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Modeling the rainfall-runoff of Ajichai Basin using intelligent models

Document Type : Research Paper

Authors

1 PhD Student in Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

2 Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

Abstract

Introduction
In recent years and decades, due to minor changes and challenges in sufficient and high-quality water resources, sustainable water resources have been the subject of various studies and research. The lack of safe and high-quality water resources is a major obstacle to sustainable development. For this reason, understanding the processes of the water cycle is very important and requires accurate information about hydrological phenomena. Runoff from water transfer is one of the main sources meeting various water demands, including agriculture, industry, and domestic use. The allocation of water resources to these sectors is planned based on runoff data at different times. A significant portion of precipitation in the hydrologic cycle is converted into runoff due to watershed characteristics. Considering the issue that the Lake Urmia Basin is shrinking, identifying the water resources of this basin and its sub-basins is crucial.
 
Materials and methods
The Ajichai Basin is one of the sub-basins of Lake Urmia. In this study, rainfall data from the Tabriz synoptic station and runoff data from the Nahand hydrometric station were used. The aim of this research is to model the daily rainfall-runoff of the Ajichai Basin using intelligent machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVM), Gene Expression Programming (GEP), and Random Forest (RF). Seventy percent of the data was used for training, and 30% was used for testing the models. Statistical measures such as the Coefficient of Determination (R²), Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models.
 
Results and discussion
The results of this research showed that all models performed very well in simulating rainfall-runoff in the Ajichai Basin. According to the obtained results, the GEP model, with R² equal 0.84, RMSE equal 0.024 m³/s, NSE equal 0.864, and WI equal 0.968, was the most accurate in modeling rainfall-runoff in the Ajichai Basin. Based on scatter plots and time series analysis, the GEP model demonstrated higher accuracy than other models in predicting rainfall-runoff values with a high correlation.
 
Conclusions
According to the results, all the investigated models showed good capability in modeling daily rainfall-runoff in the Ajichai Basin. The findings of this research highlight the strong performance of machine learning models in rainfall-runoff modeling. In general, due to the high accuracy of intelligent models, particularly the GEP model, in predicting daily rainfall-runoff, it is recommended to apply these methods to hydrological problems. Additionally, for future research, it is suggested that intelligent methods and data mining techniques be used to model the precipitation-runoff process in different basins separately for drought-affected and wet years.

Keywords

References
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Watershed Engineering and Management
Volume 17, Issue 1
April 2025
Pages 14-28
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