Document Type : Research Paper

Authors

• Ramtin Tavoosi Rad ¹
• Mohamad Ansari ghojghar ²
• Arash Malekian ³

¹ Master Candidate, 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 Professor, Department of Reclamation of Arid and Mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran

Abstract

Introduction
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 of this two single models, (Cat-XG)Boost were evaluated to predict runoff in the Karaj watershed. Few studies have compared these two algorithms and presented an intelligent hybrid combination at the monthly level.While filling this research gap the present study can provide practical guidance for water resource planners and resource management warning systems in climate change conditions.
 
Materials and method
In the present study, the efficiency of two widely used individual deep learning models, including CatBoost, XGBoost and a novel hybrid model called (Cat-XG)Boost were evaluated in predicting monthly runoff in the Karaj watershed. For this purpose, monthly average discharge data from five hydrometric stations of Gachsara, Sira-Kaloan, Sira-Karaj, Neshtarud and Pol-e-Khab Dorud with maximum monthly average discharge of 17.22, 16.56, 12.11, 0.36 and 0.28, respectively, in the Karaj watershed during the long-term statistical period of 30 years (1371-1400) were used as model inputs. After performing preprocessing, normalization, outlier removal, stationarity check and input transformation processes, the models were used as inputs. The models were measured with 4 evaluation criteria NS, R, RMSE and MAE. The predictions of the studied models were recorded in 4 states. First, the average discharge of the past 3 months was considered as the model input. In the second scenario, the average discharge of the 6 months before was entered into the model. The third scenario was designed using three consecutive delay seasons (9 months) of monthly average discharge. The fourth scenario included a year of consecutive time lag.
 
Results and discussion
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. Analysis of correlation diagrams also showed that the integrated model had the highest agreement with the actual values and identified complex runoff patterns with higher accuracy. The results obtained from the present study were also consistent with international studies. 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.
 
 
 
Conclusion
The results of the present study indicate that the hybrid model (Cat-XG)Boost can be used as a powerful tool for runoff prediction in mountainous and climate change-sensitive basins and play an effective role in the development of rapid warning systems, reservoir management and strategic water resource planning. The findings of the present study confirm that combined approaches in the face of complex hydrological data have a significant advantage over single models. This model can be used as a useful tool in water resource management studies, prediction of drought and seasonal floods and presentation of decision-making scenarios at the basin level.

Keywords

• Flood control
• Deep learning
• Metaheuristic algorithm
• Mountainous basins
• Water resource management