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# Prediction of drinking water needs: the case of Bobo-Dioulasso

Pierre Konate, Abdoulaye Sere, Diarra Mamadou

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## To cite this version:

Pierre Konate, Abdoulaye Sere, Diarra Mamadou. Prediction of drinking water needs: the case of Bobo-Dioulasso. 2024. hal-04531007

HAL Id: hal-04531007

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Preprint submitted on 8 Apr 2024

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# Prediction of Water Consumption Using Machine
Learning Algorithms

Pierre Konate, Abdoulaye Sere, Diarra Mamadou

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## To cite this version:

Pierre Konate, Abdoulaye Sere, Diarra Mamadou. Prediction of Water Consumption Using Machine
Learning Algorithms. 2024. hal-04531007v3

HAL Id: hal-04531007

https://inria.hal.science/hal-04531007v3

Preprint submitted on 26 Aug 2025

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archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

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destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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Distributed under a Creative Commons Attribution 4.0 International License
# Prediction of drinking water needs : the case of
Bobo-Dioulasso

Pierre KONATE, Abdoulaye SERE, and mamadou DIARRA

Université Nazi BONI, Bobo Dioulasso 01 BP 1091, Burkina Faso

prrkonate15@gmail.com

abdoulaye.sere@recifaso.org

diarra.md21@gmail.com

**Abstract.** The objective of this study is to develop a solution to predict daily water consumption in order to optimize the water management system in the city of Bobo-Dioulasso. To achieve this, neural networks are used to predict consumption using historical consumption data and daily temperature in the city as the parameters. Four neural network algorithms were implemented for this study: Multi-Layer Perceptron (MLP), simple recurrent neural network, Long Short-Term Memory (LSTM) recurrent neural network, and Gated Recurrent Unit (GRU) recurrent neural network. The study focused on the eight distribution zones of the National Water and Sanitation Authority in the city. In view of the results, certain algorithms stood out from others in terms of prediction. The GRU network algorithm performed better on nearly half of the training data, followed by the MLP algorithm. The resulting models allow for the prediction of daily consumption on day J, given the consumption and temperature of day J-1. This work was carried out using tools such as the Jupyter Notebook environment and the Python language.

**Keywords:** Machine learning, data analysis, prediction

## 1 Introduction

The management of drinking water becomes increasingly complex over the years in view of the considerable increase in demand for domestic, industrial, uses. This strong demand is the result of the growth in the number of populations in urban and rural areas, resulting in limited access to water resources. This complexity leads us to have other management methods and techniques that can facilitate the management of the system.

In the face of this complexity, predicting water demand is an effective means of managing the system. For that, knowledge of information on water use (i.e. data) in the past is necessary for prediction. Indeed, from the modeling of coagulant dosage in treatment stations [3], to water monitoring through sensors [2], to solving water management problems [1] specifically in consumption, it is necessary to have a sufficient amount of historical data to facilitate prediction. The availability of prediction tools is also necessary in search of a solution.

Managing the distribution system is not an easy task because the methods used by managers of the National Water and Sanitation Office (ONEA) are traditional and do not meet the needs expressed following new developments. The classic method used is a statistical estimation (ONEA, 2019) to have the daily volume of water necessary for each distribution zone. These methods are therefore outdated and less reliable, and new methods must be proposed that can meet current needs. Among these new available methods, we have neural networks.

Compared to classical statistical methods, neural networks have a great capacity for solving prediction problems. Thanks to their complex structure composed of artificial neurons, neural networks have a great capacity for analyzing data in order to make predictions. They have a structure composed of several layers, which in turn are composed of a certain number of neurons. The neurons in each layer are connected in such a way as to work based on the information received. The more neurons increase, the more neural networks gain in solving complex problems. Each neuron performs a simple processing or calculation but this complex power results from their interaction [7].

Depending on the desired objective, there are different models of neural networks whose data processing requires the interaction of the different neurons that make up the model. The models
# Prediction of Water Consumption Using Machine Learning Algorithms*

Pierre KONATE¹, Abdoulaye SERE², Mamadou DIARRA³

¹,²,³Université Nazi BONI, Bobo Dioulasso 01 BP 1091, Burkina Faso

*E-mail: prrkonate15@gmail.com

**Abstract**

The objective of this study is to propose solutions for predicting drinking water consumption over a period of 5 days. To achieve this, machine learning methods such as neural network algorithms are used to confront different results in order to obtain a model that optimizes daily water consumption the most. The algorithms used include Multi-Layer Perceptron (MLP), Simple Recurrent Neural Network, Long Short-Term Memory (LSTM) recurrent neural network, and Gated Recurrent Unit (GRU) recurrent neural network. Since the data comes from multiple sources, the algorithms are used to train models on each dataset. At the end of this study, the model that optimizes the best is selected for each dataset after comparing the results of different algorithms. Based on the various results obtained, the performance of the algorithms varies depending on the periods and the data. For example, the best performance in predicting consumption in the KUA area on the J+5 day period is attributed to the MLP algorithm based on mean absolute errors (MAE).

**Keywords**

Machine learning, data analysis, prediction

## I INTRODUCTION

Agencies responsible for managing water consumption are increasingly encountering challenges related to water distribution. One of the primary causes of these challenges lies in the high demand stemming from the significant growth of the population, both in urban and rural areas. Given the increasing complexity of water management, new and innovative solutions must be considered to effectively address these issues. In this context, it is essential to explore the opportunities provided by emerging technologies in order to identify a solution capable of efficiently responding to the current situation. A particularly relevant approach involves leveraging modern technologies to forecast water consumption volumes. To enable such forecasting, it is necessary to rely on historical consumption data. Indeed, whether it concerns coagulant dosage modeling in treatment plants [7], water monitoring through sensors [5], or addressing broader water management challenges [10]—particularly those related to consumption—having a sufficient volume of historical data is crucial to facilitate accurate predictions. By implementing such a solution, the National Office for Water and Sanitation (ONEA) can address these challenges while also modernizing its operational practices. Current approaches, which are based on conventional methods (such as the statistical estimation method employed by ONEA in 2019), exhibit significant limitations in terms of efficiency and reliability. There is a pressing need

⁰Note: This is an improved version of a previous article published in June 2024 at https://eudl.eu/doi/10.4108/eai.18-12-2023.2348136 with reference [33].
used in our study are those whose processing is based on quantitative data. This choice of model is
therefore due to the fact that the data collected from ONEA are quantitative data.

