Thông Tin Bài Báo
Multi-step-ahead prediction of water levels using machine learning: A comparative analysis in the Vietnamese Mekong Delta
Tác giả: PGS.TS Nguyễn Tiền Giang
Tác giả: Nguyen Duc Hanh, Nguyen Tien Giang, Le Xuan Hoa, Tran Ngoc Vinh, Huu Duy Nguyen
Tạp chí: Vietnam Journal of Earth Sciences
Lĩnh vực nghiên cứu: Phân tích dữ liệu trong tài nguyên và môi trường nước
Bộ môn: Bộ môn Thủy văn và Tài nguyên nước
Năm xuất bản: 2024
Mô tả:
This study evaluates the efficacy of five machine learning algorithms Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine Regressor (LGBM), and Linear Regression (LR) in predicting water levels in the Vietnamese Mekong Delta's tidal river system, a complex nonlinear hydrological phenomenon. Using daily maximum, minimum, and mean water level data from the Cao Lanh gauging station on the Tien River (2000-2020), models were developed to forecast water levels one, three, five, and seven days in advance. Performance was assessed using Nash-Sutcliffe Efficiency, coefficient of determination, Root Mean Square Error, and Mean Absolute Error. Results indicate that all models performed well, with SVR consistently outperforming others, followed by RF, DT, and LGBM. The study demonstrates the viability of machine learning in water level prediction using solely historical water level data, potentially enhancing flood warning systems, water resource management, and agricultural planning. These findings contribute to the growing knowledge of machine learning applications in hydrology and can inform sustainable water resource management strategies in delta regions.
