Hydrological model parameter regionalization: Runoff estimation using machine learning techniques in the Tha Chin River Basin, Thailand

• Published in: MethodsX Vol. 13; p. 102792
• Main Authors: Hlaing, Phyo Thandar, Humphries, Usa Wannasingha, Waqas, Muhammad
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2024; Elsevier
• Subjects: Advancements in Daily Precipitation Prediction; Engineering; Hydrological modeling; Machine Learning; Regionalization; SWAT; Ungauged basin; Qgw; ANN; Ungauged basin; Advancements in Daily Precipitation Prediction; SVM; Qsurf; Machine Learning; SW0; PBIAS; SWt; ETa; GSA; DSMW; ML; R2; NSE; KGE; Regionalization; HRUs; TMD; Wseep; RID; Rday; DEM; SWAT; RSR; Hydrological modeling; RF; SWAT-CUP; TIN; SUFI-2
• ISSN: 2215-0161; 2215-0161
• DOI: 10.1016/j.mex.2024.102792

Understanding hydrological processes necessitates the use of modeling techniques due to the intricate interactions among environmental factors. Estimating model parameters remains a significant challenge in runoff modeling for ungauged catchments. This research evaluates the Soil and Water Assessment Tool's capacity to simulate hydrological behaviors in the Tha Chin River Basin with an emphasis on runoff predictions from the regionalization of hydrological parameters of the gauged basin, Mae Khlong River Basin. Historical data of Mae Khlong River Basin from 1993 to 2017 were utilized for calibration, followed by validation using data from 2018 to 2022. •Calibration results showed the SWAT model's reasonable accuracy, with R² = 0.85, and the validation with R² of 0.64, indicating a satisfactory match between observed and simulated runoff.•Utilizing Machine Learning (ML) techniques for parameter regionalization revealed nuanced differences in model performance. The Random Forest (RF) model exhibited an R² of 0.60 and the Artificial Neural Networks (ANN) model slightly improved upon RF, showing an R² of 0.61 while the Support Vector Machine (SVM) model demonstrated the highest overall performance, with an R² of 0.63.•This study highlights the effectiveness of the SWAT and ML techniques in predicting runoff for ungauged catchments, emphasizing their potential to enhance hydrological modeling accuracy. Future research should focus on integrating these methodologies in various basins and improving data collection for better model performance. [Display omitted]