The remainder of this document will include a description of the methods used, data collection, data processing and presentation of results followed by a discussion.

## 2 Method Description

The neural network models used in the search of solutions are four (04) in number, including multi-layer perceptrons, simple recurrent networks, LSTM networks, and GRU networks.

### 2.1 The Multi-Layer Perceptron (MLP)

The MLP is a neural network with layers whose activation functions of the same layer are identical [7]. The MLP network consists of three layers; the input layer, the hidden layer, and the output layer (see figure 1). Each layer has an activation function, which helps process the received information. This information processing is carried out using the backpropagation method ([4]), which allows for the processing of information from the input to the output layer, as well as from the output to the input layer.

<figure><img src="image_4.png" /><figcaption>Fig. 1. The architecture of a multi-layer perceptron.</figcaption></figure>
to adopt modern technological methods to improve the management of water distribution systems. The methods proposed in this study are based on neural networks. The performance of traditional methods is negligible when compared to that of neural networks. These networks offer substantial predictive capabilities thanks to their complex structure, which enables them to process and model highly nonlinear and intricate problems. Their architecture consists of multiple successive layers, granting them strong analytical power in data processing. Each layer is composed of interconnected neurons that handle incoming information. The complexity of a neural network is directly related to the number of neurons it contains: the greater the number of neurons, the more complex and powerful the network becomes, with its strength lying in the interactions between neurons [20]. Within the scope of neural networks, various predictive methods exist. Among these, this study focuses on those based on quantitative data, a choice that is justified by the nature of the data collected from ONEA. This paper is structured into four main sections: (1) a detailed description of the methods employed, (2) an explanation of the data collection process, (3) data preprocessing and preparation, and finally, (4) a presentation and discussion of the results, followed by a conclusion.

## II METHOD DESCRIPTION

In the scope of our study, given that the available data are quantitative, the choice was made to utilize four (4) methods, namely Multi-Layer Perceptron (MLP), simple recurrent neural network, Long Short-Term Memory (LSTM) recurrent neural network, and Gated Recurrent Unit (GRU) recurrent neural network.

### 2.1 The Multi-Layer Perceptron (MLP)

The MLP (Multilayer Perceptron) is a neural network comprised of layers, wherein the activation functions within each layer are identical [20]. The MLP network typically consists of three layers: the input layer, the hidden layer, and the output layer (refer to figure 1). Each layer employs an activation function to process the incoming information. This information processing is facilitated by the backpropagation method ([17]), enabling the flow of information from the input layer to the output layer and vice versa.

### 2.2 Simple recurrent neural networks

Recurrent Neural Networks (RNNs) are a class of neural networks characterized by cyclic connections, making them particularly well-suited for processing sequential data such as time series, video streams, or audio signals.

Simple RNNs represent the most basic form of recurrent networks. Similar to Multi-Layer Perceptrons (MLPs), they rely on the backpropagation algorithm [22], specifically a variant known as Backpropagation Through Time (BPTT), to learn temporal patterns from sequential data. Their architecture resembles that of MLPs, with one crucial distinction: they incorporate recurrent connections that allow information to persist across time steps. Fully Recurrent Neural Networks (FRNNs) go a step further by introducing complete connectivity between all neuron outputs and all inputs. This structure represents the most generalized form of neural network topology, capable of simulating any other configuration by appropriately adjusting the connection weights (for instance, by setting certain weights to zero to simulate the absence of connections).

It is important to note that, in visual representations, these networks are often “unrolled” in time to resemble layered structures. This can be misleading: what appears to be multiple dis-
## 2.2 Simple recurrent neural networks

Recurrent neural networks are a type of cyclic neural network that perform exceptionally well in processing sequential data (such as time series data, video and audio streams).

Simple recurrent neural networks are a basic version of recurrent networks. Similar to multi-layer perceptrons, recurrent network algorithms use the backpropagation algorithm ([5]) to process sequential data. Their operation is very similar to that of MLP algorithms by associating the time steps all running in a recurrent way.

Fully recurrent neural networks (FRNNs) establish connections between all neurons' outputs and inputs. This topology represents the most general neural network structure, as it can encompass all other network configurations by selectively nullifying connection weights to simulate the absence of specific inter-neuron connections. The accompanying illustration on the right may lead to potential misinterpretations, as practical neural network structures are often visually organized into 'layers,' giving the impression of distinct, separate layers. However, what may initially appear as layers actually signifies different time steps within the same fully recurrent neural network. The illustration's leftmost component reveals the recurrent connections, denoted as the 'v' labeled arc, which are temporally 'unfolded' to create the layer-like appearance.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Schema of a RNN. [8]</figcaption></figure>
tinct layers is, in fact, the same network repeated across time steps. In illustrations (Figure 2), the recurrent connections—typically represented by arcs labeled ‘v’—are unfolded over time, creating the illusion of several layers, when in reality it is the same set of neurons processing input data at different temporal intervals.

## 2.3 Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) is a type of neural network known for its high complexity, characterized by the integration of two forms of memory: long-term memory and short-term memory. Its architecture consists of four interacting components that enable the selective addition and removal of information. These components, referred to as gates, correspond to the functional layers of the network:

* the Input Gate,

* the Forget Gate,

* the Memory Cell,

* and the Output Gate.

The core strength of LSTM networks lies in their ability to capture and manage long-term dependencies, which makes them particularly suitable for processing long or complex sequences.

