Map Removal Sensitivity Analysis Research Articles

Sensitivity Analysis and GIS Tools for Groundwater Vulnerability Assessment. (Application in the Middle Chellif Plain, Algeria)

Contamination due to rapid urban development, industrialization, and agricultural sources is increasingly threatening the groundwater resources of the plioquaternary phreatic aquifer of the Middle Western Cheliff. An intrinsic vulnerability assessment was carried out based on the DRASTIC method on parameters involved in the vertical transfer of pollution from the soil surface. The vulnerability maps obtained showed a high extension of areas of medium vulnerability (54%). On the other hand, areas of low vulnerability occupied about 36%. The high and very high vulnerability was mainly related to low water depth values (8% and 2%, respectively). The results of a sensitivity analysis using the two tests—the single parameter sensitivity analysis and the map removal sensitivity analysis—show that every parameter is, without fail, required for the computation of the vulnerability index. The validation of the vulnerability map produced by the DRASTIC method confirmed the evolution of this sensitivity which decreases towards the S.W. of the plain with nitrate concentrations between 30 and 120 mg/L. Planners can use the produced risk maps as tools to make a preliminary choice of priority locations for various forms of environmental sustainability.

Detection of groundwater conditioning factors in a hilly environment

Champhai, the rice bowl of Mizoram, is known for wet rice practices. Rapid urbanization and global climate change increased the demand for groundwater. Champhai city, being a hilly township in northeast India, is very difficult to identify the potential groundwater water availability. The present study aimed at groundwater potentiality zonation in Champhai town. For these purposes ten (10) factors from topographic, climatic, hydrological and other conditions were assigned weightage based on the AHP (analytical hierarchy process) technique. The aspect was given maximum weightage as the aspect working as the controlling factors of precipitation intensity, vegetation and soil type, while LULC (land use land cover) was assigned minimum weightage that unscientific human activities reduced infiltration rate. Subsequently, an addition of ten weighted rasters was performed for groundwater potentiality zonation. As a result, it was seen that the maximum part of the study area covered low potentiality zones (31.88%), whereas high and very high potential zone covered 14.73% and 5.08, respectively. A scientific validation technique, namely AUC (area under curve) value of 0.737, denoted a good classified groundwater water potentiality zone. Further, the single-layer sensitivity analysis highlighted that elevation, slope, aspect, infiltration number, distance to streams and rainfall had the maximum effective factors based on their effective weightage. The map removal sensitivity analysis highlighted that TWI (Topographic Wetness Index) and aspect had the maximum influence on the model. However, in a hilly environment like Champhai, the output of the current study will work as a blueprint for planning and decision making.

Groundwater potential mapping in Trans Yamuna Region, Prayagraj, using combination of geospatial technologies and AHP method.

In this study, the combination of Remote Sensing and Geographic Information System (GIS) was utilized to identify the Groundwater Potential Zones (GPZs) of the Trans-Yamuna region. The Groundwater Potential Zones (GPZ) were mapped by integrating drainage density, slope, geology, geomorphology, NDVI, lineament density, rainfall, soil types, land use & land cover, and topographic wetness index maps. For the prediction output to have a non-trivial degree of accuracy, multicollinearity tests were run before integrating the layers. Using the Analytical Hierarchy Process (AHP), groundwater recharge-affecting parameters and classes of each parameter were scored. All thematic layers were integrated using weighted linear combination on a GIS platform to create a groundwater potential zone map. The outcomes of the model indicate that the research region exhibits three distinct groundwater potential zones, namely low (11.928%; 354.884 km2), moderate (76.44%; 2274.4 km2), and high (11.267%; 345.943 km2), in sequential sequence. These categories define the model's output in descending order of how closely it matches the actual conditions. After that, a map removal sensitivity analysis was also executed and found that geology, geomorphology, lineament density and drainage density strongly influence the prediction model for groundwater potential zone identification. The reliability of the results is established by employing a Receiver Operating Characteristic (ROC) curve for evaluation, which demonstrates a prediction accuracy of 81.3%. Authorities responsible for groundwater resource management can use this study's findings to better inform future regulatory initiatives.

