Combining AHP with GIS for assessment of irrigation water quality in Çumra irrigation district (Konya), Central Anatolia, Turkey

Abstract

Ç umra irrigation district (Ç ID) is one of the most important agricultural production regions in Central Anatolia. Surface water in the region is limited and agricultural production substantially relies on groundwater irrigation. In this study, analytic hierarchy process (AHP) is combined with geographical information systems (GIS) to assess the irrigation water quality in the aquifers of Ç ID. Nine water quality criteria were classified into four main hazard groups including salinity hazard, infiltration and permeability hazard, specific ion toxicity, and miscellaneous impacts on sensitive crops. A weighting coefficient of each criterion was determined by using GIS-based AHP. Nine thematic maps defining the electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), combined EC-SAR, chloride, boron, nitrate-nitrogen (NO 3 -N), bicarbonate, and pH were generated to develop the suitability map for irrigation water quality. Based on the results of this application, the final irrigation water quality index model was classified into three groups as high, moderate, and low suitability for irrigation purposes. The derived suitability index map for irrigation water quality indicates that the aquifer water in 61.92 % of the study area has high suitability for irrigation water, moderate suitability in 19.20 %, and low suitability in the remaining 18.87 %. The suitability index map for irrigation water quality explicitly shows that most of aquifer waters in Ç ID are highly suitable for irrigation purposes while the groundwater quality deteriorates towards the north-northeastern and the eastern directions.

Land use changes and groundwater quality in Florida

“…This study is based on geographic information system (GIS) analyses of the spatial data. GIS has been widely applied to groundwater contamination studies to determine potential zones of vulnerability to surficial contamination (Stark et al 1999;Davis et al 2002;Capri et al 2009; Sener and Davraz 2013; Jasrotia and Kumar 2014; Edet 2014; Tokatli 2014; Barroso et al 2015;Bozdag 2015), or to create interactive karst databases (see Gao and Zhou (2008) for a review of GIS applications to karst). Regarding the land use impact on groundwater quality, Khan et al (2011Khan et al ( , 2017 discuss two case studies of GIS application to calculate groundwater quality index and to prepare a map of groundwater sustainability in terms of water quality.…”

Classification of Groundwater Suitability for Irrigation in the Ulagalla Tank Cascade Landscape by GIS and the Analytic Hierarchy Process

“…It structures complex problem hierarchically and examines each level of the hierarchy individually. It uses pairwise comparison matrices to compare all possible pairs of criteria and determine which criterion has the highest priority [43]. Criteria are scaled from 1 to 9 (Table 1), where 1 indicates equal importance and 9 indicates highest priority.…”

“…Since each criterion relies on different measured parameters and reflects a different aspect of water quality, they need to be given weights, which must be assigned with great care. As the AHP has proved a powerful decision support tool in this regard [43], we invited 10 experts in irrigation water quality from universities and the Department of Agriculture in Sri Lanka to prioritize these criteria for upland crops commonly grown in the study area in an AHP Excel template (www.scbuk.com/ahp.html). The names of the criteria or requirements were entered in the template and the experts were asked to work through the matrix, comparing criteria in pairs.…”

Dynamic Assessment of Comprehensive Water Quality Considering the Release of Sediment Pollution

“…Conversely, the indicator values of sample 8 are all worse than level 5, and for samples 4, 5, 6 and 7, the indicator values are just the average values of the neighboring levels from level 1 to level 5, respectively; for example, the indicator values of sample 4 are the average values of level 1 and level 2. By reference to the literature, this paper uses different methods to determine the weights of indicators which can be seen in Tables 2-4, of which the judgment matrix of the analytic hierarchy process (AHP) method is referred to in [28][29][30][31] and the procedure of the PCA method can be seen in [32]. As can be seen in Table 2, the first PC explains 95.829% (>85%) of total variance, and the component matrix of first PC is −0.965X1 + 0.960X2 + 0.994X3 + 0.982X4 + 0.982X5 + 0.980X6 + 0.960X7 + 0.995X8.…”