Regional Groundwater Potential Analysis Using Classification and Regression Trees

Bahram Choubin, Omid Rahmati, Freidoon Soleimani, Hossein Alilou, Ehsan Moradi, Nasrin Alamdari

Research output: Chapter in Book/Conference paperChapter

Abstract

Population growth increases the need for food and water, resulting in an increase in water demand around the world. Since groundwater is the main source of consumption in arid and semiarid regions, it is important to understand the groundwater processes in a given watershed. The objective of this study is to use the classification and regression trees (CARTs) algorithm to predict groundwater potential in a semiarid region, Firoozeh watershed, Iran. A total of 11 condition factors, including topographic wetness index, distance to river, slope percent, drainage density, aspect, elevation, land use, lithology, distance from fault, relative slope position (RSP), and topographic position index were employed. Model calibration and validation were conducted based on the random partition in the R software environment. To assess the accuracy of a diagnostic test, relative operating characteristic curve analysis was considered. Sensitivity analysis (SA) was also performed to assess the importance of groundwater conditioning factors. The validation results indicated that the area under the curve value for CART model was 88%. SA showed that the most sensitive factors are RSP, lithology, distance from fault, and distance to river. The findings of the current research can be helpful for decision-makers and managers for sustainable planning, irrigation, and town water supply purposes to achieve water demand goals.
Original languageEnglish
Title of host publicationSpatial Modeling in GIS and R for Earth and Environmental Sciences
EditorsHamid Reza Pourghasemi, Candan Gokceoglu
Place of PublicationNetherlands
PublisherElsevier
Chapter22
Pages485-498
Number of pages798
Edition1
ISBN (Electronic)9780128156957
ISBN (Print)9780128152263
DOIs
Publication statusPublished - 8 Feb 2019

Fingerprint

groundwater
water demand
semiarid region
sensitivity analysis
lithology
watershed
condition factor
arid region
river
conditioning
population growth
water supply
irrigation
drainage
calibration
land use
software
food
analysis
water

Cite this

Choubin, B., Rahmati, O., Soleimani, F., Alilou, H., Moradi, E., & Alamdari, N. (2019). Regional Groundwater Potential Analysis Using Classification and Regression Trees. In H. R. Pourghasemi, & C. Gokceoglu (Eds.), Spatial Modeling in GIS and R for Earth and Environmental Sciences (1 ed., pp. 485-498). Netherlands: Elsevier. https://doi.org/10.1016/B978-0-12-815226-3.00022-3
Choubin, Bahram ; Rahmati, Omid ; Soleimani, Freidoon ; Alilou, Hossein ; Moradi, Ehsan ; Alamdari, Nasrin . / Regional Groundwater Potential Analysis Using Classification and Regression Trees. Spatial Modeling in GIS and R for Earth and Environmental Sciences. editor / Hamid Reza Pourghasemi ; Candan Gokceoglu. 1. ed. Netherlands : Elsevier, 2019. pp. 485-498
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Choubin, B, Rahmati, O, Soleimani, F, Alilou, H, Moradi, E & Alamdari, N 2019, Regional Groundwater Potential Analysis Using Classification and Regression Trees. in HR Pourghasemi & C Gokceoglu (eds), Spatial Modeling in GIS and R for Earth and Environmental Sciences. 1 edn, Elsevier, Netherlands, pp. 485-498. https://doi.org/10.1016/B978-0-12-815226-3.00022-3

Regional Groundwater Potential Analysis Using Classification and Regression Trees. / Choubin, Bahram ; Rahmati, Omid ; Soleimani, Freidoon ; Alilou, Hossein; Moradi, Ehsan ; Alamdari, Nasrin .

Spatial Modeling in GIS and R for Earth and Environmental Sciences. ed. / Hamid Reza Pourghasemi; Candan Gokceoglu. 1. ed. Netherlands : Elsevier, 2019. p. 485-498.

Research output: Chapter in Book/Conference paperChapter

TY - CHAP

T1 - Regional Groundwater Potential Analysis Using Classification and Regression Trees

AU - Choubin, Bahram

AU - Rahmati, Omid

AU - Soleimani, Freidoon

AU - Alilou, Hossein

AU - Moradi, Ehsan

AU - Alamdari, Nasrin

PY - 2019/2/8

Y1 - 2019/2/8

N2 - Population growth increases the need for food and water, resulting in an increase in water demand around the world. Since groundwater is the main source of consumption in arid and semiarid regions, it is important to understand the groundwater processes in a given watershed. The objective of this study is to use the classification and regression trees (CARTs) algorithm to predict groundwater potential in a semiarid region, Firoozeh watershed, Iran. A total of 11 condition factors, including topographic wetness index, distance to river, slope percent, drainage density, aspect, elevation, land use, lithology, distance from fault, relative slope position (RSP), and topographic position index were employed. Model calibration and validation were conducted based on the random partition in the R software environment. To assess the accuracy of a diagnostic test, relative operating characteristic curve analysis was considered. Sensitivity analysis (SA) was also performed to assess the importance of groundwater conditioning factors. The validation results indicated that the area under the curve value for CART model was 88%. SA showed that the most sensitive factors are RSP, lithology, distance from fault, and distance to river. The findings of the current research can be helpful for decision-makers and managers for sustainable planning, irrigation, and town water supply purposes to achieve water demand goals.

AB - Population growth increases the need for food and water, resulting in an increase in water demand around the world. Since groundwater is the main source of consumption in arid and semiarid regions, it is important to understand the groundwater processes in a given watershed. The objective of this study is to use the classification and regression trees (CARTs) algorithm to predict groundwater potential in a semiarid region, Firoozeh watershed, Iran. A total of 11 condition factors, including topographic wetness index, distance to river, slope percent, drainage density, aspect, elevation, land use, lithology, distance from fault, relative slope position (RSP), and topographic position index were employed. Model calibration and validation were conducted based on the random partition in the R software environment. To assess the accuracy of a diagnostic test, relative operating characteristic curve analysis was considered. Sensitivity analysis (SA) was also performed to assess the importance of groundwater conditioning factors. The validation results indicated that the area under the curve value for CART model was 88%. SA showed that the most sensitive factors are RSP, lithology, distance from fault, and distance to river. The findings of the current research can be helpful for decision-makers and managers for sustainable planning, irrigation, and town water supply purposes to achieve water demand goals.

U2 - 10.1016/B978-0-12-815226-3.00022-3

DO - 10.1016/B978-0-12-815226-3.00022-3

M3 - Chapter

SN - 9780128152263

SP - 485

EP - 498

BT - Spatial Modeling in GIS and R for Earth and Environmental Sciences

A2 - Pourghasemi, Hamid Reza

A2 - Gokceoglu, Candan

PB - Elsevier

CY - Netherlands

ER -

Choubin B, Rahmati O, Soleimani F, Alilou H, Moradi E, Alamdari N. Regional Groundwater Potential Analysis Using Classification and Regression Trees. In Pourghasemi HR, Gokceoglu C, editors, Spatial Modeling in GIS and R for Earth and Environmental Sciences. 1 ed. Netherlands: Elsevier. 2019. p. 485-498 https://doi.org/10.1016/B978-0-12-815226-3.00022-3