The equations of the LSTM model are defined by the set of the following equations ([25]):

$$F_t = \sigma (b^F + x_t U^F + h_{t-1} W^F)$$

$$I_t = \sigma (b^I + x_t U^I + h_{t-1} W^I)$$

$$O_t = \sigma (b^O + x_t U^O + h_{t-1} W^O)$$

$$F_t = \sigma (b^F + x_t U^F + h_{t-1} W^F)$$

$$c_t = F_t c_{t-1} + I_t \tanh (b + x_t U + h_{t-1} W)$$

$$ht = \tanh (c_t) O_t$$

$$\hat{y} = g (b^{\hat{y}} + h_t W^{\hat{y}})$$

Where F corresponds to the "forget gate," the gate responsible for updating the memory cell c. I and O correspond to the "input" and "output" gates, allowing the input and output of information in the LSTM based on the input data x and ht-1, which represents the hidden state, the output vector of the LSTM at time step t-1. The vectors U and W correspond to the weights associated with the input data and recurrent weights associated with the hidden layer at time step t-1. g represents an activation function used to obtain ŷt, the LSTM's prediction or classification at time step t.

## 2.4 Gated Recurrent Unit (GRU)

The Gated Recurrent Unit (GRU) model is a simplified version of the LSTM network. Like LSTM, it is capable of capturing long-term temporal dependencies, but its architecture is based on only two gates:

* the Reset Gate,

* and the Update Gate.

A key advantage of the GRU lies in its reduced training time, making it computationally more efficient. Figure 4 illustrates the structure of a GRU network.
## 2.3 Long Short-Term Memory (LSTM)

The LSTM is a highly complex type of neural network with two memories, one long-term and one short-term. This network structure consists of four interacting elements that allow for the addition and removal of information. These elements are gates that represent the network's layers, including the Input Gate, Forget Gate, Memory, and Output Gate. The key feature of LSTMs is their ability to manage long-term dependencies.

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 3. Schema of a LSTM. [8]</figcaption></figure>

The equations of the LSTM model are defined by the set of the following equations ([6]):

$$F_t = \sigma (b^F + x_t U^F + h_{t-1} W^F)$$

$$I_t = \sigma (b^I + x_t U^I + h_{t-1} W^I)$$

$$O_t = \sigma (b^O + x_t U^O + h_{t-1} W^O)$$

$$F_t = \sigma (b^F + x_t U^F + h_{t-1} W^F)$$

$$c_t = F_t c_{t-1} + I_t \tanh (b + x_t U + h_{t-1} W)$$

$$ht = tanh (c_t) O_t$$

$$\hat{y} = g (b\hat{y} + h_t W\hat{y})$$

Where F corresponds to the "forget gate," the gate responsible for updating the memory cell c. I and O correspond to the "input" and "output" gates, allowing the input and output of information in the LSTM based on the input data x and ht-1, which represents the hidden state, the output vector of the LSTM at time step t-1. The vectors U and W correspond to the weights associated with the input data and recurrent weights associated with the hidden layer at time step t-1. g represents an activation function used to obtain ŷt, the LSTM's prediction or classification at time step t.
### III DATA COLLECTION

The data used in this study originate from daily potable water consumption, collected from the National Office for Water and Sanitation (ONEA) in Bobo-Dioulasso. The city of Bobo-Dioulasso and its surrounding areas are supplied with drinking water by ONEA. To improve water management, the agency has divided the city and its outskirts into eight (8) water distribution zones. The data related to the volume of water distributed in each of these zones constitute the dataset for this study, covering the period from November 2018 to November 2021. In addition to consumption data, temperature data were also of particular interest in this study. To obtain them, temperature records for the Bobo-Dioulasso region, covering the same period (November 2018 to November 2021), were collected from the official platform of the national meteorological agency of Burkina Faso.

The data provided to us has undergone a cleaning process to ensure the acquisition of reliable and consistent data. Following this cleaning process, nine variables were selected for further analysis in the study, including data on:

* The distribution of KUA

* The distribution of LOW SARFALAO,

* The distribution of HIGH SARFALAO,

* The distribution of BAMA,

* The distribution of BELLE VILLE,

* The distribution of LAFIABOUGOU,

* The distribution of BOLO VERS VILLE,

* The distribution of BELLE VILLE PIQUAGE,

* Maximum temperature.

After statistical analysis, we obtain the results (summaries) in the table 1.
## 2.4 Gated Recurrent Unit (GRU)

The GRU model is a simplified version of the LSTM network model. It also has the capability to manage long-term temporal dependencies, but its structure includes two gates; the Gate reset and the Gate update. Its uniqueness lies in its ability to reduce the learning time of a model, making it faster in computations. The figure 2.4 shows the diagram of a GRU.

<figure><img src="image_8.png" /><figcaption>Fig. 4. Schema of a GRU. [6]</figcaption></figure>
## IV DATA PROCESSING

Data processing involves model training, encompassing data cleaning and normalization to ensure high-quality inputs. Data cleaning—which accounts for more than half of the total processing time—ensures that the dataset is reliable, consistent, and meaningful. Normalization, in turn, standardizes the data onto a common scale while preserving the original range and informational content. In this study, Z-score normalization is applied, which involves subtracting the mean from each raw value and dividing the result by the standard deviation (see Equation 1).

Before normalization, the dataset is partitioned into training and testing subsets. Although there is no universally fixed ratio for this split, previous studies commonly recommend proportions such as 75% training and 25% testing, or 80% and 20%, respectively [10]. In the present work, data from each zone and corresponding temperature records underwent the full preprocessing pipeline: cleaning, normalization, and partitioning according to the selected ratio.

$$X_{norm} = \frac{X_i - \bar{X}}{\sigma_x} \qquad (1)$$

where:

* $X_{norm}$ : normalized value

* $X_i$ : gross value

* $\bar{X}$ : average of the data

* $\sigma_x$ : standard deviation

## V RESULTS

The data obtained from each zone after processing was used to train models over multiple periods using the selected algorithms. The results obtained after training on the different zones are presented in the following sections.

### 5.1 Prediction results of Belle ville

The water consumption data from the Belle Ville zone were preprocessed to ensure data reliability, alongside daily temperature records from the city of Bobo. The resulting dataset was then used to train the algorithms and develop predictive models for days J+2, J+3, J+4, and J+5. Model evaluation was conducted based on the corresponding Mean Absolute Error (MAE) values, which are reported for each forecast horizon in Table 2.

### 5.2 Prediction results of Lafiabougou

Regarding the Lafiabougou zone, the models obtained based on the water consumption data of the area and the temperature were evaluated using the error values recorded in Table 3.

