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GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran

GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and... AIMS Geosciences, 3 (1): 91-115 DOI: 10.3934/geosci.2017.1.91 Received date 18 December 2016 Accepted date 02 March 2017 Published date 22 March 2017 http://www.aimspress.com/journal/geosciences Research article GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran 1 2 3 4 Seyed Mohsen Mousavi , Ali Golkarian , Seyed Amir Naghibi , Bahareh Kalantar , and 4,* Biswajeet Pradhan Department of Environmental Science, College of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Iran Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia * Correspondence: E-mail: [email protected] or [email protected] Abstract：This study intends to investigate the performance of boosted regression tree (BRT) and frequency ratio (FR) models in groundwater potential mapping. For this purpose, location of the springs was determined in the western parts of the Mashhad Plain using national reports and field surveys. In addition, thirteen groundwater conditioning factors were prepared and mapped for the modelling process. Those factor maps are: slope degree, slope aspect, altitude, plan http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png AIMS Geosciences Unpaywall

GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran

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Unpaywall
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2471-2132
DOI
10.3934/geosci.2017.1.91
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Abstract

AIMS Geosciences, 3 (1): 91-115 DOI: 10.3934/geosci.2017.1.91 Received date 18 December 2016 Accepted date 02 March 2017 Published date 22 March 2017 http://www.aimspress.com/journal/geosciences Research article GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran 1 2 3 4 Seyed Mohsen Mousavi , Ali Golkarian , Seyed Amir Naghibi , Bahareh Kalantar , and 4,* Biswajeet Pradhan Department of Environmental Science, College of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Iran Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia * Correspondence: E-mail: [email protected] or [email protected] Abstract：This study intends to investigate the performance of boosted regression tree (BRT) and frequency ratio (FR) models in groundwater potential mapping. For this purpose, location of the springs was determined in the western parts of the Mashhad Plain using national reports and field surveys. In addition, thirteen groundwater conditioning factors were prepared and mapped for the modelling process. Those factor maps are: slope degree, slope aspect, altitude, plan

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AIMS Geosciences – Unpaywall

Published: Jan 1, 2017

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