A Multi-Timestep LSTM Ensemble regressor for Enhanced Short-Term Runoff Prediction

Hamid Saadatfar; AmirHossein Eshghi; Behnaz Behdani

cs.AIAug 14, 2026

A Multi-Timestep LSTM Ensemble regressor for Enhanced Short-Term Runoff Prediction

Authors: Hamid Saadatfar, AmirHossein Eshghi, Behnaz Behdani

Organizations: Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran · Department of Computer Science, Clemson University, Clemson, SC, USA

Abstract

Accurately forecasting river runoff is key to managing water resources, controlling floods, and planning agriculture. This study examines the Ajichay River in northwest Iran, a major tributary of Lake Urmia that has experienced increasing water-related stress in recent years. We introduce a daily runoff prediction model based on Long Short-Term Memory (LSTM) networks. The model combines five LSTM units, each trained on different time intervals ranging from 2 to 6 days, to better capture variations in river flow patterns. To improve performance, each model was fine-tuned using Particle Swarm Optimization (PSO), a population-based optimization algorithm. The proposed approach was evaluated on unseen data from 2017-2018 using $R^2$ , RMSE, and MSE as performance metrics. The results showed strong predictive accuracy, with $R^2$ values ranging from 74.95% to 91.42%. In addition, multiple feature-importance methods were applied to identify the most influential variables, providing further insight into the factors that drive runoff variations.

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Physics-Informed Machine Learning for Short-Term Flood Prediction

Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

North Carolina A&T State University · Greensboro, NC 27407, USA

Long Short-Term Memory Networks Physics-Informed ML Spatiotemporal Forecasting Flood Forecasting

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Accurate flood forecasting is essential for mitigating disaster risks and protecting communities. However, purely data-driven machine learning models often struggle in data-scarce environments and may violate fundamental hydrological principles. Standard Long Short-Term Memory (LSTM) networks can generate physically inconsistent predictions, particularly when extrapolating to extreme weather conditions. To address these limitations, we propose a Physics-Informed Machine Learning (PIML) framework that incorporates hydrological knowledge directly into the loss function of an LSTM model. Specifically, a Trend Alignment constraint penalizes directional inconsistencies between precipitation and discharge trends, improving model robustness without requiring complex hydrodynamic equations. This regularization encourages the model to learn physically plausible hydrograph behavior, even with limited training data, while enhancing reliability during peak flood events. Experimental results show that the proposed physics-informed model outperforms a standard LSTM baseline in data-scarce settings, increasing the Nash-Sutcliffe Efficiency (NSE) from 0.20 to 0.23 when trained on only 5% of the available data. Additional stress tests under simulated extreme climate scenarios demonstrate that the baseline model exhibits unstable behavior, whereas the physics-informed model maintains directional consistency and physical plausibility. Although accurately predicting extreme peak magnitudes remains challenging with limited data, the proposed approach substantially reduces unphysical fluctuations common in purely data-driven models. These findings demonstrate that simple physical constraints can significantly improve the reliability of deep learning models for real-time flood forecasting, offering a practical solution for ungauged basins and evolving climate conditions.

Abstract

Explore similar work

Physics-Informed Machine Learning for Short-Term Flood Prediction

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