Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50-70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

Département d'Analytique Opérations et Technologies de l'Information Université de Québec à Montreal 315 Sainte Catherine Street East H2X 3X2 Montreal Canada

Department of Civil and Environmental Engineering Amirkabir University of Technology Tehran 1591634311 Iran

Department of Civil Environmental and Geomatic Engineering University College London Gower St London WC1E 6BT UK

Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11 12 80 233 Gdansk Poland

Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Studentská 1402 2 461 17 Liberec Czech Republic

New Era and Development in Civil Engineering Research Group Scientific Research Center Al Ayen University Nasiriyah Thi Qar 64001 Iraq

School of Civil Engineering University of Tehran Tehran Iran

School of Computing and Engineering University of West London London W5 5RF UK

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