Prédiction de la qualité de l'eau en utilisant des méthodes machine learning.

Abstract

Various pollutants have presented a threat to water quality in recent years. As a result, water quality modeling and prediction have become critical in the fight against pollution. Advanced artificial intelligence (AI) algorithms are explained in this comparative study, including how they predict water quality using the water quality index (WQI) and water quality classification (WQC). specifically we talked about artificial neural networks and we took two examples of the models For the WQI, the deep learning algorithms nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) were used. The dataset used has seven significant parameters, and the developed models were assessed using statistical parameters. The findings revealed that the proposed models are capable of accurately predicting WQI and classifying water quality based on robustness. The NARNET model performed slightly better than the LSTM model in predicting WQI values, according to the results.Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient (R NARNET=96:17% and R LSTM=94:21%). This kind of promising research can contribute significantly to water management.