Optimization of conditioning factors for groundwater potential zonation using GIS-based single parameter and map removal sensitivity analysis in Alipurduar district of West Bengal, India

Optimization of conditioning factors for groundwater potential zonation using GIS-based single parameter and map removal sensitivity analysis in Alipurduar district of West Bengal, India

Integrating multi-influencing factor techniques and fuzzy methods to identify recommendation domains for out-scaling conservation agriculture in China

Climate-smart agriculture (CSA) is a global development strategy aimed to address the interlinked challenges of food security and climate change. Expanding the implementation of conservation agriculture (CA), a vital component of CSA, is essential for enhancing agricultural and food security resilience while sustainably managing arable land. However, the extensive heterogeneity of biophysical and socioeconomic conditions presents significant complexities in promoting CA adoption. Addressing these challenges, this study carried out a comprehensive theoretical investigation of biophysical and socioeconomic factors influencing CA adoption and performance, integrating stakeholder feedback to create a systematic and robust evaluation index system for assessing CA suitability. By integrating multi-influencing factor techniques and fuzzy logic methods, we spatially identified suitable areas for CA implementation in China, providing valuable insights for land use policy. The reliability of the models was verified through a sensitivity analysis using the map removal sensitivity analysis method and the extended Fourier amplitude sensitivity test. The results indicated that 29.78% of the cultivated land was unsuitable or marginally suitable for CA, while 29.30 and 40.92% were determined to be moderately suitable and suitable zones, respectively. Suitable cultivated land was primarily distributed in the northern arid and semi-arid regions, the Loess Plateau, the Huang-Huai-Hai Plain, and the Northeast China Plain. Conversely, unsuitable, and marginally suitable cultivated land was predominantly located in the Qinghai Tibet Plateau, Middle-lower Yangtze Plain, Sichuan Basin and surrounding areas, the Yunnan-Guizhou Plateau, and Southern China. The topographical index, annual mean precipitation, humidity index, and population density were identified as the most significant factors influencing CA suitability. The CA suitability maps generated in this study will guide the development and extension agents targeting CA to suitable locations with a high potential impact, thereby maximizing the likelihood of adoption and minimizing the risk of failure.

Groundwater potential assessment in the Blue Nile River catchment, Ethiopia, using geospatial and multi-criteria decision-making techniques

Groundwater supplies have been exploited because of global water shortage. Therefore, effective management of water resources is crucial. Identifying potential groundwater regions in arid and mountainous terrains is challenging for many developing nations because of a lack of financial and human resources. An integrated strategy using remote sensing, geographic information systems, and multi-criteria decision analysis of the hierarchical analytical process was used to identify potential zones for groundwater in the Gulufa Watershed, Blue Nile River Basin, Ethiopia, which covers 1700 km2. Nine groundwater-influencing thematic layers were produced from conventional and satellite data, including lineament density, lithology, slope, geomorphology, soil, land use/land cover, drainage density, rainfall, and elevation. Satty scale values for the thematic layers and their classes were determined based on experts' opinions and literature. Thematic maps were integrated based on their weights and rates to produce a potential zone map using ArcGIS weighted overlay spatial function tool. According to the results, the prospect zone map consists of 383 km2 of very high, 865 km2 of high, 350 km2 of moderate, 58 km2 of low, and 0.3 km2 of poor zones. Validation of the potential zone map using existing boreholes yielded a close agreement, demonstrating the method's accuracy. According to the map removal sensitivity analysis results, the potential zone was more sensitive to lithology than other thematic layers. The map created in the research region can be an essential reference for identifying potential locations for additional groundwater resource exploration, planning, and management.

Groundwater potential mapping in Jashore, Bangladesh

The global groundwater crisis is a perplexing issue, and for its resolution, it is of the utmost importance for delineating groundwater potential zones. This research aims to create a precise groundwater potential map for Bangladesh's Jashore district by combining geospatial approach and an analytical hierarchy process. Fourteen parameters, namely, lineament density, drainage density, land use and land cover, slope, curvature, topographic position index, topographic wetness index, rainfall, geology, roughness, fractional impervious surface, topsoil texture, soil permeability, and general soil types, were considered for the study after an extensive literature review. The weights of these parameters were determined using an analytical hierarchy process, and the scores of each sub-parameter were assigned based on published literature. The final groundwater potential map was then generated using the weighted overlay analysis tool in ArcGIS 10.3 and categorized into five classes. The analysis reveals that very high, high, moderate, low, and very low groundwater potential zones cover 3.96 km2 (0.16%), 444.75 km2 (17.72%), 1615.51 km2 (64.37%), 441.79 km2 (17.60%), and 3.59 km2 (0.14%) of the study area, respectively. The map removal sensitivity analysis shows that geology is the most significant element in groundwater potential zoning, followed by land use and land cover (LULC), slope, and topsoil texture as moderately sensitive elements. Since the groundwater potentiality zones of the study region are clearly delineated, this research may be valuable for implementing an appropriate water resource management strategy.