### 5.3 Prediction results of Bolo Vers Ville

The error values obtained from the training models of the Bolo Vers Ville zone are thus presented in Table 4.
## 3 Data collection

The data used are daily water consumption of data that were collected from the National Water and Sanitation Office (ONEA) of Bobo-Dioulasso. In fact, the Bobo-Dioulasso area and its surroundings are served with drinking water by ONEA. For effective water management, the Office has divided the city and its environs into eight (8) water distribution zones. The data on the amount of water distributed for each zone constitute the study data for the period from November 2018 to November 2021. In addition to consumption data, temperature data were of great interest in this study. To do this, temperature data for the Bobo-Dioulasso area for the same period from November 2018 to November 2021 were collected from the official platform of the agency responsible for meteorology in Burkina Faso

## 4 Data Analysis Exploration

The data made available to us has undergone a cleaning process in order to obtain reliable and consistent data. At the end of this cleaning, nine variables were retained for the remainder of the study, namely data on:

* The distribution of KUA

* The distribution of LOW SARFALAO,

* The distribution of HIGH SARFALAO,

* The distribution of BAMA,

* The distribution of BELLE VILLE,

* The distribution of LAFIABOUGOU,

* The distribution of BOLO VERS VILLE,

* The distribution of BELLE VILLE QUILTING,

* Maximum temperature.

After statistical analysis, we obtain the results (summaries) in the table 1:

<table><caption>**Table 1.** Statistics on the parameters used.</caption><thead><tr><th></th><th>Mean</th><th>Standard deviation</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>KUA</td><td>8044.157</td><td>1320.111</td><td>360.0</td><td>10000.0</td></tr><tr><td>LOW SARFALAO</td><td>5326.553</td><td>744.506</td><td>528.0</td><td>6000.0</td></tr><tr><td>HIGH SARFALAO</td><td>1804.199</td><td>553.291</td><td>8.0</td><td>2500.0</td></tr><tr><td>BAMA</td><td>18543.119</td><td>2903.644</td><td>225.0</td><td>21000.0</td></tr><tr><td>BELLE VILLE</td><td>1730.155</td><td>899.705</td><td>70.0</td><td>3000.0</td></tr><tr><td>LAFIABOUGOU</td><td>6313.376</td><td>1195.902</td><td>2.0</td><td>7000.0</td></tr><tr><td>BOLO VERS VILLE</td><td>5065.460</td><td>1059.768</td><td>195.0</td><td>6000.0</td></tr><tr><td>BELLE VILLE QUILTING</td><td>2445.469</td><td>230.131</td><td>135.0</td><td>3000.0</td></tr></tbody></table>
## 5.4 Prediction results of Bama

After the training and evaluation phase of the models using data from the Bama zone, their errors, on which the models were evaluated over multiple periods, are listed in Table 5.

## 5.5 Prediction results of Kua

The data collected in Table 6 represents the outcomes of various training models using consumption data from the Kua zone.

## 5.6 Prediction results of High Sarfalaho

The mean absolute errors in Table 7 are the evaluation results of the various models obtained with the data from the High Sarfalaho zone.

## 5.7 Prediction results of Low Sarfalaho

For each period, models were established for water consumption prediction. The performance of these models was measured based on mean absolute errors. Table 8 presents the different errors for the periods.

## 5.8 Prediction results of Belle Ville Piquage

The consumption data from the Belle Ville Piquage zone allowed the development of various models across the four forecast horizons. Model performance was evaluated based on the Mean Absolute Error (MAE), with the corresponding error values presented in Table 9.
# 5 Data processing

Data processing is a process of model learning that involves cleaning and normalization to make data of good quality. Cleaning is the stage of the process that takes up more than half of the data processing time. It makes the data reliable, consistent, and valuable. Normalization, on the other hand, allows data to be placed on the same scale without the differences in value ranges being distorted and without any loss of information. In this case the Z-score normalization is the normalization method used, it consists of subtracting the average of the data from the raw value and dividing it by the standard deviation (equation 1).

Before the normalization phase, data is separated into training data and test data. However, the proportion of data does not have a defined rule, as researchers have suggested for similar studies ratios of 75% and 25% or 80% and 20%, respectively, for learning and testing [1]. In this study, data from each zone and temperature underwent the processing process, from cleaning to normalization, passing through the separation of data into proportions.

$$X_{norm} = \frac{X_i - \bar{X}}{\sigma_x} \qquad (1)$$

where:

* $X_{norm}$ : normalized value

* $X_i$ : gross value

* $\bar{X}$ : average of the data

* $\sigma_x$ : standard deviation

# 6 Results and discussion

Upon completion of data processing, we obtain data ready for model training. Thus, models were trained on the various data obtained using the four neural network algorithms mentioned earlier. The data from each zone was passed through each of the four algorithms, and models were obtained after these trainings.

## 6.1 Prediction results of Belle ville

The data on consumption in the Belle ville area and the temperature in the locality are the two input parameters for each of the algorithms. Of all the models obtained, the GRU algorithm achieves better results and Figure 5 shows the variations in the predicted data and those of the observations.

## 6.2 Prediction results of Lafiabougou

The forecast of consumption on day J required the use of the temperature and consumption of the previous day (J-1) as input parameters for each of the models obtained after learning the different algorithms.

The figure 6 illustrates the variation of observed values and predicted values of the same model
# VI TABLES AND FIGURES

## 6.1 Tables

<table><caption>Table 1: Statistics on the parameters used.</caption><thead><tr><th></th><th>Mean</th><th>Standard deviation</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>KUA</td><td>8044.157</td><td>1320.111</td><td>360.0</td><td>10000.0</td></tr><tr><td>LOW SARFALAO</td><td>5326.553</td><td>744.506</td><td>528.0</td><td>6000.0</td></tr><tr><td>HIGH SARFALAO</td><td>1804.199</td><td>553.291</td><td>8.0</td><td>2500.0</td></tr><tr><td>BAMA</td><td>18543.119</td><td>2903.644</td><td>225.0</td><td>21000.0</td></tr><tr><td>BELLE VILLE</td><td>1730.155</td><td>899.705</td><td>70.0</td><td>3000.0</td></tr><tr><td>LAFIABOUGOU</td><td>6313.376</td><td>1195.902</td><td>2.0</td><td>7000.0</td></tr><tr><td>BOLO VERS VILLE</td><td>5065.460</td><td>1059.768</td><td>195.0</td><td>6000.0</td></tr><tr><td>BELLE VILLE QUILTING</td><td>2445.469</td><td>230.131</td><td>135.0</td><td>3000.0</td></tr></tbody></table>

