Abstract
Accurate groundwater level (GWL) prediction is crucial for the management and sustainable utilization of groundwater resources. This study proposes a method, considering spatial–temporal correlation among geographic multi-feature in data, and Self-Organizing Map (SOM)-based clustering technique to identify and partition spatially connectivity among observation wells. Finally, based on the connectivity results, the observation well dataset is determined as inputs to LSTM for GWL prediction. This approach provides a new idea to enhance the accuracy of existing data-driven methods in karst critical zones characterized by significant spatial heterogeneity in GWL. Comparing with prediction models that solely consider internal data correlations, experiments are conducted in the typical highly spatially heterogeneous karst critical zone of Jinan City, Shandong Province, China. The results show a significant improvement in prediction accuracy when considering spatial connectivity between observation wells based on geographical multi-feature spatial–temporal correlation. Confirming that considering the spatial connectivity of observation wells in GWL prediction methods are more accurate, particularly in areas with significant spatial heterogeneity in karst aquifers.
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Data availability
High-resolution DEM were provided by the Chinese government; the authors do not have the right to make these data publicly available on the Internet. The meteorological data and other model simulation data sets used in this study are available from the corresponding author upon reasonable request.
Code availability
The python code for assimilation of this study are available from the corresponding author upon reasonable request.
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Funding
This work was supported by the National Natural Science Foundation of China (No. 41930648), the National Key R&D Program of China (2022YFB3904104 and 2022YFC3803601), the Research program of Jiangsu Hydraulic Research Institute (2020z024), Hydraulic science and technology projects of Jiangsu Province, China (2022015).
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The method was conceived by Fei Guo, and the experiment designed by Fei Guo, Huiting Hu and Zhuo Zhang, and performed by Shilong Li and Songshan Yue. The algorithm was conceived, mented and optimized by Fei Guo, Hong Zhang and Yi Xu. Fei Guo and Huiting Hu took part in writing the paper. Fei Guo and Hong Zhang provided critical review and substantially revised the manuscript. All authors read and approved the final manuscript.
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Appendix 1
Appendix 1
Detailed prediction results for each well, due to data limitations, the experimental durations for the different observation wells vary. The observation well with the most complete data covers the period from 2009 to 2013 (W1, W4, W5, W6, W7, W8, W10, W14, W16, W17, W18, W23, W24, W26, W27, W30, W31). The subsequent period with relatively comprehensive data coverage is from 2009 to 2012 (W2, W3, W11, W12, W13, W15, W22, W25, W29). The time span with the smallest duration is from 2009 to 2011 (W19, W22).
See Fig. 12
GWL prediction results of standard LSTM model and SOM-LSTM model
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Guo, F., Li, S., Zhao, G. et al. A SOM-LSTM combined model for groundwater level prediction in karst critical zone aquifers considering connectivity characteristics. Environ Earth Sci 83, 267 (2024). https://doi.org/10.1007/s12665-024-11567-5
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DOI: https://doi.org/10.1007/s12665-024-11567-5