Not only are reservoir managers and aquatic scientists concerned with the environmental effects of water quality, civil engineers must also consider water quality to comply with regulations in the construction of new reservoirs, or in making structural and operational modifications to existing reservoirs. This study establishes a machine learning approach for predicting Carlson’s Trophic State Index (CTSI), which is a frequently used metric of water quality in reservoirs. Data collected over ten years (1995-2016) from the stations at 20 reservoirs in Taiwan were preprocessed as the input for the modeling system. Four well-known artificial intelligence (AI) techniques, ANN (Artificial Neural Network), SVM (Support Vector Machine), CART (Classification And Regression Technique), and LR (Linear Regression), were used to analyze in baseline and ensemble scenarios. The comprehensive comparison demonstrated that the ensemble ANN model, based on tiering method, is more accurate than the other single and ensemble models. The novelty of this study is providing a new approach of AI models, reducing the complexity of measuring three traditional parameters of CTSI formula, as an alternative to the conventional approach to predicting CTSI. This work contributes to the improvement of water quality management by providing a versatile technique that offers diverse predictive methods to meet the specific requirements of practitioners.
Jui-Sheng Chou– Professor, National Taiwan University of Science and Technology, Taipei, Taiwan (Republic of China)
National Taiwan University of Science and Technology, Taipei, Taiwan (Republic of China)
Jui-Sheng Chou specializes in project analytics and engineering management. Currently, he is a Distinguished Professor in the Department of Civil and Construction Engineering at National Taiwan University of Science and Technology. Dr. Chou received his BS and MS from National Taiwan University and his PhD in Construction Engineering and Project Management at the Department of Civil, Architectural and Environmental Engineering - The University of Texas at Austin.