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

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

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...

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Bibliographic Details
Main Authors:	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.
Other Authors:	57209510674
Format:	Article
Published:	Ain Shams University 2025
Subjects:	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
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Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques

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