Integration of different influencing factors in GIS to delineate groundwater potential areas using IF and FR techniques: a study of Pravara basin, Maharashtra, India

Abstract

In the present days, remote sensing and geographic information system (GIS) techniques are comprehensive tools for the assessment of water resource, its management and conservation. In this study, remote sensing and GIS techniques are taken into consideration for zonation of different groundwater prospects of Pravara basin. Several contributing factors in which groundwater potential of an area entirely or partially depends such as lithology, geomorphology, slope, soil, lineament density, drainage density, land use and rainfall are assessed individually as well as together for making the different groundwater prospect maps through influencing factor (IF) and frequency ratio (FR) techniques. In ArcGIS software, all these thematic layers are prepared using different satellite imageries and conventional data sets, obtained from different sources. All these layers are transformed into high-resolution raster format and meshed together in GIS environment using IF and FR methods. Groundwater prospect maps are constructed as a result of overlay analysis. Based on the constructed map of groundwater prospect zones, as a result of this study, the study area is divided into five categories of different groundwater potential zones: very high, high, moderate, low and very low. Validation of the resultant maps has shown that frequency ratio technique is having higher accuracy (AUC = 73%) compared to the influencing factors (AUC = 69%). The present study of groundwater zonation provides a simple and less time-consuming technique; also, the results can directly be used for planning and sustainable management in Pravara basin.

Delineation of potential groundwater recharge zones using analytical hierarchy process (AHP)

“…Several researchers globally have used different techniques to identify potential groundwater recharge zones such as frequency ratio (Al-Abadi, Al-Temmeme, & Al-Ghanimy, 2016;Balamurugan et al, 2017Das & Pardeshi, 2018bGuru, Seshan, & Bera, 2017;Ozdemir, 2011;Naghibi et al, 2015a,b;Razandi, Pourghasemi, Neisani, & Rahmati, 2015). Logistic regression model techniques (Ozdemir, 2011;Pourtaghi & Pourghasemi, 2014), random forest model (Golkarian & Rahmati, 2018;Naghibi, Pourghasemi, & Dixon, 2016), decision tree model (Chenini, Mammou, &El May, 2010, Lee andJones-Lee, 1999); artificial neural network (Naghibi, Pourghasemi, & Abbaspour, 2018), and evidential belief function (Nampak, Pradhan, & Manap, 2014).…”

Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea

“…The stream layer extracted from the DEM is the basic map to prepare a drainage map. According to Das and Pardeshi [36], a high drainage density indicates considerable surface runoff and low-groundwater-potential zone. In this study, the drainage density varied from 0.77 to 3.14 km/km 2 , as shown in Figure 3h, and was classified into the following four classes: <0.78, 0.78-1.57, 1.57-2.36, and >3.14 km/km 2 .…”

GIS-Based Groundwater Potentiality Mapping Using AHP and FR Models in Central Antalya, Turkey

“…The 3rd International Electronic Conference on Geosciences, 7 -13 December 2020 other, and the movement mainly depends on the porosity, permeability, transmissibility, and the storage capacity of the rocks [9][10][11][12].…”

“…Several studies have been carried out for groundwater management using various multi-criterial decision making and learning machines algorithms [12,13,[17][18][19]. Diverse studies have been undertaken on groundwater potential mapping using Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and Influencing Factor [4,15,[20][21][22][23][24][25][26].…”