An attention-based LSTM model for long-term runoff forecasting and factor recognition

نویسندگان

چکیده

Abstract With advances in artificial intelligence, machine learning-based models such as long short-term memory (LSTM) have shown much promise forecasting long-term runoff by mapping pathways between large-scale climate patterns and catchment responses without considering physical processes. The recognition of key factors plays a vital role thus affects the performance model. However, there is no conclusion on which algorithm most suitable. To address this issue, an LSTM model combined with two attention mechanisms both input hidden layers, namely AT-LSTM, proposed for at Yichang Pingshan stations China. added automatically assign weights to 130 phenomenon indexes, avoiding use subjectively set algorithms. Results show that AT-LSTM outperforms Pearson’s correlation based terms four evaluation metrics monthly forecasting. Further, indirect prediction method verifies also performs effectively precipitation potential evapotranspiration forecasting, inferior establish direct link runoff. Finally, related are identified mechanism their impacts analyzed intra- inter-annual scales. can improve accuracy identify dynamic influence factors.

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ژورنال

عنوان ژورنال: Environmental Research Letters

سال: 2023

ISSN: ['1748-9326']

DOI: https://doi.org/10.1088/1748-9326/acaedd