<table><caption>Table 2: Table summarizing the errors of the different algorithms after evaluation of the Belle Ville zone</caption><thead><tr><th></th><th>MLP</th><th>RNN Simple</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><td>J+2</td><td>0,2255</td><td>0,0793</td><td>0,2289</td><td>0,0763</td></tr><tr><td>J+3</td><td>0,2225</td><td>0,0908</td><td>0,2287</td><td>0,0863</td></tr><tr><td>J+4</td><td>0,2242</td><td>0,0921</td><td>0,2274</td><td>0,0939</td></tr><tr><td>J+5</td><td>0,225</td><td>0,0974</td><td>0,2305</td><td>0,0927</td></tr></tbody></table>

<table><caption>Table 3: Table summarizing the errors of the different algorithms after evaluation of the Lafiabougou zone.</caption><thead><tr><th></th><th>MLP</th><th>RNN Simple</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><td>J+2</td><td>0,0738</td><td>0,0281</td><td>0,0934</td><td>0,0311</td></tr><tr><td>J+3</td><td>0,0813</td><td>0,0309</td><td>0,0888</td><td>0,0339</td></tr><tr><td>J+4</td><td>0,083</td><td>0,0351</td><td>0,0791</td><td>0,0383</td></tr><tr><td>J+5</td><td>0,072</td><td>0,034</td><td>0,097</td><td>0,0296</td></tr></tbody></table>

<table><caption>Table 4: Table summarizing the errors of the different algorithms after evaluation of the Bolo Vers Ville zone.</caption><thead><tr><th></th><th>MLP</th><th>RNN Simple</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><td>J+2</td><td>0,0946</td><td>0,0971</td><td>0,0918</td><td>0,0975</td></tr><tr><td>J+3</td><td>0,0937</td><td>0,1007</td><td>0,0908</td><td>0,1013</td></tr><tr><td>J+4</td><td>0,0888</td><td>0,0935</td><td>0,0887</td><td>0,0929</td></tr><tr><td>J+5</td><td>0,0895</td><td>0,0956</td><td>0,0887</td><td>0,0947</td></tr></tbody></table>

<table><caption>Table 5: Table summarizing the errors of the different algorithms after evaluation of the Bama zone.</caption><thead><tr><th></th><th>MLP</th><th>RNN Simple</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><td>J+2</td><td>0,0741</td><td>0,0839</td><td>0,0828</td><td>0,081</td></tr><tr><td>J+3</td><td>0,0766</td><td>0,0805</td><td>0,0797</td><td>0,0859</td></tr><tr><td>J+4</td><td>0,072</td><td>0,0821</td><td>0,0787</td><td>0,0782</td></tr><tr><td>J+5</td><td>0,0767</td><td>0,083</td><td>0,0832</td><td>0,0822</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>Fig. 5. Variation of observations and predicted values of Belle ville</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 6. Variation of observations and predicted values of LAFIABOUGOU</figcaption></figure>

## 6.3 Prediction results of bolo vers ville

Among all the algorithms employed for modeling on Bolo vers ville data, the best model is attained using the GRU algorithm with input parameters consisting of consumption data from the area and temperature data. Figure 7 displays the variation between the predicted and observed data.

The data used being the temperature and daily consumption in the HIGH SARFALAO area, the forecast model obtained takes as input parameters the temperature and consumption of the previous day (J-1) and the output of the model is the consumption of the day J.

The variations of the actual data and the predicted data are also represented in the figure 8 :
<table><caption>Table 6: Table summarizing the errors of the different algorithms after evaluation of the Kua zone.</caption><thead><tr><th></th><th>MLP</th><th>RNN Simple</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><th>J+2</th><td>0,0845</td><td>0,0834</td><td>0,0864</td><td>0,0833</td></tr><tr><th>J+3</th><td>0,0842</td><td>0,0854</td><td>0,0865</td><td>0,0865</td></tr><tr><th>J+4</th><td>0,0845</td><td>0,0836</td><td>0,0847</td><td>0,0835</td></tr><tr><th>J+5</th><td>0,0842</td><td>0,0868</td><td>0,0874</td><td>0,0863</td></tr></tbody></table>

<table><caption>Table 7: Table summarizing the errors of the different algorithms after evaluation of the High Sarfalaho zone.</caption><thead><tr><th></th><th>MLP</th><th>RNN Simple</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><th>J+2</th><td>0,1581</td><td>0,1268</td><td>0,1629</td><td>0,1296</td></tr><tr><th>J+3</th><td>0,1561</td><td>0,1466</td><td>0,1582</td><td>0,1444</td></tr><tr><th>J+4</th><td>0,1579</td><td>0,1265</td><td>0,1565</td><td>0,1248</td></tr><tr><th>J+5</th><td>0,1547</td><td>0,1414</td><td>0,1596</td><td>0,1403</td></tr></tbody></table>

<table><caption>Table 8: Table summarizing the errors of the different algorithms after evaluation of the Low Sarfalaho zone.</caption><thead><tr><th></th><th>MLP</th><th>RNN Simple</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><th>J+2</th><td>0,0543</td><td>0,0855</td><td>0,09</td><td>0,0884</td></tr><tr><th>J+3</th><td>0,0883</td><td>0,0828</td><td>0,0885</td><td>0,0897</td></tr><tr><th>J+4</th><td>0,0803</td><td>0,0838</td><td>0,0919</td><td>0,0896</td></tr><tr><th>J+5</th><td>0,0821</td><td>0,0934</td><td>0,089</td><td>0,0901</td></tr></tbody></table>

