Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques

• المؤلفون الرئيسيون: Pathan A.I., Sidek L.B.M., Basri H.B., Hassan M.Y., Khebir M.I.A.B., Omar S.M.B.A., Khambali M.H.B.M., Torres A.M., Najah Ahmed A.
• مؤلفون آخرون: 57209510674
• التنسيق: مقال
• منشور في: Ain Shams University 2025
• الموضوعات: Adaptive boosting; Dams; Forecasting; Forestry; Hydrology; Information management; Mean square error; Rain; Support vector machines; Water management; Water supply; Comparative assessment; Effective tool; Hydrological process; Machine learning techniques; Machine-learning; Mean absolute error; Multilayers perceptrons; Root mean squared errors; Support vector regressions; Water level prediction; Water levels

Machine learning (ML) techniques are rapidly emerging as effective tools in predicting complex hydrological processes. The present study aims to comparatively assess the efficacy of four machine learning algorithms ? Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector R...