Volume 21, Issue 3 (Fall 2017)                   jwss 2017, 21(3): 205-218 | Back to browse issues page

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Boali A H, Jafari R, Bashari H. Analyzing the Effect of Groundwater Quality on Desertification using Bayesian Belief Networks in Segzi Desertification Hotspot. jwss 2017; 21 (3) :205-218
URL: http://jstnar.iut.ac.ir/article-1-2839-en.html
1. Dept. of Combat Desertification, Faculty of Natural Resour .Isf. Univ. of Technol., Isfahan, Iran. , Hossien.boali@yahoo.com
Abstract:   (6728 Views)

This paper aimed to assess the severity of desertification in Segzi plain located in the eastern part of Isfahan city, focusing on groundwater quality criteria used in MEDALUS model. Bayesian Belief networks (BBNs) were also used to convert MEDALUS model into a predictive, cause and effects model. Different techniques such as Kriging and IDW were applied to water quality data of 12 groundwater wells to map continuous variations of the CL, SAR, EC, TDS, pH and decline in water table indices in GIS environment. The effects of measured water quality indicators on desertification severity levels were assessed using sensitivity and scenario analysis in BBNs model. According to the results of the MEDALUS, the desertification of the study area was classified as severe class due to its low quality of groundwater. Sensitivity analysis by the both models showed that decline in waater table, water chloride content and electrical conductivity were the most important parameters responsible for desertification in the region from ground water condition standpoint. The determination coefficient between the outputs of the MEDALUS and BBNs models (R2>0.63) indicated that the results of both models were significantly correlated (α=5 %). These results indicate that the application of BBNs model in desertification assessment can appropriately accommodate the uncertainty of desertification methods and can help managers to make better decision for upcoming land management projects.

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Type of Study: Research | Subject: Ggeneral
Received: 2014/12/17 | Accepted: 2016/12/28 | Published: 2017/11/12

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