Modeling Chlorophyll-A in Taihu Lake with Machine Learning Models

This paper studies the relation between chlorophyll-a and 10 environmental factors such as water temperature (T), COD, NH₄ ⁺, NO₃ ^- TN, PO4₃ ⁺, TP, suspend solids (SS), Secci-depth (SD) and water depth (D) based on the monitoring data of 2005 in Taihu Lake. Three kinds of models are designed using the multiple regression statistical (MRS) method, the back propagation artifical neural network (BP ANN) and the support vector machine (SVM). The model validation shows that the machine learning models, BP ANN model and SVM model, work better than the linear MRS model, and the SVM presents the best performance in terms of root mean square error. The sensitivity analysis indicates that the concentration of chlorophyll-a is very sensitive to the changes of water temperature, water depth, and total nitrogen, but does not show significant changes to phosphorous variables such as total phosphorus and orthophosphate. It implies that algae blooms are more likely decided by physical parameters and accumulated at shallow areas by wind.