Prediction of Water Consumption Using Machine Learning Algorithms

Pierre Konate, Abdoulaye Séré, Diarra Mamadou

The objective of this study is to propose solutions for predicting drinking water consumption over aperiod of 5 days. To achieve this, machine learning methods such as neural network algorithms areused to confront different results in order to obtain a model that optimizes daily water consumptionthe most. The algorithms used include Multi-Layer Perceptron (MLP), Simple Recurrent NeuralNetwork, 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 areused to train models on each dataset. At the end of this study, the model that optimizes the best isselected for each dataset after comparing the results of different algorithms. Based on the variousresults 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 dayperiod is attributed to the MLP algorithm based on mean absolute errors (MAE).