Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China

Dynamic monitoring data of groundwater level is an important basis for understanding the current situation of groundwater development and for the utilization and planning of sustainable exploitation. Dynamic monitoring data of groundwater level are typical spatio-temporal sequence data, which have t...

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Autores principales:	Liang He, Manqing Hou, Suozhong Chen, Junru Zhang, Junyi Chen, Hui Qi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	changwu area groundwater level long short-term memory network spatio-temporal prediction model wavelet transform Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/6cc1a961d91d4f27aa4bd711fc704fc2
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spelling	oai:doaj.org-article:6cc1a961d91d4f27aa4bd711fc704fc22021-11-23T18:56:48ZConstruction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China1606-97491607-079810.2166/ws.2021.140https://doaj.org/article/6cc1a961d91d4f27aa4bd711fc704fc22021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3790https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Dynamic monitoring data of groundwater level is an important basis for understanding the current situation of groundwater development and for the utilization and planning of sustainable exploitation. Dynamic monitoring data of groundwater level are typical spatio-temporal sequence data, which have the characteristics of non-linearity and strong spatio-temporal correlation. The trend of dynamic change of groundwater level is the key factor for the optimal allocation of groundwater resources. However, most of the existing groundwater level prediction models are insufficient in considering temporal and spatial factors and their spatio-temporal correlation. Therefore, construction of a space–time prediction model of groundwater level considering space–time factors and improving the prediction accuracy of groundwater level dynamic changes is of considerable theoretical and practical importance for the sustainable development of groundwater resources utilization. Based on the analysis of spatial–temporal characteristics of groundwater level of the pore confined aquifer II of Changwu area in the Yangtze River Delta region of China, the wavelet transform method was used to remove the noise in the original data, and the K-nearest neighbor (KNN) method was used to calculate the water level. The spatial–temporal dataset and the long short-term memory (LSTM) were reconstructed by screening the spatial correlation of the monitoring wells in the study area. A spatio-temporal KNN-LSTM prediction model for groundwater level considering spatio-temporal factors was also constructed. The reliability and accuracy of KNN-LSTM, LSTM, support vector regression (SVR), and autoregressive integrated moving average (ARIMA) model were evaluated by a cross-validation algorithm. Results showed that the prediction accuracy of KNN-LSTM is 20.68%, 46.54%, and 55.34% higher than that of the other single prediction models (LSTM, SVR, and ARIMA, respectively). HIGHLIGHTS A KNN-LSTM spatio-temporal prediction model for groundwater level is proposed.; It is vital to use wavelet transform to denoise the original data before prediction.; KNN-LSTM has better applicability and accuracy than traditional single random models.;Liang HeManqing HouSuozhong ChenJunru ZhangJunyi ChenHui QiIWA Publishingarticlechangwu areagroundwater levellong short-term memory networkspatio-temporal prediction modelwavelet transformWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3790-3809 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	changwu area groundwater level long short-term memory network spatio-temporal prediction model wavelet transform Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	changwu area groundwater level long short-term memory network spatio-temporal prediction model wavelet transform Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Liang He Manqing Hou Suozhong Chen Junru Zhang Junyi Chen Hui Qi Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China
description	Dynamic monitoring data of groundwater level is an important basis for understanding the current situation of groundwater development and for the utilization and planning of sustainable exploitation. Dynamic monitoring data of groundwater level are typical spatio-temporal sequence data, which have the characteristics of non-linearity and strong spatio-temporal correlation. The trend of dynamic change of groundwater level is the key factor for the optimal allocation of groundwater resources. However, most of the existing groundwater level prediction models are insufficient in considering temporal and spatial factors and their spatio-temporal correlation. Therefore, construction of a space–time prediction model of groundwater level considering space–time factors and improving the prediction accuracy of groundwater level dynamic changes is of considerable theoretical and practical importance for the sustainable development of groundwater resources utilization. Based on the analysis of spatial–temporal characteristics of groundwater level of the pore confined aquifer II of Changwu area in the Yangtze River Delta region of China, the wavelet transform method was used to remove the noise in the original data, and the K-nearest neighbor (KNN) method was used to calculate the water level. The spatial–temporal dataset and the long short-term memory (LSTM) were reconstructed by screening the spatial correlation of the monitoring wells in the study area. A spatio-temporal KNN-LSTM prediction model for groundwater level considering spatio-temporal factors was also constructed. The reliability and accuracy of KNN-LSTM, LSTM, support vector regression (SVR), and autoregressive integrated moving average (ARIMA) model were evaluated by a cross-validation algorithm. Results showed that the prediction accuracy of KNN-LSTM is 20.68%, 46.54%, and 55.34% higher than that of the other single prediction models (LSTM, SVR, and ARIMA, respectively). HIGHLIGHTS A KNN-LSTM spatio-temporal prediction model for groundwater level is proposed.; It is vital to use wavelet transform to denoise the original data before prediction.; KNN-LSTM has better applicability and accuracy than traditional single random models.;
format	article
author	Liang He Manqing Hou Suozhong Chen Junru Zhang Junyi Chen Hui Qi
author_facet	Liang He Manqing Hou Suozhong Chen Junru Zhang Junyi Chen Hui Qi
author_sort	Liang He
title	Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China
title_short	Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China
title_full	Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China
title_fullStr	Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China
title_full_unstemmed	Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China
title_sort	construction of a spatio-temporal coupling model for groundwater level prediction: a case study of changwu area, yangtze river delta region of china
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/6cc1a961d91d4f27aa4bd711fc704fc2
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Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China

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