Improving the resolution of seismic data based on S-transform and modified variational mode decomposition, an application to Songliao Basin, Northeast China

Liu, Siqin; Zhou, Zhu-sheng; Peng, Sanxi; Yang, Yang; Zeng, Weizu; Chen, Keyang

doi:10.1007/s11600-022-00781-z

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…However, the multi-model method [ 27 – 29 ] provides the possibility for stable runoff prediction. Among them, model average method is an effective method to deal with model uncertainty and improve model performance and stability [ 30 , 31 ], mainly including Bayesian Model Average (BMA) [ 27 ] and Frequency Model Average (FMA) [ 28 ]. Among them, BMA is one of the most commonly and widely used methods to generate reliable model prediction.…”

Section: Introductionmentioning

confidence: 99%

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.

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