Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

Accurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stat...

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Autores principales:	Zehua Liang, Yaping Liu, Hongchang Hu, Haoqian Li, Yuqing Ma, Mohd Yawar Ali Khan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	water table depth long short-term memory neural network wavelet transform over-exploitation area feedforward neural network North China Plain Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/e1a5258f86d142f4be6def6e4f4a910b
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spelling	oai:doaj.org-article:e1a5258f86d142f4be6def6e4f4a910b2021-12-03T06:55:58ZCombined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain2296-665X10.3389/fenvs.2021.780434https://doaj.org/article/e1a5258f86d142f4be6def6e4f4a910b2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenvs.2021.780434/fullhttps://doaj.org/toc/2296-665XAccurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stationarity of water table depth data, the performance of NNs in areas of over-exploitation is challenging. Therefore, reducing data noise is an essential step before simulating the water table depth. This research proposed a novel method to model the non-stationary time series data of water table depth through combing the advantages of wavelet analysis and Long Short-Term Memory (LSTM) neural network (NN). A typical groundwater over-exploitation area, Baoding, North China Plain (NCP), was selected as a study area. To reflect the impact of anthropogenic activities, the variables harnessed to develop the model includes temperature, precipitation, evaporation, and some socio-economic data. The results show that decomposing the time series of the water table depth into three sub-temporal components by Meyer wavelets can significantly improve the simulation effect of LSTM on the water table depth. The average NSE (Nash-Sutcliffe efficiency coefficient) value of all the sites increased from 0.432 to 0.819. Additionally, a feedforward neural network (FNN) is used to compare forecasts over 12-months. As expected, wavelet-LSTM outperforms wavelet-FNN. As the prediction time increases, the advantages of wavelet-LSTM become more evident. The wavelet-LSTM is satisfactory for forecasting the water table depth at most in 6 months. Furthermore, the importance of input variables of wavelet-LSTM is analysed by the weights of the model. The results indicate that anthropogenic activities influence the water table depth significantly, especially in the sites close to the Baiyangdian Lake, the largest lake in the North China Plain. This study demonstrates that the wavelet-LSTM model provides an option for water table depth simulation and predicting areas of over-exploitation of groundwater.Zehua LiangYaping LiuYaping LiuHongchang HuHaoqian LiYuqing MaMohd Yawar Ali KhanFrontiers Media S.A.articlewater table depthlong short-term memory neural networkwavelet transformover-exploitation areafeedforward neural networkNorth China PlainEnvironmental sciencesGE1-350ENFrontiers in Environmental Science, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	water table depth long short-term memory neural network wavelet transform over-exploitation area feedforward neural network North China Plain Environmental sciences GE1-350
spellingShingle	water table depth long short-term memory neural network wavelet transform over-exploitation area feedforward neural network North China Plain Environmental sciences GE1-350 Zehua Liang Yaping Liu Yaping Liu Hongchang Hu Haoqian Li Yuqing Ma Mohd Yawar Ali Khan Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain
description	Accurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stationarity of water table depth data, the performance of NNs in areas of over-exploitation is challenging. Therefore, reducing data noise is an essential step before simulating the water table depth. This research proposed a novel method to model the non-stationary time series data of water table depth through combing the advantages of wavelet analysis and Long Short-Term Memory (LSTM) neural network (NN). A typical groundwater over-exploitation area, Baoding, North China Plain (NCP), was selected as a study area. To reflect the impact of anthropogenic activities, the variables harnessed to develop the model includes temperature, precipitation, evaporation, and some socio-economic data. The results show that decomposing the time series of the water table depth into three sub-temporal components by Meyer wavelets can significantly improve the simulation effect of LSTM on the water table depth. The average NSE (Nash-Sutcliffe efficiency coefficient) value of all the sites increased from 0.432 to 0.819. Additionally, a feedforward neural network (FNN) is used to compare forecasts over 12-months. As expected, wavelet-LSTM outperforms wavelet-FNN. As the prediction time increases, the advantages of wavelet-LSTM become more evident. The wavelet-LSTM is satisfactory for forecasting the water table depth at most in 6 months. Furthermore, the importance of input variables of wavelet-LSTM is analysed by the weights of the model. The results indicate that anthropogenic activities influence the water table depth significantly, especially in the sites close to the Baiyangdian Lake, the largest lake in the North China Plain. This study demonstrates that the wavelet-LSTM model provides an option for water table depth simulation and predicting areas of over-exploitation of groundwater.
format	article
author	Zehua Liang Yaping Liu Yaping Liu Hongchang Hu Haoqian Li Yuqing Ma Mohd Yawar Ali Khan
author_facet	Zehua Liang Yaping Liu Yaping Liu Hongchang Hu Haoqian Li Yuqing Ma Mohd Yawar Ali Khan
author_sort	Zehua Liang
title	Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain
title_short	Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain
title_full	Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain
title_fullStr	Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain
title_full_unstemmed	Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain
title_sort	combined wavelet transform with long short-term memory neural network for water table depth prediction in baoding city, north china plain
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e1a5258f86d142f4be6def6e4f4a910b
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Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

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