New paradigm for watershed model development by coupling machine learning algorithm and mechanistic model

Abstract

The development of watershed models faces double pressure of data requirements and physical interpretation. The simulation of nonpoint source (NPS) pollution is an important application of watershed model, and the best management practices (BMPs) have attracted wide attention as the main control approach of NPS pollution. In this study, a new paradigm was proposed based on the integration of data-driven and mechanistic methods, taking BMP evaluation as an example. Specifically, comprehensive databases were constructed for filter and retention BMPs by collecting, classifying and analyzing published data. Twelve machine learning algorithms were employed for regression analysis between BMPs efficiency and their influencing factors, while the constructed equations were coupled with physical-based models. A case study was performed in a typical catchment of Chaohu Lake Watershed, China. The results demonstrated total interception area, soil type, and vegetation biomass had significant impacts on BMPs performances, while the multilayer perceptron regression (MLPR), K nearest neighbor regression (KNRD), and extremely randomized tree (ETR) methods had the best performances of nutrients removal. The study generated over ten thousand datasets using mechanistic processes, resulting in more efficient and interpretable BMPs evaluator. Compare to traditional methods, this new paradigm has shown advantages in the model application at the watershed scale, enhancing the BMPs evaluator with a higher level of interpretability by coupling the approach with mechanistic models.