Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS

Abstract

The main goal of this study is to investigate the application of the probabilistic-based frequency ratio (FR) model in groundwater potential mapping at Langat basin in Malaysia using geographical information system. So far, the approach of probabilistic frequency ratio model has not yet been used to delineate groundwater potential in Malaysia. Moreover, this study includes the analysis of the spatial relationships between groundwater yield and various hydrological conditioning factors such as elevation, slope, curvature, river, lineament, geology, soil, and land use for this region. Eight groundwater-related factors were collected and extracted from topographic data, geological data, satellite imagery, and published maps. About 68 groundwater data with high potential yield values of ≥11 m3/h were randomly selected using statistical software of SPSS. Then, the groundwater data were randomly split into a training dataset 70 % (48 borehole data) for training the model and the remaining 30 % (20 borehole data) was used for validation purpose. Finally, the frequency ratio coefficients of the hydrological factors were used to generate the groundwater potential map. The validation dataset which was not used during the FR modeling process was used to validate the groundwater potential map using the prediction rate method. The validation results showed that the area under the curve for frequency model is 84.78 %. As far as the performance of the FR approach is concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative groundwater potential. This information could be used by government agencies as well as private sectors as a guide for groundwater exploration and assessment in Malaysia.

Regional gold potential mapping in Kelantan (Malaysia) using probabilistic based models and GIS

“…In the literature, the probability based models of the frequency ratio and weights-of-evidence have been successfully applied in Malaysia for the studies of landslide susceptibility assessment [45,[49][50][51] and groundwater mapping [52].…”

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

“…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).…”

A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

“…As demand for fresh groundwater in the worldwide is increasing, delineation of groundwater spring potential zones become an increasingly important tool for implementing a successful groundwater determination, protection, and management programs. In the last decade, some researchers have employed several statistical models such as frequency ratio (Oh et al 2011;Manap et al 2012;Pourtaghi and Pourghasemi 2014;Davoodi Moghaddam et al 2015;Naghibi et al 2015), weights-of-evidence (Ozdemir 2011a;Pourtaghi and Pourghasemi 2014), logistic regression (Ozdemir 2011a;Pourtaghi and Pourghasemi 2014), index of entropy , artificial neural network (Lee et al 2012), analytical hierarchy process (Rahmati et al 2014;Razandi et al 2015) and evidential belief function (Pourghasemi and Beheshtirad 2014) models in the groundwater potential mapping.…”