Vulnerability Mapping of Groundwater Resources of Mekelle City and Surroundings, Tigray Region, Ethiopia

The management and monitoring of the quality of water resources in the Mekelle area are challenging, due to both geogenic and anthropogenic impacts. The extent of these impacts and the sources of pollution in this area have not been thoroughly investigated. In this article, a mapping of water resources vulnerability was carried out using the DRASTIC method and a modified DRASTIC vulnerability map was produced. Single-parameter and map-removal sensitivity analyses were performed on the relevant rates and weights. A final DRASTIC vulnerability index, varying from 54 to 140, was divided into four vulnerability classes: low (225.7 km2), medium (302.8 km2), high (307.2 km2), and very high (187.6 km2); the values in the parentheses indicate the corresponding areal coverage of each class. Similarly, a modified DRASTIC vulnerability index, ranging from 91 to 192, was divided into four vulnerability classes: low (166.4 km2), medium (266.8 km2), high (338.0 km2), and very high (252.2 km2) Nitrates were used to validate both models. in which moderate positive correlations (with Pearson’s correlation coefficient, r) of 0.681 and 0.702 were calculated for the DRASTIC and modified DRASTIC indices, respectively. A comparison of the two maps showed that significant sources of pollution are located in areas with high to very high vulnerability. The results of this research work can be used for the protection and monitoring of groundwater resources in the Mekelle area.

GROUND WATER VULNERABILITY ASSESSMENTOF KODOOR RIVER BASIN BY INTEGRATED DRASTIC METHOD

Groundwater vulnerability assessment techniques are useful tools in groundwater management. The purpose of the study is to assess the groundwater vulnerability of Kodoor river basin in Kerala by DRASTIC model and to modify the model to suit the regional scale by incorporating the land use and/ or land cover (LULC) pattern of the area of study in it to produce DRASTICA model. The interdependence of the nitrate concentration data and vulnerability indices from DRASTIC and DRASTICA models were calculated. Correlation coefficient values obtained for DRASTIC and DRASTICA model is 0.21 and 0.38 respectively which shows that the modified DRASTICA model suits the study area better. The study area was grouped into three zones based on the grades of vulnerability. The study also includes the sensitivity analysis of the DRASTICA model. The map removal sensitivity analysis and single parameter sensitivity analysis were carried out to validate the model results.

Appraising groundwater potential zones using geospatial and multi-criteria decision analysis (MCDA) techniques in Andasa-Tul watershed, Upper Blue Nile basin, Ethiopia

An integrated approach of remote sensing, geographic information system and multicriteria decision analysis of analytical hierarchical process (AHP) were applied to delineate groundwater potential (GWP) zones in Andasa-Tul watershed, Upper Blue Nile Basin, Ethiopia with an area of 1872 km2. Nine GWP influencing thematic layers comprising lithology, lineament density, geomorphology, slope, soil, drainage density, land use/land cover, rainfall and depth to groundwater level prepared from conventional and satellite data were used. The thematic layers and classes within them were given scale values based on literature and experts’ decision by using Satty’s AHP. The thematic layers have been integrated via their weights/rates using weighted overlay spatial function tool of ArcGIS to provide GWP map. The result shows that GWP map comprises 250.8 km2 (13.4%) very good, 132.9 km2 (7.1%) good, 441.8 km2 (23.6%) moderate, 662.7 km2 (35.4%) poor and 383.8 km2 (20.5%) very poor zones. Validation of the GWP map with existing boreholes and springs yield shows 83.8% agreement indicating good accuracy of the method. The map removal sensitivity analysis result reveals that GWP is more sensitive to lithology (mean variation index, 1.92%) and less sensitive to geomorphology (mean variation index, 0.59%). Similarly, from the single layer sensitivity analysis, lithology and slope are found to be more effective parameters, whereas rainfall and depth to groundwater level are less effective variables. The generated GWP map in the study region can be used as important guideline for locating GWP areas for further exploration, planning and management of the groundwater resources.

