A spatial analysis for geothermal energy exploration using bivariate predictive modelling

Abstract The development of predictive maps for geothermal resources is fundamental for its exploration across Nigeria. In this study, spatial exploration data consisting of geology, geophysics and remote sensing was initially analysed using the Shannon entropy method to ascertain a correlation to k...

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Autores principales:	Andongma W. Tende, Mohammed D. Aminu, Jiriko N. Gajere
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/95284910e227470bb063ed3165926f81
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id	oai:doaj.org-article:95284910e227470bb063ed3165926f81
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spelling	oai:doaj.org-article:95284910e227470bb063ed3165926f812021-12-02T19:16:15ZA spatial analysis for geothermal energy exploration using bivariate predictive modelling10.1038/s41598-021-99244-62045-2322https://doaj.org/article/95284910e227470bb063ed3165926f812021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99244-6https://doaj.org/toc/2045-2322Abstract The development of predictive maps for geothermal resources is fundamental for its exploration across Nigeria. In this study, spatial exploration data consisting of geology, geophysics and remote sensing was initially analysed using the Shannon entropy method to ascertain a correlation to known geothermal manifestation. The application of statistical index, frequency ratio and weight of evidence modelling was then used for integrating every predictive data for the generation of geothermal favourability maps. The receiver operating/area under curve (ROC/AUC) analysis was then employed to ascertain the prediction accuracy for all models. Basically, all spatial data displayed a significant statistical correlation with geothermal occurrence. The integration of these data suggests a high probability for geothermal manifestation within the central part of the study location. Accuracy assessment for all models using the ROC/AUC analysis suggests a high prediction capability (above 75%) for all models. Highest prediction accuracy was obtained from the frequency ratio (83.3%) followed by the statistical index model (81.3%) then the weight of evidence model (79.6%). Evidence from spatial and predictive analysis suggests geological data integration is highly efficient for geothermal exploration across the middle Benue trough.Andongma W. TendeMohammed D. AminuJiriko N. GajereNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andongma W. Tende Mohammed D. Aminu Jiriko N. Gajere A spatial analysis for geothermal energy exploration using bivariate predictive modelling
description	Abstract The development of predictive maps for geothermal resources is fundamental for its exploration across Nigeria. In this study, spatial exploration data consisting of geology, geophysics and remote sensing was initially analysed using the Shannon entropy method to ascertain a correlation to known geothermal manifestation. The application of statistical index, frequency ratio and weight of evidence modelling was then used for integrating every predictive data for the generation of geothermal favourability maps. The receiver operating/area under curve (ROC/AUC) analysis was then employed to ascertain the prediction accuracy for all models. Basically, all spatial data displayed a significant statistical correlation with geothermal occurrence. The integration of these data suggests a high probability for geothermal manifestation within the central part of the study location. Accuracy assessment for all models using the ROC/AUC analysis suggests a high prediction capability (above 75%) for all models. Highest prediction accuracy was obtained from the frequency ratio (83.3%) followed by the statistical index model (81.3%) then the weight of evidence model (79.6%). Evidence from spatial and predictive analysis suggests geological data integration is highly efficient for geothermal exploration across the middle Benue trough.
format	article
author	Andongma W. Tende Mohammed D. Aminu Jiriko N. Gajere
author_facet	Andongma W. Tende Mohammed D. Aminu Jiriko N. Gajere
author_sort	Andongma W. Tende
title	A spatial analysis for geothermal energy exploration using bivariate predictive modelling
title_short	A spatial analysis for geothermal energy exploration using bivariate predictive modelling
title_full	A spatial analysis for geothermal energy exploration using bivariate predictive modelling
title_fullStr	A spatial analysis for geothermal energy exploration using bivariate predictive modelling
title_full_unstemmed	A spatial analysis for geothermal energy exploration using bivariate predictive modelling
title_sort	spatial analysis for geothermal energy exploration using bivariate predictive modelling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/95284910e227470bb063ed3165926f81
work_keys_str_mv	AT andongmawtende aspatialanalysisforgeothermalenergyexplorationusingbivariatepredictivemodelling AT mohammeddaminu aspatialanalysisforgeothermalenergyexplorationusingbivariatepredictivemodelling AT jirikongajere aspatialanalysisforgeothermalenergyexplorationusingbivariatepredictivemodelling AT andongmawtende spatialanalysisforgeothermalenergyexplorationusingbivariatepredictivemodelling AT mohammeddaminu spatialanalysisforgeothermalenergyexplorationusingbivariatepredictivemodelling AT jirikongajere spatialanalysisforgeothermalenergyexplorationusingbivariatepredictivemodelling
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A spatial analysis for geothermal energy exploration using bivariate predictive modelling

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