Research Article
Prediction of drinking water needs: the case of Bobo-Dioulasso
@INPROCEEDINGS{10.4108/eai.18-12-2023.2348136, author={Pierre Konate and Abdoulaye Sere and Mamadou Diarra}, title={Prediction of drinking water needs: the case of Bobo-Dioulasso}, proceedings={Proceedings of the 6th Computer Science Research Days, JRI 2023, 18-20 December 2023, Ouagadougou, Burkina Faso}, publisher={EAI}, proceedings_a={JRI}, year={2024}, month={6}, keywords={machine learning data analysis prediction}, doi={10.4108/eai.18-12-2023.2348136} }- Pierre Konate
Abdoulaye Sere
Mamadou Diarra
Year: 2024
Prediction of drinking water needs: the case of Bobo-Dioulasso
JRI
EAI
DOI: 10.4108/eai.18-12-2023.2348136
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 D-day, given the consumption and temperature of D-day-1. This work was carried out using tools such as the Jupyter NoteBook environment and the Python language.