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<p>Groundwater is a relatively stable water resource and vital for human existence and economic development. Therefore, properly utilizing groundwater becomes very important when faced with water shortages. However, it is difficult to obtain information about the pumping amount, which has crucial impact on the groundwater hydrological cycle. Most studies use monthly groundwater data and previous time steps of groundwater information to model and predict without considering pumping information. Therefore, this study proposes a wavelet-deep learning model, combining wavelet analysis and deep learning, using the Daliao area of Kaohsiung, Taiwan as an example, which has considerable historical data from a smart groundwater real-time IoT pumping meter system to monitor pumping quantities for the main wells. We extracted relevant features from hourly observation data from 23 August 2017 through 30 January 2020, and investigated each factor’s features relative to groundwater level by wavelet transform, and then used recurrent neural networks (RNN) and long short-term memory (LSTM) deep learning models to summarize and predict multiple factor impacts on groundwater level under different time lags. Hourly prediction models of the LSTM and RNN achieved reliable cross validation performance with 0.81 and 0.78 coefficient of determination (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11269_2024_4088_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\({\text{R}}^{2}\)</EquationSource> </InlineEquation>), respectively. Especially after the feature extraction by wavelet analysis and the addition of artificial groundwater pumping information, it will greatly increase the prediction accuracy. This study provides a feasible and accurate approach for groundwater level prediction, and hence will be an important reference for groundwater resources management and risk assessment, helping to achieve sustainable groundwater use.</p>

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Groundwater Level Prediction by Wavelet Deep Learning with Smart Pumping Data

  • 期刊论文

  • 发表日期：

    2025年1月7日

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  • Tsai-Ning Weng,
  • Ray-Shyan Wu,
  • Tzu-Han Weng,
  • Yi-Ho Chen,
  • Tsai-Chun Lai,
  • Jui-Yun Hsieh,
  • Chu-Chun Hsu,
  • Yuan-Chien Lin

摘要

Groundwater is a relatively stable water resource and vital for human existence and economic development. Therefore, properly utilizing groundwater becomes very important when faced with water shortages. However, it is difficult to obtain information about the pumping amount, which has crucial impact on the groundwater hydrological cycle. Most studies use monthly groundwater data and previous time steps of groundwater information to model and predict without considering pumping information. Therefore, this study proposes a wavelet-deep learning model, combining wavelet analysis and deep learning, using the Daliao area of Kaohsiung, Taiwan as an example, which has considerable historical data from a smart groundwater real-time IoT pumping meter system to monitor pumping quantities for the main wells. We extracted relevant features from hourly observation data from 23 August 2017 through 30 January 2020, and investigated each factor’s features relative to groundwater level by wavelet transform, and then used recurrent neural networks (RNN) and long short-term memory (LSTM) deep learning models to summarize and predict multiple factor impacts on groundwater level under different time lags. Hourly prediction models of the LSTM and RNN achieved reliable cross validation performance with 0.81 and 0.78 coefficient of determination ( \({\text{R}}^{2}\) ), respectively. Especially after the feature extraction by wavelet analysis and the addition of artificial groundwater pumping information, it will greatly increase the prediction accuracy. This study provides a feasible and accurate approach for groundwater level prediction, and hence will be an important reference for groundwater resources management and risk assessment, helping to achieve sustainable groundwater use.