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arXiv Machine Learning By Hamid Saadatfar, AmirHossein Eshghi, Behnaz Behdani

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

Read the original on arXiv Machine Learning →

The study presents a daily runoff prediction model for the Ajichay River in northwest Iran, employing an ensemble of five Long Short-Term Memory (LSTM) units trained on varying time intervals (2–6 days). Each LSTM is fine‑tuned with Particle Swarm Optimization to enhance accuracy, and the ensemble’s performance on 2017–2018 data shows $R^2$ values between 74.95% and 91.42%, with RMSE and MSE also reported. Feature‑importance analyses identify key variables influencing runoff variations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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