Application of GIS based data driven evidential belief function model to predict groundwater potential zonation
Introduction
Groundwater is one of the most important natural resources worldwide serving as a major source of water to communities, industries and agricultural purposes (Ayazi et al., 2010, Manap et al., 2012, Manap et al., 2013, Neshat et al., 2013, Pradhan, 2009). Groundwater is defined as water in saturated zone (Fitts, 2002) which fills the pore spaces among mineral grains or cracks and fractured rocks in rock mass. Groundwater is usually formed by rain or snow melts which seeps down through the soil into the underlying rocks (Banks et al., 2002, Saraf and Choudhury, 1998).
The traditional approach of groundwater exploration through drilling, geological, hydro-geological, and geophysical methods are costly and time consuming (Sander et al., 1996, Singh and Prakash, 2002). A common method used to prepare groundwater potential maps is mainly based on ground surveys (Ganapuram et al., 2009). Recently, with the popular use of geographic information systems (GISs) and remote sensing (RS) based technologies, groundwater potential mapping has become an easy procedure (Singh and Prakash, 2002). GIS is a powerful tool to handle huge amount of spatial data and can be used in the decision making process in a number of fields such as geology and environmental management. The information about surface features related to groundwater such as landforms, land use, lineaments can be extracted through RS data. Those data can be easily entered to GIS to integrate with other associated tabular data, followed by spatial analysis and visual interpretation (Jha et al., 2007).
In Malaysia, groundwater has been considered as a hot issue especially during prolonged drought periods. The Selangor state faced a long period of drought in 1998 due to El Nino effects. Groundwater, in other states of Malaysia such as Kelantan, Perlis, Terengganu, Pahang, Sarawak and Sabah has been utilized as a main source of water supply (Suratman, 2004). Moreover, it is being exploited by private sectors for commercial production of mineral water. The failure to recognize the vast potential zone is the main reason for underutilization of groundwater resources in Malaysia.
More recently, a lot of studies have been applied using index based models for assessing groundwater potential mapping (Dar et al., 2010, Madrucci et al., 2008, Nag et al., 2012, Prasad et al., 2008). In some studies probabilistic models such as multi-criteria decision analysis (Chenini et al., 2010, Gupta and Srivastava, 2010, Murthy and Mamo, 2009), weights-of-evidence (Corsini et al., 2009, Lee et al., 2012), frequency ratio (FR) (Oh et al., 2011), and analytical hierarchy process (AHP) (Chowdhury et al., 2009, Pradhan, 2009) have been used for groundwater potential mapping. In recent years, some soft computing techniques such as fuzzy logic (Shahid et al., 2002, Ghayoumian et al., 2007), numerical modelling and decision tree (DT) (Chenini and Mammou, 2010) approaches have been applied in groundwater potential mapping. Magesh et al. (2012) carried out weighted overlay analysis using a multi-influencing factors and assigned weights to various groundwater conditioning factors.
In this paper, an EBF model was applied for groundwater potential mapping (Shafer, 1976, Dempster, 2008). The EBF approach has been popularly used in mineral potential mapping (Moon, 1990). Carranza and Hale (2003) proposed a data-driven approach based on the Dempster’s rule of combination using GIS for mineral potential mapping (Carranza and Castro, 2006). Similarly multivariate based logistic regression model (LR) has been applied in groundwater potential mapping (Ozdemir, 2011). LR model is useful to describe the significance and correlation of groundwater occurence to each conditioning factor.
The main aim of the present study is to evaluate the efficiency of the EBF model for groundwater potential mapping. In order to compare the robustness of the proposed EBF model, a well-known LR model was applied to identify the significant groundwater conditioning factors and subsequently the EBF model was re-run to check its efficiency. Through this analysis, the relationships between wells and each conditioning factor will be quantitatively defined. The main difference between this research and the approaches described in the aforementioned publications is that an EBF model is applied and the result is validated for groundwater potential mapping in the Langat basin, Malaysia. The application of EBF in groundwater potential mapping provides originality to this study.
Section snippets
Study area
Langat River catchment is located in the southeast part of Selangor state. It is considered as the most urbanized river basin in Malaysia providing two thirds of the water in the Selangor state. However, with the large-scale physical and economic development in the area, water scarcity and water quality deterioration is emerging in recent years. Bringemeier (2006) reported that the number of water-intensive enterprises (e.g. steel works, pulp and paper industry, power plants) play a vital role
Groundwater occurrence characteristics
The groundwater data such as topography, number of wells, yield and depth were obtained from Department of Mineral and Geosciences (JMG), Malaysia. Groundwater yield is based on actual pumping test of groundwater well e.g. m3/h. Moreover, groundwater potential is based on prediction of the best potential for groundwater extraction in the study area. There are 125 individual groundwater borehole wells in the study area. In the year 2007, JMG had set up their own standard of groundwater potential
Methodology
The groundwater potential zonation mapping consists of four main steps: (1) data collection and spatial database construction for the groundwater related conditioning factors, (2) assessment of groundwater potential using the relationships between wells and groundwater conditioning factors, (3) validation of the results, (4) description and visual interpretation of the results. Fig. 5 illustrates the general methodological flow chart used in this study.
Application of EBF to groundwater potential mapping
Once three mass functions for all input data layers were prepared, Dempster’s rule of combination was applied to obtain four combined mass functions including the belief, disbelief, ignorance, and plausibility functions.
Eq. (12) shows the Dempster–Shafer theory direct mass function.where Tp and express the following:
- •
Tp = class pixels involved by groundwater wells.
- •
= class pixels not involved by groundwater wells.
In this part, L Numbers of multiple spatial data layer
Conclusion
Groundwater is one of the most important natural resources which play an increasingly significant role for water supplies throughout the world. Therefore, detection and prediction of spatial distribution of potential locations for groundwater exploration have become an important topic for private, government, and research institutions worldwide. In this paper, a data driven EBF model was successfully applied to delineate the potential groundwater zones in the Langat basin in Malaysia. The
Acknowledgments
This research was supported by RUGS Grant/05-02-12-2195 RU at the University Putra Malaysia (Vote No. 9376500). The Authors would like to thank Department of Survey and Mapping Malaysia (JUPEM), Minerals and Geosciences Department Malaysia (JMG) and Department of Agriculture for providing various data sets for the research. Thanks to Mahyat Shafapour Tehrany, Mustafa Neamah Jebur and Omar Althuwaynee for their valuable contribution while preparing the manuscript. Thanks to anonymous reviewers
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