Ấn phẩm:
Multi-step-ahead prediction of water levels using machine learning: A comparative analysis in the Vietnamese Mekong Delta
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Tóm tắt
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.
Mô tả
Vietnam Journal of Earth Sciences, Vol. 46, No. 4
Tác giả
Nguyen, Duc Hanh
Nguyen, Tien Giang
Le, Xuan Hoa
Tran, Ngoc Vinh
Huu, Duy Nguyen
Người hướng dẫn
Nơi xuất bản
Nhà xuất bản
Viện Địa chất, Viện Hàn lâm Khoa học và Công nghệ Việt Nam
Năm xuất bản
2024-10
ISSN tạp chí
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Từ khóa chủ đề
Water level , Multi-step-ahead prediction , Machine learning , Vietnamese Mekong delta
URI
Tài liệu tham khảo
Thông tin bản quyền
Tệp tin
Nguyen Duc Hanh, Nguyen Tien Giang, Le Xuan Hoa, Tran Ngoc Vinh, Huu Duy Nguyen.pdf
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