<table><caption>Table 9: Table summarizing the errors of the different algorithms after evaluation of the Belle Ville Piquage zone.</caption><thead><tr><th></th><th>MLP</th><th>RNN Simple</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><th>J+2</th><td>0,4234</td><td>0,0039</td><td>0,4419</td><td>0,0088</td></tr><tr><th>J+3</th><td>0,3945</td><td>0,0069</td><td>0,4442</td><td>0,0051</td></tr><tr><th>J+4</th><td>0,418</td><td>0,0057</td><td>0,431</td><td>0,008</td></tr><tr><th>J+5</th><td>0,4339</td><td>0,0025</td><td>0,4482</td><td>0,0045</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>Fig. 7. Variation of observations and predicted values of Bolo vers ville</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 8. Variation of observations and predicted values of high Sarfalao</figcaption></figure>
## 6.2 Figures

<figure><img src="image_4.png" /><figcaption>Figure 1: The architecture of a multi-layer perceptron. [33]</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 2: Schema of a RNN. [32]</figcaption></figure>
## 6.4 Discussions

The entire training process revolved around eight (8) datasets, each containing 1091 rows, using four (4) neural network algorithms, including MLP, RNN, GRU, and LSTM. Multiple rounds of experiments were conducted on each algorithm, during which the evaluation criteria consisted of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE).

At the end of validation, the models are ready to make predictions on the data. The relevance of these models is based on the difference between the predicted and observed data, which is determined by the RMSE and MAE errors. Some algorithms outperformed the others, such as the GRU, MLP, and LSTM algorithms. In the case of the GRU algorithm, it achieved superior results on most of the data. The average MAE errors obtained during validation are the lowest, with values of 0.1389 for high Sarfalao, 0.0708 for Belle ville, and 0.0341 for Lafiabougou data. As for the MLP algorithm, it yielded the lowest average RMSE errors of 0.0804 for BAMA data and 0.0884 for Bolo vers ville. These two algorithms are ranked as the best in terms of MAE errors. Overall, the GRU algorithm provides better predictions for most of the data used.

The table 2 presents the various best values obtained for Mean Absolute Errors (MAE) after training with different algorithms.

<table><caption>**Table 2.** Table recapitulating the MAE error values of the different algorithms after evaluation</caption><thead><tr><th rowspan="2">Model<br>Data</th><th colspan="4">MAE</th></tr><tr><th>MLP</th><th>Simple RNN</th><th>LSTM</th><th>GRU</th></tr></thead><tbody><tr><td>KUA</td><td>0,0856</td><td>0,0847</td><td>0,0849</td><td>0,0861</td></tr><tr><td>LAFIABOUGOU</td><td>0,0838</td><td>0,0957</td><td>0,0801</td><td>0,0341</td></tr><tr><td>LOW SARFALAO</td><td>0,0906</td><td>0,0841</td><td>0,0834</td><td>0,0841</td></tr><tr><td>HIGH SARFALAO</td><td>0,1515</td><td>0,1659</td><td>0,1584</td><td>0,1389</td></tr><tr><td>BAMA</td><td>0,0804</td><td>0,0855</td><td>0,0512</td><td>0,0870</td></tr><tr><td>BELLE VILLE</td><td>0,2272</td><td>0,2265</td><td>0,2260</td><td>0,0708</td></tr><tr><td>BOLO VERS VILLE</td><td>0,0884</td><td>0,0934</td><td>0,0908</td><td>0,0908</td></tr><tr><td>BELLE VILLE<br>QUILTING</td><td>0,0978</td><td>0,4493</td><td>0,4271</td><td>0,0173</td></tr></tbody></table>

## 7 Application

At the end of the training of the various models, we exported the models in files which allowed us to carry out a phase of simulation starting from the random data of consumption and temperature. During this phase, we implemented a small interface (figure 9 et 10) allowing us to facilitate the simulation. This figure is composed as follows:

* a drop-down list to select a given model,

* a consumption data entry area,

* a temperature entry zone,

* a button to press to "start prediction",

* an area that displays the result of the prediction,

This phase ended with the setting up of a table (confer table 3) containing the prediction of the consumption of day J from the random data of the day J-1
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>Figure 3: Schema of a LSTM. [32]</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 4: Schema of a GRU. [25]</figcaption></figure>
<figure><img src="image_13.png" /></figure>

lincs

<figure><img src="image_14.png" /><figcaption>Fig. 10. BELLE VILLE prediction interface</figcaption></figure>

<table><caption>Table 3. Simulation result of the different models</caption><thead><tr><th rowspan="2"></th><th colspan="2">Day J-1</th><th>Day J</th></tr><tr><th>consumption</th><th>Temperature</th><th>consumption</th></tr></thead><tbody><tr><td>KUA</td><td>8000</td><td>34</td><td>7914</td></tr><tr><td>LOW SARFALAO</td><td>4500</td><td>34</td><td>5320</td></tr><tr><td>HIGH SARFALAO</td><td>2500</td><td>34</td><td>2191</td></tr><tr><td>BAMA</td><td>19000</td><td>34</td><td>18701</td></tr><tr><td>BELLE VILLE</td><td>2000</td><td>34</td><td>1892</td></tr><tr><td>LAFIABOUGOU</td><td>5500</td><td>34</td><td>5688</td></tr><tr><td>BOLO VERS VILLE</td><td>4500</td><td>34</td><td>4711</td></tr><tr><td>BELLE VILLE QUILTING</td><td>1500</td><td>34</td><td>1644</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>Figure 5: Variation des valeurs observées et celles prédites de la zone Lafiabougou par l'algorithme GRU.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure 6: Variation des valeurs observées et celles prédites de la zone Sarfalaho haute par l'algorithme GRU.</figcaption></figure>
# 8 Conclusion and perspectives

## Conclusion

This task was the place for us to apply algorithms in the field of water. The results obtained allowed us to compare the performance of the different algorithms on each of the eight (8) data used. At the end of the work, the models allow predicting daily water consumption, a solution that shows the importance of neural networks in the hydraulic field. The solution thus makes it possible to optimize water management and save resources for a better future.

Through this study, we have covered the essentials of the technologies of the machine learning which are used more and more nowadays. These technologies have importance in the life of man because they help to improve these living conditions. Several of them are increasingly essential in changes in certain areas of life. This is the case of our field of study who see the intervention of these technologies in the improvement and optimization of its management. The tools used in the implementation allowed us to learn of advantages on their importance and to know their capacity in the implementation place forecasting models.

