A forecasting method for water table depths in areas with power transmission lines based on remote sensing and deep learning models

ZHANG Ke; ZHANG Gengsheng; WANG Ning; WEN Jing; LI Yu; YANG Jun

doi:10.6046/zrzyyg.2021450

2023 Vol. 35, No. 1

Article Contents

Article navigation > Remote Sensing for Natural Resources > 2023 > 35(1): 213-221

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ZHANG Ke, ZHANG Gengsheng, WANG Ning, WEN Jing, LI Yu, YANG Jun. 2023. A forecasting method for water table depths in areas with power transmission lines based on remote sensing and deep learning models. Remote Sensing for Natural Resources, 35(1): 213-221. doi: 10.6046/zrzyyg.2021450

Citation:

ZHANG Ke, ZHANG Gengsheng, WANG Ning, WEN Jing, LI Yu, YANG Jun. 2023. A forecasting method for water table depths in areas with power transmission lines based on remote sensing and deep learning models. Remote Sensing for Natural Resources, 35(1): 213-221. doi: 10.6046/zrzyyg.2021450

A forecasting method for water table depths in areas with power transmission lines based on remote sensing and deep learning models

1. State Grid Electric Power Research Institute, Hefei 230088, China；
2. Anhui NARI Jiyuan Electric Power System Tech Co., Ltd, Hefei 230088, China；
3. University of Science and Technology of China, Hefei 230088, China；
4. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China；
5. State Grid Hangzhou Power Supply Company, Zhejiang Electric Power Co.,Ltd.,Hangzhou 310000, China

Received Date: 20 December 2021

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

Abstract

Areas with power transmission lines have been frequently struck by flood disasters in recent years. Therefore, forecasting the water table depths in these areas is critical to the safety of these areas. This study forecasted the water table depth using remote sensing satellite products and observed meteorological and hydrological data. Based on the meteorological and hydrological data, this study forecast the daily and monthly water table depths using the long short-term memory (LSTM), gated recurrent unit (GRU), long short-term memory-seq2seq (LSTM-S2S), and feedforward neural network (FFNN) models. The results indicate that the LSTM-S2S and FFNN models delivered the best and the worst performances, respectively. Meanwhile, the LSTM, GRU, and LSTM-S2S models performed well in forecasting both daily and monthly water table depths, with their forecasts of daily water table depths having a higher coefficient of determination (R2) and a Nash-Sutcliffe efficiency coefficient (NSE) than those of monthly water table depths. Therefore, the method presented in this study can be used to forecast the future daily and monthly water table depths in areas with power transmission lines.
- deep learning model /
- GRU /
- LSTM-S2S /
- LSTM /
- water table depth

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References

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