Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models

Continuous monitoring and accurate spatiotemporal groundwater storage change predictions can help support sustainable development and efficient groundwater resources management. In this study, remote sensing-based data were used to develop two deep learning predictive models, long short term memory...

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Autores principales:	Jae Young Seo, Sang-Il Lee
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Groundwater storage changes deep learning LSTM CNN-LSTM satellite GRACE & GRACE-FO Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f28ca1d49bec49e38867bbdf805fa5a2
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spelling	oai:doaj.org-article:f28ca1d49bec49e38867bbdf805fa5a22021-12-03T00:00:27ZPredicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models2169-353610.1109/ACCESS.2021.3130306https://doaj.org/article/f28ca1d49bec49e38867bbdf805fa5a22021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9625974/https://doaj.org/toc/2169-3536Continuous monitoring and accurate spatiotemporal groundwater storage change predictions can help support sustainable development and efficient groundwater resources management. In this study, remote sensing-based data were used to develop two deep learning predictive models, long short term memory (LSTM), and convolutional neural network-LSTM (CNN-LSTM) models. The input variables were terrestrial water storage anomaly (TWSA) recorded by the Gravity Recovery and Climate Experiment (GRACE) satellites and the GRACE-Follow-On (GRACE-FO) mission, precipitation measured by the Tropical Rainfall Measuring Mission (TRMM) satellite, average temperature and soil moisture obtained from the Global Land Data Assimilation System (GLDAS), and normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) acquired from the Landsat 5 and 8 satellites. A comparison of groundwater storage change predictions by the two deep learning predictive models with in situ measurements of the National Groundwater Monitoring Network (NGMN) in South Korea showed that the CNN-LSTM model was more accurate [root mean square error (RMSE) = 44.92 mm/month, correlation coefficient (<inline-formula> <tex-math notation="LaTeX">$r) =0.70$ </tex-math></inline-formula>, index of agreement (IOA) = 0.81] than the LSTM model [RMSE = 47.44 mm/month, <inline-formula> <tex-math notation="LaTeX">$r =0.62$ </tex-math></inline-formula>, and IOA = 0.77]. Furthermore, a comparison of the performance of the two models for three specific regions showed that the CNN-LSTM model captured spatiotemporal variations in groundwater storage better. Additionally, a comparison of cumulative change trends in groundwater storage with the NDVI, MNDWI, and TWSA data showed that changes in land cover and total water storage affect groundwater storage. These comparison results demonstrate that the use of parameters recorded by multiple satellites as training data for deep learning models can play an important role in the model’s performance.Jae Young SeoSang-Il LeeIEEEarticleGroundwater storage changesdeep learningLSTMCNN-LSTMsatelliteGRACE & GRACE-FOElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157571-157583 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Groundwater storage changes deep learning LSTM CNN-LSTM satellite GRACE & GRACE-FO Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Groundwater storage changes deep learning LSTM CNN-LSTM satellite GRACE & GRACE-FO Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jae Young Seo Sang-Il Lee Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models
description	Continuous monitoring and accurate spatiotemporal groundwater storage change predictions can help support sustainable development and efficient groundwater resources management. In this study, remote sensing-based data were used to develop two deep learning predictive models, long short term memory (LSTM), and convolutional neural network-LSTM (CNN-LSTM) models. The input variables were terrestrial water storage anomaly (TWSA) recorded by the Gravity Recovery and Climate Experiment (GRACE) satellites and the GRACE-Follow-On (GRACE-FO) mission, precipitation measured by the Tropical Rainfall Measuring Mission (TRMM) satellite, average temperature and soil moisture obtained from the Global Land Data Assimilation System (GLDAS), and normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) acquired from the Landsat 5 and 8 satellites. A comparison of groundwater storage change predictions by the two deep learning predictive models with in situ measurements of the National Groundwater Monitoring Network (NGMN) in South Korea showed that the CNN-LSTM model was more accurate [root mean square error (RMSE) = 44.92 mm/month, correlation coefficient (<inline-formula> <tex-math notation="LaTeX">$r) =0.70$ </tex-math></inline-formula>, index of agreement (IOA) = 0.81] than the LSTM model [RMSE = 47.44 mm/month, <inline-formula> <tex-math notation="LaTeX">$r =0.62$ </tex-math></inline-formula>, and IOA = 0.77]. Furthermore, a comparison of the performance of the two models for three specific regions showed that the CNN-LSTM model captured spatiotemporal variations in groundwater storage better. Additionally, a comparison of cumulative change trends in groundwater storage with the NDVI, MNDWI, and TWSA data showed that changes in land cover and total water storage affect groundwater storage. These comparison results demonstrate that the use of parameters recorded by multiple satellites as training data for deep learning models can play an important role in the model’s performance.
format	article
author	Jae Young Seo Sang-Il Lee
author_facet	Jae Young Seo Sang-Il Lee
author_sort	Jae Young Seo
title	Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models
title_short	Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models
title_full	Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models
title_fullStr	Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models
title_full_unstemmed	Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models
title_sort	predicting changes in spatiotemporal groundwater storage through the integration of multi-satellite data and deep learning models
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f28ca1d49bec49e38867bbdf805fa5a2
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_version_	1718374024642297856

Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models

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