GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data

Springs, the primary source of water in the Indian state of Uttarakhand, are disappearing day by day. A report published by United Nations Development Program in 2015 indicates that due to deforestation, and forest fire, the groundwater of the state has been reduced by 50% between 2007 and 2010. As...

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Autores principales:	Dilip Kumar, Rajib Kumar Bhattacharjya
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ann grace grnn groundwater groundwater fluctuation uttarakhand Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/d572554b67ab4c869b31fb0599db0fcc
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spelling	oai:doaj.org-article:d572554b67ab4c869b31fb0599db0fcc2021-11-05T17:46:50ZGRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data1464-71411465-173410.2166/hydro.2021.108https://doaj.org/article/d572554b67ab4c869b31fb0599db0fcc2021-05-01T00:00:00Zhttp://jh.iwaponline.com/content/23/3/567https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Springs, the primary source of water in the Indian state of Uttarakhand, are disappearing day by day. A report published by United Nations Development Program in 2015 indicates that due to deforestation, and forest fire, the groundwater of the state has been reduced by 50% between 2007 and 2010. As such, for taking proper adaptation policies for the state, it is necessary to monitor the state's groundwater fluctuation. Unfortunately, the bore well data are very limited. Thus, we are proposing two general regression neural network (GRNN)-based models for fast estimation of groundwater fluctuation. The first model evaluates and predicts the groundwater fluctuation in the five known bore well data districts of the state, and the second model, which is based on the first model along with a correlation matrix, predicts the groundwater fluctuation in the districts where no bore well data are available. The assessment of the results shows that the proposed GRNN-based model is capable of estimating the groundwater fluctuation both in the areas where bore well data are available and the areas where bore well data are not available. The study shows that there is a sharp decline in the groundwater level in the hilly districts of the state. HIGHLIGHTS A methodology has been proposed to obtain the regional groundwater fluctuation using GRACE data.; The model is also capable of deriving the groundwater fluctuation for the areas where bore well data is not available.; The model uses GRACE data along with rainfall and temperature for the prediction of groundwater fluctuation.;Dilip KumarRajib Kumar BhattacharjyaIWA Publishingarticleanngracegrnngroundwatergroundwater fluctuationuttarakhandInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 3, Pp 567-588 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ann grace grnn groundwater groundwater fluctuation uttarakhand Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	ann grace grnn groundwater groundwater fluctuation uttarakhand Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Dilip Kumar Rajib Kumar Bhattacharjya GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data
description	Springs, the primary source of water in the Indian state of Uttarakhand, are disappearing day by day. A report published by United Nations Development Program in 2015 indicates that due to deforestation, and forest fire, the groundwater of the state has been reduced by 50% between 2007 and 2010. As such, for taking proper adaptation policies for the state, it is necessary to monitor the state's groundwater fluctuation. Unfortunately, the bore well data are very limited. Thus, we are proposing two general regression neural network (GRNN)-based models for fast estimation of groundwater fluctuation. The first model evaluates and predicts the groundwater fluctuation in the five known bore well data districts of the state, and the second model, which is based on the first model along with a correlation matrix, predicts the groundwater fluctuation in the districts where no bore well data are available. The assessment of the results shows that the proposed GRNN-based model is capable of estimating the groundwater fluctuation both in the areas where bore well data are available and the areas where bore well data are not available. The study shows that there is a sharp decline in the groundwater level in the hilly districts of the state. HIGHLIGHTS A methodology has been proposed to obtain the regional groundwater fluctuation using GRACE data.; The model is also capable of deriving the groundwater fluctuation for the areas where bore well data is not available.; The model uses GRACE data along with rainfall and temperature for the prediction of groundwater fluctuation.;
format	article
author	Dilip Kumar Rajib Kumar Bhattacharjya
author_facet	Dilip Kumar Rajib Kumar Bhattacharjya
author_sort	Dilip Kumar
title	GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data
title_short	GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data
title_full	GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data
title_fullStr	GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data
title_full_unstemmed	GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data
title_sort	grnn model for prediction of groundwater fluctuation in the state of uttarakhand of india using grace data under limited bore well data
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/d572554b67ab4c869b31fb0599db0fcc
work_keys_str_mv	AT dilipkumar grnnmodelforpredictionofgroundwaterfluctuationinthestateofuttarakhandofindiausinggracedataunderlimitedborewelldata AT rajibkumarbhattacharjya grnnmodelforpredictionofgroundwaterfluctuationinthestateofuttarakhandofindiausinggracedataunderlimitedborewelldata
_version_	1718444130175025152

GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data

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