A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting-Reference-Cited by-同舟云学术

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A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting

Published:2024-05 Issue: Volume:634 Page:131128
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Zhou Renjie^ORCID,Zhang Yanyan,Wang Quanrong,Jin Aohan,Shi Wenguang

Publisher

Elsevier BV

Reference47 articles.

1. Simulation of karst spring discharge using a combination of time–frequency analysis methods and long short-term memory neural networks;An;J. Hydrol.,2020

2. An end-to-end multimodal voice activity detection using WaveNet encoder and residual networks;Ariav;IEEE J. Sel. Top. Signal Process.,2019

3. Karst groundwater: a challenge for new resources;Bakalowicz;Hydrogeol J,2005

4. Application of Nonlinear time series and machine learning algorithms for forecasting groundwater flooding in a lowland karst area;Basu;Water Resour. Res.,2022

5. Identifying the impact of climate and anthropic pressures on karst aquifers using wavelet analysis;Charlier;J. Hydrol.,2015

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