## Perspectives

Although we conducted a study to find solutions for improving water management through technology, it is worth noting that these solutions are proposed with the aim of optimizing the management of drinking water. Indeed, during the data collection process from ONEA, we were able to identify the management issues and offer implementable solutions. Thus, the implementation of a data storage system is proposed to gather consumption data from distribution areas. This system will facilitate data input for forecasting models, providing readily usable data. Furthermore, a study can be conducted for predicting the quantity of water consumed while taking into account demographic factors, which can be a contributing factor to limited access to drinking water. Regarding long-term solutions, once the distribution system is optimized, it would be prudent to establish a water leak control system, as water leaks represent a considerable loss of a resource with limited access.
# VII CONCLUSION AND REFERENCES

## 7.1 Discussion

This study builds upon the work of [33], which focused on forecasting water consumption for a given day based on previous consumption data. The dataset used in that prior research is the same as the one employed in the present study, which aims to predict water consumption for days J+2 through J+5. The algorithms used are those previously described, and their performance is evaluated based on the Mean Absolute Error (MAE) metric.

Following the training and evaluation phases, the best MAE values vary depending on the algorithm, dataset, and prediction horizon. For day J+2, the GRU algorithm yielded the best results for the Belle Ville and KUA datasets. The simple RNN performed best on the Lafiabougou and High Sarfalaho datasets, while the MLP model achieved the highest accuracy for Bama and Low Sarfalaho.

For day J+3, the results shift slightly, with some algorithms maintaining their performance across the same datasets, while others change. For instance, the datasets corresponding to Lafiabougou, Belle Ville, and Bama continue to yield optimal performance with the same algorithms used for J+2. In contrast, for KUA, High Sarfalaho, and Low Sarfalaho, the best-performing algorithms are MLP, GRU, and simple RNN, respectively.

For the J+5 forecast horizon, the zones that produced the best models using the MLP algorithm are KUA, Low Sarfalaho, and Bama. The GRU algorithm demonstrated superior performance on the High Sarfalaho, Belle Ville, and Lafiabougou datasets.

Across all the analyzed prediction horizons, the LSTM algorithm never emerged as the top-performing model in the various cases studied. However, it consistently produced the best-performing model for all forecast horizons when applied to data from the Bolo Vers Ville zone. No other algorithm managed to produce satisfactory models for this zone, as indicated by the MAE values.

Compared to the study conducted in [33], the GRU algorithm consistently stands out in terms of performance. This result can be attributed to its strong ability to solve complex problems, particularly in the context of time series processing.

Figures 5 and 6 illustrate the variation between observed and predicted values for the Belle Ville and High Sarfalaho datasets, respectively, using the different algorithms.
References

1. Dr Pezon, Christelle and Nansi, Juste and Bassono, Richard: De l'accès aux systèmes de distribution d'eau potable à l'accès aux services d'eau potable : méthode et outils, IRC (Avril 2012)

2. Mohamed, Bouamarand and Mohamed, Ladjal: Système multicapteur utilisant les réseaux de neurones artificiels pour la surveillance des eaux potables, LASS Laboratoire d'Analyse des Signaux et Systèmes Université de M'sila Algérie (2007)

3. BELOURGHI, Bariza and HOUICHI, Larbi and HEDDAM, Salim: Reseaux De Neurones Artificiels Pour La Modelisation Du Dosage Du Coagulant Dans Les Stations De Traitements Des Eaux De Surface a Faible Turbidite, Université de Batna, Algérie (septembre 2012)

4. Nutini, Julie: Feedforward Neural Nets and Backpropagation, University of British Columbia (septem-
bre 2016)

5. Gelly, Gregory: Reseaux de neurones recurrents pour le traitement automatique de la parole, Université Paris-Saclay préparée à l'Université Paris-Sud (septembre 2017).

6. Toque, Florian: Prévision et visualisation de l'affluence dans les transports en commun à l'aide de méthodes d'apprentissage automatique, UNIVERSITÉ PARIS-EST, Ecole doctorale MSTIC (décembre 2019).

7. Zaki, Sabit Fawzi Philippe: Classification par réseaux de neurones dans le cadre de la scattérométrie ellipsométrique, These de doctorat de l'universite deyon opéréeau sein de l'universitéjean monnet de saint étienne, Ecole Doctorale N° 488 Science-Ingénierie-Santé, p. 68, (Décembre 2016).

8. Valentin NOËL: Séries temporelles et réseaux de neurones récurrents, Ecole normale superieure Paris-saclay (2022).
## 7.2 Conclusion

This study highlights the effectiveness of machine learning algorithms in predicting water consumption data. Comparative analysis revealed that no single algorithm consistently outperformed the others across all datasets and time periods. Instead, performance varied depending on the specific characteristics of the dataset and the prediction horizon. These results underscore the importance of selecting appropriate algorithms and considering contextual factors when developing predictive models for water consumption. Future research could focus on refining model architectures and incorporating additional features to further enhance prediction accuracy.

Overall, the insights gleaned from this study can inform water management practices, enabling water management agencies to make informed decisions to optimize water distribution and ensure sustainability in the face of increasing demand and environmental challenges.
## REFERENCES

[1] X. Ding, S. Canu, and T. Denoeux. "Neural network models for forecasting". In: *Neural Networks and their Applications*. Edited by J. G. Taylor. Chichester, UK: John Wiley and Sons, 1996.

[2] N. Valentin. "Construction d'un capteur logiciel pour le contrôle automatique du procédé de coagulation en traitement d'eau potable". In: *UTC*. Dec. 2000.

[3] A. Schmitt, B. Le Blanc, M.-M. Corsini, C. Lafond, and J. Brůžek. "Les réseaux de neurones artificiels". In: volume 13. June 2001.

[4] J. Vazquez, M. Zug, D. Bellefleur, B. Grandjean, and O. Scrivener. "Utilisation d'un réseau de neurones pour appliquer le modèle de Muskingum aux réseaux d'assainissement". In: 2005.

[5] M. Bouamarand and M. Ladjal. "Système multicapteur utilisant les réseaux de neurones artificiels pour la surveillance des eaux potables". Master's thesis. LASS Laboratoire d'Analyse des Signaux et Systèmes Université de M'sila Algérie, 2007.

