Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process

• 主要な著者: Chong K.L., Huang Y.F., Koo C.H., Sherif M., Ahmed A.N., El-Shafie A.
• その他の著者: 57208482172
• フォーマット: 論文
• 出版事項: Springer Science and Business Media Deutschland GmbH 2024
• 主題: Deep learning algorithms; Hyperparameter optimization; Machine learning algorithms; Streamflow forecasting; Forecasting; Long short-term memory; Regression analysis; Stream flow; Time series analysis; Water resources; Automated searches; Cross entropy; Deep learning algorithm; Entropy-based; Hyper-parameter; Hyper-parameter optimizations; Machine learning problem; Streamflow forecast; algorithm; empirical analysis; entropy; machine learning; optimization; peak flow; streamflow; water management; Learning algorithms

Streamflow forecasting has always been important in water resources management, particularly the peak flow, which often determines the seriousness of the impending flood. However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper,...