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, streamflow forecasting was approached through the formulation of two distinct machine learning problems: categorical streamflow forecast and regression streamflow forecast. Due to the distinctive characteristics of these two adopted forms, selecting the correct algorithm for the machine learning problem along with their hyperparameter tuning process is critical to the realization of the desired results. For the distinct streamflow formulated scenarios, three neural network algorithms and their hyperparameter tuning strategy were investigated. The comparative empirical studies had revealed that formulated categorical-based streamflow forecast is a better choice than a regression-based streamflow forecast, regardless of the algorithms used