[6] I. Azoui, L. Houichi, and D. Bengora. "Modélisation sous l'effet du retard de la quantité globale hebdomadaire de boue produite par une station d'épuration à boue activée - cas de la station de la ville de Batna". In: *Université de Batna, Algérie*. 2012.

[7] B. Bariza, H. Larbi, and H. Salim. "Reseaux De Neurones Artificiels Pour La Modélisation Du Dosage Du Coagulant Dans Les Stations De Traitements Des Eaux De Surface a Faible Turbidite". In: Sept. 2012.

[8] S. Heddam, A. Bermad, and N. Dechemi. "Modélisation de la dose de coagulant par les systèmes à base d'inférence floue (ANFIS) : application à la station de traitement des eaux de Boudouaou (Algérie)". In: *Université du Québec - INRS-Eau, Terre et Environnement (INRS-ETE)*. 2012.

[9] A. Holmes. "Hadoop in Practice: includes 85 techniques". In: 2012.

[10] C. Pezon, J. Nansi, and R. Bassono. "De l'accès aux systèmes de distribution d'eau potable à l'accès aux services d'eau potable : méthode et outils". In: *IRC*. Apr. 2012.

[11] C. Salhi, B. Touaibia, and A. Zeroual. "Neural networks and multiple regression approaches in the prediction of specific erosion: case of the Algérois-Hodna-Soummam basin, Algeria". In: volume 58. Oct. 2013.

[12] E. Cloarec, A. Jlassi, and P. Martineau. "Hadoop: Optimisation et Ordonnancement". In: 2014.

[13] E. Roux. "Développement d'un outil d'aide au diagnostic pour la production de maïs permettant la réduction de la consommation en eaux d'irrigation et en traitements phytosanitaires". In: *Université de Toulouse, INSA de Toulouse*. Dec. 2015.

[14] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. "TensorFlow: A System for Large-Scale Machine Learning". In: 2016.

[15] K. Belhassen, I. Nouiri, A. Jeridi, and S. Ridene. "Prévision de la demande en eau potable journalière par les réseaux de neurones artificiels : Cas du système de répartition du grand Tunis, Tunisie". In: Nov. 2016.

[16] Ministère de l'Eau et de l'Assainissement (MAE). "Programme National d'Approvisionnement en Eau Potable 2016-2030". In: May 2016.

[17] J. Nutini. "Feedforward Neural Nets and Backpropagation". In: *University of British Columbia*. Sept. 2016.
[18] R. Rakotomalala. "Deep Learning : Perceptrons simples et multicouches". In: *Université Lumière Lyon 2*. Sept. 2016.

[19] K. Yohou and L. Modeste. "Architecture de stockage de Big Data : Mise en oeuvre de cluster Cassandra". In: *Université Nangui Abrogoua*. Oct. 2016.

[20] S. F. P. Zaki. "Classification par réseaux de neurones dans le cadre de la scattérométrie ellipsométrique". PhD thesis. These de doctorat de l'université de Lyon opérée au sein de l'université Jean monnet de saint étienne, Ecole Doctorale N° 488 Science-Ingénierie-Santé, Décembre 2016.

[21] Z. Ali. "Apprentissage de représentation pour la prédiction et la classification de séries temporelles, Université Pierre et Marie Curie École Doctorale Informatique, Télécommunications et Électronique". In: Oct. 2017.

[22] G. Gelly. "Reseaux de neurones recurrents pour le traitement automatique de la parole". PhD thesis. Université Paris-Saclay préparée à l'Université Paris-Sud, Sept. 2017.

[23] S. Allemand. "Introduction aux technologies et applications Big Data". In: *Actes des Ateliers du Numérique Francophone (ANF)*. 2018.

[24] A. K. Y. Settouti. "Indexation et sélection des services Cloud Computing IaaS pour des réseaux de capteurs". In: *Université Abou-Bekr Belkaid - Tlemcen*. May 2018.

[25] F. Toque. "Prévision et visualisation de l'affluence dans les transports en commun à l'aide de méthodes d'apprentissage automatique". PhD thesis. UNIVERSITÉ PARIS-EST, Ecole doctorale MSTIC, décembre 2019.

[26] S. Djerarda and K. Hedli. "Traitement automatique des eaux basé sur les techniques d'apprentissage statistique". In: *Université Mohamed Boudiaf - M'sila*. 2020.

[27] R. Oulebsir. "Élaboration d'un dash-board par data-mining pour le benchmarking des STEP". In: *Université des Sciences et de la Technologie Houari Boumediene, Faculté de Génie Civil*. Nov. 2020.

[28] T. Ren, X. Liu, J. Niu, X. Lei, and Z. Zhang. "Real-time water level prediction of cascaded channels based on multilayer perception and recurrent neural network". In: 2020.

[29] A. Sere, M. Kabore, H. T. Achi, T. Kone, and A. K. Atiampo. "Analysis of Data Research in Smart GIS Database using Bayesian Probability for Prediction". In: *Conference Proceedings*. Volume 6. 2020.

[30] J. A. Ouedraogo. "Extraction et traitement distribué des posts sur les réseaux sociaux". In: *École Supérieure d'Informatique, Université Nazi BONI*. Nov. 2021.

[31] A. Boudhaouia. "Analyse, classification et prédiction de consommation d'eau et d'électricité par des techniques de machine learning". In: *Université de Haute Alsace*. Feb. 2022.

[32] V. NOËL. *Séries temporelles et réseaux de neurones récurrents*. 2022.

[33] P. Konate, A. Sere, and M. Diarra. "Prediction of drinking water needs: the case of Bobo-Dioulasso". In: EAI, June 2024.

[34] A. Krause and A. Vroutsis. "Hadoop YARN". In: *The University of Edinburgh*.

[35] N. P. Rougier. "Perceptron simple, Perceptron multi-couches". In: *Cours de Sciences Cognitives, Université de Bordeaux, Master 2*.

[36] M. Nohair, A. St-Hilaire, and T. Ouarda. "Utilisation des réseaux de neurones et de la régularisation bayésienne en modélisation de la température de l'eau en rivière". In: 2008).