Delineation of groundwater potential zones using GIS and multi influence factor (MIF) techniques: a study of district Swat, Khyber Pakhtunkhwa, Pakistan
“…Drainage pattern is an important factor that helps in the determination of potentiality of water (Nasir et al 2018 ). The types of drainage and its density describe the surface/subsurface characteristics like runoff, infiltration relief, and permeability.…”
Section: Study Areamentioning
confidence: 99%
“…Rk includes garnet bearing calcareous and graphitic schist, with thin bedded limestone and marble. Tmg is composed of amphibolites, schist, garnet, biotite, schist, and schistose marble, while Pcg contained granite with siliceous schist layer (Nasir et al 2018 ; Searle and Khan 1996 ). Fig.…”
This study evaluates the characteristics of water along the Swat River, Northern Pakistan. For this purpose, water samples (n = 30) were collected and analyzed for physicochemical parameters including heavy metals (HM). The mean concentrations of physicochemical parameters and HM were within the drinking water guideline values set by the World Health Organization (WHO 2011) except 34%, 60%, and 56% of copper (Cu), nickel (Ni), and lead (Pb), respectively. Pollution sources were identified by various multivariate statistical techniques including correlation analysis (CA) and principal component analysis (PCA) indicating different origins both naturally and anthropogenically. Results of the water quality index (WQI) ranged from 13.58 to 209 with an average value of 77 suggesting poor water quality for drinking and domestic purposes. The poor water quality was mainly related to high sodium (alkalinity) and salinity hazards showing > 27% and 20% water samples have poor alkalinity and salinity hazards, respectively. Hazard quotient (HQ) and hazard index (HI) were used to determine the health risk of HM in the study area. For water-related health risk, HQingestion, HQdermal, and HI values were > 1, indicating noncarcinogenic health risk (NCR) posed by these HM to the exposed population.
“…Drainage pattern is an important factor that helps in the determination of potentiality of water (Nasir et al 2018 ). The types of drainage and its density describe the surface/subsurface characteristics like runoff, infiltration relief, and permeability.…”
Section: Study Areamentioning
confidence: 99%
“…Rk includes garnet bearing calcareous and graphitic schist, with thin bedded limestone and marble. Tmg is composed of amphibolites, schist, garnet, biotite, schist, and schistose marble, while Pcg contained granite with siliceous schist layer (Nasir et al 2018 ; Searle and Khan 1996 ). Fig.…”
This study evaluates the characteristics of water along the Swat River, Northern Pakistan. For this purpose, water samples (n = 30) were collected and analyzed for physicochemical parameters including heavy metals (HM). The mean concentrations of physicochemical parameters and HM were within the drinking water guideline values set by the World Health Organization (WHO 2011) except 34%, 60%, and 56% of copper (Cu), nickel (Ni), and lead (Pb), respectively. Pollution sources were identified by various multivariate statistical techniques including correlation analysis (CA) and principal component analysis (PCA) indicating different origins both naturally and anthropogenically. Results of the water quality index (WQI) ranged from 13.58 to 209 with an average value of 77 suggesting poor water quality for drinking and domestic purposes. The poor water quality was mainly related to high sodium (alkalinity) and salinity hazards showing > 27% and 20% water samples have poor alkalinity and salinity hazards, respectively. Hazard quotient (HQ) and hazard index (HI) were used to determine the health risk of HM in the study area. For water-related health risk, HQingestion, HQdermal, and HI values were > 1, indicating noncarcinogenic health risk (NCR) posed by these HM to the exposed population.
“…Around the world, different methods have been used in delineating GWPZs such as integration of remote sensing and GIS with resistivity data [77], influence factor (IF) [47,51,66], statistical methods [78,79], groundwater modeling [80], and analytical hierarchy process (AHP) [63,74,81]. These methods have been proven as reliable and effective, and have been used by many researchers.…”
Rapid population growth has raised the groundwater resources demand for socio-economic development in the Shatt Al-Arab basin. The sustainable management of groundwater resources requires precise quantitative evaluation, which can be achieved by applying scientific principles and modern techniques. An integrated concept has been used in the current study to identify the groundwater potential zones (GWPZs) in the Shatt Al-Arab basin using remote sensing (RS), geographic information system (GIS), and analytic hierarchy process (AHP). For this purpose, nine groundwater occurrence and movement controlling parameters (i.e., lithology, rainfall, geomorphology, slope, drainage density, soil, land use/land cover, distance to river, and lineament density) were prepared and transformed into raster data using ArcGIS software. These nine parameters (thematic layers) were allocated weights proportional to their importance. Furthermore, the hierarchical ranking was conducted using a pairwise comparison matrix of the AHP in order to estimate the final normalized weights of these layers. We used the overlay weighted sum technique to integrate the layers for the creation of the GWPZs map of the study area. The map has been categorized into five zones (viz., very good, good, moderate, poor, and very poor) representing 4, 51, 35, 9, and 1% of the study area, respectively. Finally, for assessing the effectiveness of the model, the GWPZs map was validated using depth to groundwater data for 99 wells distributed over the basin. The validation results confirm that the applied approach provides significantly solid results that can help in perspective planning and sustainable utilization of the groundwater resources in this water-stressed region.
“…Although we opted to use the FR method in this study to demonstrate the noise-resilience horizon, the framework should be applicable for other GWP estimation methods. The choice of the FR (Díaz-Alcaide and Martínez-Santos, 2019) is made in consideration of eliminating as much artificial bias originating from the subjective assessments present in other semi-quantitative methods such as the MIF (Nasir et al, 2018), AHP (Sahoo et al, 2015), and simple additive weight (SAW) (Abrams et al, 2018). Previous GWP studies typically assumed that the input map layers are clean.…”
Section: Discussionmentioning
confidence: 99%
“…The common approaches for determining those values, such as the multi-influence factor (MIF) technique and AHP, use a qualitative system of ranking the importance of factors. However, this ranking system relies on subjective assessment of the importance (Nasir et al, 2018), which can amplify site-specific biases. In the present study, we opted to use the frequency-ratio (FR) technique to determine the weight values based on prior information about the location of existing wells (Díaz-Alcaide and Martínez-Santos, 2019).…”
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