uses Taylor’s formula to expand the objective function to a second-order column with fixed parameters. XGBoost stores a lot of objective function information, which can make the economic coupling relationship model of the coastal ecological fragile zone of the machine learning model have relatively less loss and lower variance, as shown in Figure 5.


## 4.4. Error Comparison

In order to objectively and accurately evaluate the economic coupling relationship model analysis model of the coastal ecological fragile zone of the machine learning model, compare the result error of the classifier after training and change the weight of the training tuple according to the classifier error. Each tuple of the machine learning model has a probability of being picked out, and the error training several times biases the model to being “error-prone.” At the beginning, the same “error-prone” weight of the biased model is set, so that the probability of training after any one of the biased models is selected as “error-prone” is set by the weight. Regarding the issue of iterative update weights of economic coupling relations in the coastal ecological fragile zone, the classification of the model is “error-prone” as much as possible, as shown in Table4.Table 4
Error comparison.
Volatility modelRMSEMAEMAPE600.19270.127113.4204900.19880.143912.67881800.23560.152411.5405GARCH0.20430.138610.5326As shown in Figure6, when the frequency is 60, MSE=0.1927, MAE=0.1271, and MAPE=13.4204; when the frequency is 90, RMSE=0.1988, MAE=0.1439, and MAPE=12.6788; and when the frequency is 180, RMSE=0.2356, MAE=0.1524, and MAPE=11.5405.Figure 6
Error comparison.


## 4.5. Model Comparison

The machine learning models of the coastal ecological fragile zone economic coupling relationship model analysis of different models are compared, including the AIC algorithm model, SC algorithm model, and Hd algorithm model; prediction results are shown in Table5. The SC algorithm model is the optimal model. In SC, AR4=3.509, AR3=3.533, AR2=3.531, AR1=3.523, ARMA1,1=3.532, A1,1=3.532, A1,1=3.532, A1,1=3.532, A1,1=3.532, A1,1=3.532, and A1,12,1)=3.539; in the AIC algorithm model, AR4=3.596, AR3=3.605, AR2=3.588, AR1=3.567, ARMA1,1=3.596, AR2=3.5881,2=3.609, and ARMA(2,1) =3.611; in the algorithm model, AR4=3.544, AR3=3.562, AR2=3.554, AR,ARARMA1,1=3.555, ARMA1,2=3.566, and ARMA2,1=3.568, as shown in Table 5 and Figure 7.Table 5
Model comparison.
ModelTypeSCAICHQ1AR(4)3.5093.5963.5442AR(3)3.5333.6053.5623AR(2)3.5313.5883.5544AR(1)3.5233.5673.5415ARMA(1,1)3.5323.5893.5556ARMA(1,2)3.5373.6093.5667ARMA(2,1)3.5393.6113.568Figure 7
Model comparison.


## 5. Conclusion

Based on the machine learning model, this study analyzes and predicts the economic coupling relationship of the coastal ecologically fragile zone and builds a machine learning model for human production, exchange, distribution, exchange, and development of marine resources through the decision tree algorithm. By reducing the redundant features and using the optimal feature subset to train the model, the accuracy of the model can be improved and the running time can be reduced. Normal distribution and generalized error distribution are better. The clustering algorithm based on theK-means partition is a number of compact and independent classification clusters based on the K-means partition algorithm. By reducing the redundant features, the optimal number and the optimal statistics are obtained. In order to objectively and accurately treat the machine learning model, an analysis model and economic coupling relationship model were used to evaluate the coastal ecological fragile zone. The future work will focus on the optimization and analysis of the experimental application effect and performance of the model, focusing on the optimization of data sets.



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