Modeling groundwater probability index in Ponnaiyar River basin of South India using analytic hierarchy process

Abstract

In the present study, an effort has been made to investigate the analytical hierarchy process has been applied to delineate groundwater potential based on integrated geographic information system (GIS) and remote sensing (RS) techniques in Ponnaiyar River basin, Tamil Nadu, India. At first, the climatic factor, topographic factors, water related factors, geological factors, hydrogeological factors and other ecological factors such as land use/land cover and soil depth were derived from the spatial geo-database. Secondly, the 74 groundwater data with high potential yield values of C40 m 3 /h were collected and mapped in GIS. Out these, 44 (60 %) cases were randomly selected for models training, and the remaining 31 (40 %) cases were used for the validation purposes. Then, the assigned weights of thematic layers based on expert knowledge were normalized by eigenvector technique of AHP. To prepare the groundwater potential index, the weighted linear combination (WLC) method was applied in GIS. Finally, the receiver operating characteristic (ROC) curve was drawn for groundwater potential map, and the area under curve (AUC) was computed. Results indicated that the rainfall and slope percent factors have taken the highest and lowest weights, respectively. Validation of results showed that the AHP method (AUC = 76.90 %) performed fairly good predication accuracy. Results of this study could be helpful for better management of groundwater resources in the study area and give planners and decision makers an opportunity to prepare appropriate groundwater investment plans for sustainable environment.

Assessment of groundwater potential zones using multi-influencing factor (MIF) and GIS: a case study from Birbhum district, West Bengal

“…The interrelationship among factors classes (Fig. 3) and assigning of ranks to factor's sub-classes was established based on author's expertise as well as literature review (Kaliraj et al 2014;Senthil-Kumar and Shankar 2014;Dinesan et al 2015;Razandi et al 2015;Taheri et al 2015;Taheri et al 2016;Jothibasu and Anbazhagan 2016;Senanayake et al 2016;Thapa et al 2016;Zabihi et al 2016). Factors having major influence were marked as major effect and were assigned a weight of 1.0 whereas, minor influence were marked as a minor effect with a weight of 0.5 as shown in Table 1 (Magesh et al 2012).…”

“…So far, various techniques have been adopted by various researchers such as, frequency ratio (Manap et al 2014;Razandi et al 2015), multi-criteria decision evaluation (MCDE) (Murthy and Mamo 2009;Machiwal and Singh 2015;Jothibasu and Anbazhagan 2016), artificial neural network (ANN) (Lee et al 2012b), random forest model (Naghibi et al 2016;Zabihi et al 2016) logistic regression model (Pourtaghi and Pourghasemi 2014) and analytic hierarchy process (AHP) (Adiat et al 2012). Most of the bivariate and multivariate statistical techniques have their drawbacks in making assumptions prior to investigation and sensitivity towards outlier values (Abrahart et al 2008;Tehrany et al 2013;Umar et al 2014).…”

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

“…The DEM has been used as the elevation dataset ( Figure 3a). The altitudinal fluctuation controls climatic conditions and helps to induce various vegetation types and soil development [33]. The slope data layer has been derived from PALSAR DEM by spatial analysis in the GIS environment ( Figure 3b).…”

Modeling Environmental Susceptibility of Municipal Solid Waste Disposal Sites: A Case Study in São Paulo State, Brazil

“…The AHP was developed by Saaty in the 1970s [76] and consists of an assessment theory through pairwise comparison to help decision makers set priorities and choose the best decision [37] [59]. The AHP in combination with GIS has been widely used in the field of natural resources and environmental management [77], first because the combined approaches are easy to implement using map algebra operations and cartographic models, and second because the approaches are intuitively appealing to decision makers [62]. The comparisons are made using a scale of absolute judgments ranging from one to nine, where one represents equal importance and nine represents the highest importance from one element to another (Table 1).…”