Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models

Abstract Many regions in Iran are currently experience water crisis, largely driven by frequent droughts and expanding agricultural land combined with over abstraction of groundwater. Therefore, it is extremely important to identify potential groundwater recharge (GWR) zones to help in prevent water...

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Autores principales:	Maryam Sadat Jaafarzadeh, Naser Tahmasebipour, Ali Haghizadeh, Hamid Reza Pourghasemi, Hamed Rouhani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/175feb62ba564fcf8bf0eb3d021f3b39
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spelling	oai:doaj.org-article:175feb62ba564fcf8bf0eb3d021f3b392021-12-02T15:53:43ZGroundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models10.1038/s41598-021-85205-62045-2322https://doaj.org/article/175feb62ba564fcf8bf0eb3d021f3b392021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85205-6https://doaj.org/toc/2045-2322Abstract Many regions in Iran are currently experience water crisis, largely driven by frequent droughts and expanding agricultural land combined with over abstraction of groundwater. Therefore, it is extremely important to identify potential groundwater recharge (GWR) zones to help in prevent water scarcity. The key objective of this research is to applying different scenarios for GWR potential mapping by means of a classifier ensemble approach, namely a combination of Maximum Entropy (ME) and Frequency Ratio (FR) models in a semi-arid mountainous, Marboreh Watershed of Iran. To consider the ensemble effect of these models, 15 input layers were generated and used in two models and then the models were combined in seven scenarios. According to marginal response curves (MRCs) and the Jackknife technique, quaternary formations (Qft1 and Qft2) of lithology, sandy-clay-loam (Sa. Cl. L) class of soil, 0–4% class of slope, and agriculture & rangeland classes of land use, offered the highest percolation potential. Results of the FR model showed that the highest weight belonged to Qft1 rocks and Sa. Cl. L textures. Seven scenarios were used for GWR potential maps by different ensembles based on basic mathematical operations. Correctly Classified Instances (CCI), and the AUC indices were applied to validate model predictions. The validation indices showed that scenarios 5 had the best performance. The combination of models by different ensemble scenarios enhances the efficiency of these models. This study serves as a basis for future investigations and provides useful information for prediction of sites with groundwater recharge potential through combination of state-of-the-art statistical and machine learning models. The proposed ensemble model reduced the machine learning and statistical models’ limitations gaps and promoted the accuracy of the model where combining, especially for data-scarce areas. The results of present study can be used for the GWR potential mapping, land use planning, and groundwater development plans.Maryam Sadat JaafarzadehNaser TahmasebipourAli HaghizadehHamid Reza PourghasemiHamed RouhaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maryam Sadat Jaafarzadeh Naser Tahmasebipour Ali Haghizadeh Hamid Reza Pourghasemi Hamed Rouhani Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
description	Abstract Many regions in Iran are currently experience water crisis, largely driven by frequent droughts and expanding agricultural land combined with over abstraction of groundwater. Therefore, it is extremely important to identify potential groundwater recharge (GWR) zones to help in prevent water scarcity. The key objective of this research is to applying different scenarios for GWR potential mapping by means of a classifier ensemble approach, namely a combination of Maximum Entropy (ME) and Frequency Ratio (FR) models in a semi-arid mountainous, Marboreh Watershed of Iran. To consider the ensemble effect of these models, 15 input layers were generated and used in two models and then the models were combined in seven scenarios. According to marginal response curves (MRCs) and the Jackknife technique, quaternary formations (Qft1 and Qft2) of lithology, sandy-clay-loam (Sa. Cl. L) class of soil, 0–4% class of slope, and agriculture & rangeland classes of land use, offered the highest percolation potential. Results of the FR model showed that the highest weight belonged to Qft1 rocks and Sa. Cl. L textures. Seven scenarios were used for GWR potential maps by different ensembles based on basic mathematical operations. Correctly Classified Instances (CCI), and the AUC indices were applied to validate model predictions. The validation indices showed that scenarios 5 had the best performance. The combination of models by different ensemble scenarios enhances the efficiency of these models. This study serves as a basis for future investigations and provides useful information for prediction of sites with groundwater recharge potential through combination of state-of-the-art statistical and machine learning models. The proposed ensemble model reduced the machine learning and statistical models’ limitations gaps and promoted the accuracy of the model where combining, especially for data-scarce areas. The results of present study can be used for the GWR potential mapping, land use planning, and groundwater development plans.
format	article
author	Maryam Sadat Jaafarzadeh Naser Tahmasebipour Ali Haghizadeh Hamid Reza Pourghasemi Hamed Rouhani
author_facet	Maryam Sadat Jaafarzadeh Naser Tahmasebipour Ali Haghizadeh Hamid Reza Pourghasemi Hamed Rouhani
author_sort	Maryam Sadat Jaafarzadeh
title	Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
title_short	Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
title_full	Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
title_fullStr	Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
title_full_unstemmed	Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
title_sort	groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/175feb62ba564fcf8bf0eb3d021f3b39
work_keys_str_mv	AT maryamsadatjaafarzadeh groundwaterrechargepotentialzonationusinganensembleofmachinelearningandbivariatestatisticalmodels AT nasertahmasebipour groundwaterrechargepotentialzonationusinganensembleofmachinelearningandbivariatestatisticalmodels AT alihaghizadeh groundwaterrechargepotentialzonationusinganensembleofmachinelearningandbivariatestatisticalmodels AT hamidrezapourghasemi groundwaterrechargepotentialzonationusinganensembleofmachinelearningandbivariatestatisticalmodels AT hamedrouhani groundwaterrechargepotentialzonationusinganensembleofmachinelearningandbivariatestatisticalmodels
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Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models

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