Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: Comparing different deep learning models

Amini, Amirmasoud; Dolatshahi, Mehri; Kerachian, Reza

doi:10.1016/j.jhydrol.2024.130804

Cited by 9 publications

(1 citation statement)

References 49 publications

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“…This study uses an LSTM model coupled with VMD and PCA methods to predict the monthly runoff and finds that the hybrid model can enhance the model's predictive accuracy, particularly during the flood season. This is consistent with previous studies that reported an improvement based on the deep learning and decomposition technique for the runoff prediction [53,54]. It is worth noting that considering the structural differences of the model, different results can be obtained under different deep learning models.…”

Section: Discussionsupporting

confidence: 91%

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Wang,

Tang,

Zou

et al. 2024

Water

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Accurate forecasting of monthly runoff is essential for efficient management, allocation, and utilization of water resources. To improve the prediction accuracy of monthly runoff, the long and short memory neural networks (LSTM) coupled with variational mode decomposition (VMD) and principal component analysis (PCA), namely VMD-PCA-LSTM, was developed and applied at the Waizhou station in the Ganjiang River Basin. The process begins with identifying the main forecasting factors from 130 atmospheric circulation indexes using the PCA method and extracting the stationary components from the original monthly runoff series using the VMD method. Then, the correlation coefficient method is used to determine the lag of the above factors. Lastly, the monthly runoff is simulated by combining the stationary components and key forecasting factors via the LSTM model. Results show that the VMD-PCA-LSTM model effectively addresses the issue of low prediction accuracy at high flows caused by a limited number of samples. Compared to the single LSTM and VMD-LSTM models, this comprehensive approach significantly enhances the model’s predictive accuracy, particularly during the flood season.

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Section: Discussionsupporting

confidence: 91%