Advanced Machine Learning Algorithms for Hydrological Modeling: Deep Learning, Ensemble Methods, and Hybrid Approaches

Abstract

The problems of water catastrophes and shortages have become one of the most urgent issues of the modern era, thereby rendering highly accurate predictions of river discharges a critical concern for governments, engineers, and climatologists. Although the conventional model of hydrological prediction based on physical principles of energy and mass conservation is theoretically flawless, it faces difficulties in adequately modeling catchment behavior against a backdrop of an ever-growing unpredictability of the precipitation regime and a rapidly changing structure of the land surface due to anthropogenic impacts. This paper examines whether a purposefully crafted set of machine learning models is capable of solving this problem. For this purpose, seven different algorithms are trained and tested using historical daily hydrometeorological data from five globally renowned river basins over a total period of seventy years. One significant contribution is the design of a Hybrid CNN-LSTM, where the loss function is enhanced with soft physical constraints using Manning's equation for open-channel flow and SCS Curve Number method of runoff, guiding the neural network weights towards physically consistent outputs while remaining adaptable to the input data. Another important contribution is the application of a stacking ensemble method, which combines the predictions of all the seven base-learners by training an additional ridge regression model. The benchmark tests show that the Hybrid CNN-LSTM beats plain LSTM with a 7.0% improvement (NSE = 0.931), while beating Random Forest model by 13.5%; meanwhile, the ensemble stack model achieves NSE of 0.944 and improves prediction timing error to only 2.1 hours, all under <0.3 seconds per inference.