We investigate the simultaneous daily prediction of the pH and temperature of a water point using AI-based methods. These parameters are part of the physicochemical parameters of surface water favoring the reproduction of parasitic worms responsible for Schistosomiasis. Wavelet Artificial Neural Network (WANN), Long Short Term Memory (LSTM) and Support Vector Regression (SVR) are the AI-based methods employed to build models with fifteen months collected data. They are evaluated through two metrics: root mean square (RMSE) and mean absolute error (MAE). The results show that in overall three methods give acceptable RMSE which varies from 1.59 to 0.17. WANN model shows the best performance with a RMSE equals to 0.17 and a MAE equals to MAE 0.12 over LSTM and SVR ones in forecasting parameters values one day ahead based on their two previous days observations.
BT - 2022 IEEE Multi-conference on Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD) DO - 10.1109/mne3sd53781.2022.9723395 LA - eng N2 -We investigate the simultaneous daily prediction of the pH and temperature of a water point using AI-based methods. These parameters are part of the physicochemical parameters of surface water favoring the reproduction of parasitic worms responsible for Schistosomiasis. Wavelet Artificial Neural Network (WANN), Long Short Term Memory (LSTM) and Support Vector Regression (SVR) are the AI-based methods employed to build models with fifteen months collected data. They are evaluated through two metrics: root mean square (RMSE) and mean absolute error (MAE). The results show that in overall three methods give acceptable RMSE which varies from 1.59 to 0.17. WANN model shows the best performance with a RMSE equals to 0.17 and a MAE equals to MAE 0.12 over LSTM and SVR ones in forecasting parameters values one day ahead based on their two previous days observations.
PB - IEEE PY - 2022 T2 - 2022 IEEE Multi-conference on Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD) TI - An AI-based approach to the prediction of water points quality indicators for schistosomiasis prevention ER -