Authors : Md Abu Bakar Siddik Farhan; Tanvir Alam; Md Gazi Jamal Uddin; Shilpi Mondal

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/jvztfeum

DOI : https://doi.org/10.38124/ijisrt/26sep1313

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Machine learning (ML) models are increasingly reported to estimate river discharge from stage more accurately than the conventional power-law rating curve. Most of these comparisons, however, rely on randomly split data, which mixes old and new gaugings in training and testing and ignores the fact that rating relations drift as river channels change. We compared a power-law rating curve (RC) with Random Forest, XGBoost, support vector regression and a neural network using 14,317 discharge gaugings (1969-2022) from 16 Bangladesh Water Development Board stations on 11 rivers in northwest Bangladesh. ML models were trained with stage alone and with additional hysteresis and seasonal inputs derived from daily water levels.

Keywords : Rating Curve; Stage-Discharge Relationship; Machine Learning; Chronological Validation; Non-Stationarity; Bangladesh.

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