2023
DOI: 10.3389/frwa.2023.1112970
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Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods

Abstract: Long short-term memory (LSTM) networks have demonstrated successful applications in accurately and efficiently predicting reservoir releases from hydrometeorological drivers including reservoir storage, inflow, precipitation, and temperature. However, due to its black-box nature and lack of process-based implementation, we are unsure whether LSTM makes good predictions for the right reasons. In this work, we use an explainable machine learning (ML) method, called SHapley Additive exPlanations (SHAP), to evalua… Show more

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Cited by 8 publications
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