Document Type : Research Paper
Authors
1 Department of Reclamation of Arid and Mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
2 Assistant professor, Department of reclamation of arid and mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
3 Department of Reclamation of Arid and Mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran
Abstract
Accurate runoff prediction plays a crucial role in water resource management, flood control, and climate change adaptation planning. Given the nonlinear, complex, and multifactorial nature of hydrological processes, the use of data-driven methods and machine learning algorithms has become an efficient approach for runoff analysis and modeling in recent years. Two individual models, XGBoost, CatBoost, and a hybrid model Boost(Cat-XG) were evaluated to predict runoff in the Karaj watershed. The models were measured with 4 evaluation criteria NS, R, RMSE and MAE. The prediction results showed that the hybrid model (Cat-XG)Boost with a significant difference provides the best performance in predicting monthly runoff of the Karaj watershed compared to the two individual models evaluated. This model recorded NS above 0.957 and correlation above 0.939 in all stations studied. In addition, it recorded significantly fewer errors than the other two models. While the individual models XGBoost and CatBoost, especially in stations with more extensive data, faced increased errors. The two individual models studied provided average performance in predicting values related to extreme climate events, but by combining the two individual models and introducing the hybrid model Boost(Cat-XG), the defects in the individual models were covered and also by eliminating existing errors, much more accurate predictions were recorded.
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