Abstract
Accurate multi-day streamflow forecasts support flood preparedness, but single holdout scores can conceal lead-time dependence, temporal instability, baseline skill, and uncertainty. This study compares Long Short-Term Memory, Random Forest, Multiple Linear Regression, Multi-Layer Perceptron, Support Vector Regression, and AdaBoost using an audited Bangladesh Water Development Board discharge record for the Bahadurabad Transit station on the Brahmaputra River. The source contained 10,314 entries from 1995 to 2023. After 275 excess same-date records were consolidated by date-wise averaging, 553 calendar days were missing; no temporal interpolation was applied. We constructed 9,272 complete samples using 30 antecedent discharges to predict Days 1–30 directly. Leakage-resistant evaluation used three expanding rolling-origin folds, target-end-date training cut-offs, a 2019–2020 calibration period, and an independent 2021–2023 test period. Moving-block bootstrap confidence intervals, split-conformal prediction intervals, flow-regime errors, and lag sensitivity were evaluated. Random Forest had the lowest mean rolling-origin RMSE, whereas Multi-Layer Perceptron had the lowest pooled test RMSE (6,763.47 m³/s; R²/NSE = 0.721); their uncertainty intervals overlapped. Ranking changed with horizon: Multiple Linear Regression was best at Day 1 and AdaBoost at Day 30. All models underestimated the highest-flow decile. The results support an uncertainty-aware discharge-only benchmark, not universal model superiority or operational flood-warning readiness.
Abbreviations
- LSTM:
Long short-term memory
- RF:
Random forest
- MLR:
Multiple linear regression
- MLP:
Multi-layer perceptron
- SVR:
Support vector regression
- AB:
AdaBoost
- ACF:
Autocorrelation function
- PACF:
Partial autocorrelation function
- BWDB:
Bangladesh Water Development Board
- MAE:
Mean absolute error
- MSE:
Mean squared error
- RMSE:
Root mean squared error
- R²:
Coefficient of determination
- ANN:
Artificial neural network
- CNN:
Convolutional neural network
- ARIMA:
AutoRegressive integrated moving average
- CART:
Classification and regression tree
- XGBoost:
Extreme gradient boosting
- LSSVR:
Least squares support vector regression
- GSA:
Gravitational search algorithm
- ODPC:
One-sided dynamic principal components
- GDP:
Gross domestic product
- BPTT:
Backpropagation through time
- RBF:
Radial basis function
- ML:
Machine learning
- DL:
Deep learning
- MCDA:
Multi-criteria decision analysis
- PSO:
Particle swarm optimization
- GA:
Genetic algorithm
- CI:
Confidence interval
- NSE:
Nash–Sutcliffe efficiency
- PI:
Prediction interval
- PICP:
Prediction interval coverage probability
- TFT:
Temporal fusion transformer
- UQ:
Uncertainty quantification
- XAI:
Explainable artificial intelligence
Acknowledgements
The authors acknowledge the Bangladesh Water Development Board for supplying the mean daily discharge record used in this study. The authors would like to express their sincere gratitude to the Military Institute of Science and Technology (MIST), Dhaka, Bangladesh, for providing research facilities and data support; to Multimedia University (MMU), Cyberjaya, Malaysia, for supporting the Article Processing Charges (APC) and providing administrative and research facilitation; and to the Deanship of Scientific Research at Shaqra University, Saudi Arabia, for their valuable support.
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Siddiquee, K.NeA., Sarker, M.T., Al Qwaid, M. et al. Advanced machine learning techniques for daily streamflow forecasting: a case study of the Brahmaputra River.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-69747-1
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DOI: https://doi.org/10.1038/s41598-026-69747-1
Keywords
- Brahmaputra River
- Streamflow forecasting
- Multi-horizon prediction
- Rolling-origin validation
- Uncertainty quantification
- Explainable machine learning
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