Development of GIS-based fuzzy pattern recognition model (modified DRASTIC model) for groundwater vulnerability to pollution assessment

Abstract

Groundwater is one of the main sources of drinking water in Ranchi district and hence its vulnerability assessment to delineate areas that are more susceptible to contamination is very important. In the present study, GISbased fuzzy pattern recognition model was demonstrated for groundwater vulnerability to pollution assessment. The model considers the seven hydrogeological factors [depth to water table (D), net recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone (I), and hydraulic conductivity (C)] that affect and control the groundwater contamination. The model was applied for groundwater vulnerability assessment in Ranchi district, Jharkhand, India and validated by the observed nitrate concentrations in groundwater in the study area. The performance of the developed model is compared to the standard DRASTIC model. It was observed that GIS-based fuzzy pattern recognition model have better performance than the standard DRASTIC model. Aquifer vulnerability maps produced in the present study can be used for environmental planning and predictive groundwater management. Further, a sensitivity analysis has been performed to evaluate the influence of single parameters on aquifer vulnerability index.

A bibliometric-based evaluation on environmental research in the Arab world

“…Additionally, these problems have featured hugely in scientific publications and warnings (Tolba and Saab 2009). Such problems that have been hugely addressed in the literature include, as an examples, air pollution (Ahmadi et al 2015;Li et al 2016a), climatic changes (Carleton and Hsiang 2016; Deutsch et al 2015), industrial effluents (Padmanaban et al 2016), desertification (Juřička et al 2016;Li et al 2016b), vulnerability of groundwater to external pollutants (Damak et al 2016;Iqbal et al 2014), land degradation (Akhtar-Schuster et al 2016;Kosmas et al 2016) and aquatic environment pollution with organic and inorganic pollutants (Shakeri et al 2015).…”

Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models

“…The results of this study indicate that the GOD method has a stronger correlation with the other two methods, and the three models produced comparable vulnerability maps. Ighbal et al (2014) compared a GISbased fuzzy pattern recognition model (modified DRASTIC model) with a standard DRASTIC model in Ranchi district, Jharkhand, India. The results of this study indicated that GIS-based fuzzy pattern recognition model had better performance than the standard DRAS-TIC model.…”

“…It is the vital local water source for industry, agriculture, as well as wildlife. Groundwater is also the main source of drinking water in arid and semiarid area, and hence its vulnerability assessment in delineate areas that are more susceptible to contamination is very important (Ighbal et al 2014). The groundwater dynamics reflects the response of the groundwater system to external factors such as climate condition, water storage, groundwater consumption, and other human activities (Minville et al 2010;Ghazavi et al 2011Ghazavi et al , 2012.…”

Application of vulnerability modeling techniques in groundwater resources management: a comparative study

“…The susceptibility of groundwater to pollution is a consequence of a finite combination of different factors ranging from the variation in hydrogeological settings and human activities whose togetherness often formed dynamic system (Pathak et al 2014;Pradhan et al 2013). These interrelated factors interact in a manner by which the quality monitoring of groundwater system could be predicted.…”

“…One of the consequences of these limitations particularly the latter factor is such that the final output of DRASTIC index does not reflect the resultant effect of any missing data etc. Sequel to these aforementioned weaknesses of DRASTIC model, several researchers have devised different means of improving the model's performance (Thirumalaivasan et al 2003;Dixon 2005;Antonakos and Lambrakis 2007;Ckakraborty 2007;Pathak et al 2014;Pradhan et al 2013;Mogaji et al 2014;Nobre et al 2007;Chen et al 2013;Singh et al 2015;Nerantzis and Konstantinos 2015;Wang et al 2012;Biswajeet and Pradhan 2104;Boris et al 2016;Kumar et al 2017;Issoufou and Defourny 2016;Sadeghfam et al 2016). However, few of these DRASTIC model enhancement studies have quantitatively evaluated the efficiency of their improved model output versus the conventional DRASTIC model result.…”