Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

作者：Alain Niyongabo, Danrong Zhang, Yiqing Guan, Ziyuan Wang, Muhammad Imran, Bertrand Nicayenzi, Alemayehu Kabeta Guyasa, Pascal Hatungimana · 发表于：Water · 年份：2024 · DOI：10.3390/w16131793 · 被引用次数：8 · 研究领域：Water Quality Monitoring Technologies、Hydrological Forecasting Using AI、Water resources management and optimization

Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water...