Evaluation of groundwater potential using Dempster-Shafer model and sensitivity analysis of effective factors: A case study of north Khuzestan province

In order to optimize the management of groundwater resources, accurate measurement of groundwater potential is necessary. Application of learning models and remote sensing is very encouraging in reducing the costs of groundwater potential measurement. In this research, Dempster-Shafer learning model and remote sensing data are used to evaluate the groundwater potential in the north of Khuzestan. The study area has a semi-arid climate and is mostly covered by quaternary sediments except for the north, which is mountainous and different geological formations are seen there. For this purpose, 9 parameter layers including: lithology, soil type, slope percent, land use, altitude, TWI index, lineament density, river density and distance from the river as the most effective parameters in groundwater potential are prepared. Additionally, 474 wells in high potential areas have been selected as suitable real data for training and testing of the model. In the training phase, the weights of the layers were calculated using Dempster-Shafer model based on real data of 70% of wells then the groundwater potential map was obtained by combining the weighted layers. To confirm the efficiency of the model and the accuracy of the groundwater potential map, the ROC curve is plotted on the basis of 30% of wells that were not used during the training phase. The results show that the final groundwater potential map is 86% accurate. After verifying the efficiency of the model, sensitivity analysis of effective parameters in predicting groundwater potential is performed using map removal sensitivity analysis method. The results show that all of the considered parameters have a positive effect on groundwater potential prediction accuracy. However, lithology, soil type, and slope percent are the most effective on the accuracy of the model.

Robustness analysis of machine learning classifiers in predicting spatial gully erosion susceptibility with altered training samples

The present research intended to assess the robustness of three popular machine learning models, i.e. random forest (RF), boosted regression tree (BRT) and naïve bayes (NB) in spatial gully erosion susceptibility modelling in Jainti River basin, India. A gully inventory map of 208 gullies was prepared through field survey and Google earth imageries. Following the 70/30 ratio, three randomly sampled groups of altered training and validation gully sets G1, G2 and G3 were prepared for modelling gully erosion susceptibility. Using information gain ratio and multi-collinearity analysis, 14 gully conditioning factors (GCF) were selected. The discrimination ability and reliability of the models were measured through Kappa coefficient, efficiency, receiver operating characteristic curve, root-mean-square-error (RMSE) and mean-absolute-error (MAE). The stability of the machine learning models was estimated by comparing the accuracy statistics and the departure in areal outcomes among intra-model and inter-model. RF model was found as the most consistent. With the highest mean AUC (0.903), efficiency (91.17), Kappa coefficient (0.835) and lowest RMSE (0.192) and MAE (0.081), RF was found to be more consistent when the training and validation data sets were altered. The effectiveness of each input GCFs was determined using map removal sensitivity analysis technique. This study could be supportive in ascertaining model deployment for mapping gully erosion and managing the land resource.

Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran

The uncertainty of flash flood makes them highly difficult to predict through conventional models. The physical hydrologic models of flash flood prediction of any large area is very difficult to compute as it requires lot of data and time. Therefore remote sensing data based models (from statistical to machine learning) have become highly popular due to open data access and lesser prediction times. There is a continuous effort to improve the prediction accuracy of these models through introducing new methods. This study is focused on flash flood modeling through novel hybrid machine learning models, which can improve the prediction accuracy. The hybrid machine learning ensemble approaches that combine the three meta-classifiers (Real AdaBoost, Random Subspace, and MultiBoosting) with J48 (a tree-based algorithm that can be used to evaluate the behavior of the attribute vector for any defined number of instances) were used in the Gorganroud River Basin of Iran to assess flood susceptibility (FS). A total of 426 flood positions as dependent variables and a total of 14 flood conditioning factors (FCFs) as independent variables were used to model the FS. Several threshold-dependent and independent statistical tests were applied to verify the performance and predictive capability of these machine learning models, such as the receiver operating characteristic (ROC) curve of the success rate curve (SRC) and prediction rate curve (PRC), efficiency (E), root-mean square-error (RMSE), and true skill statistics (TSS). The valuation of the FCFs was done using AdaBoost, frequency ratio (FR), and Boosted Regression Tree (BRT) models. In the flooding of the study area, altitude, land use/land cover (LU/LC), distance to stream, normalized differential vegetation index (NDVI), and rainfall played important roles. The Random Subspace J48 (RSJ48) ensemble method with an area under the curve (AUC) of 0.931 (SRC), 0.951 (PRC), E of 0.89, sensitivity of 0.87, and TSS of 0.78, has become the most effective ensemble in predicting the FS. The FR technique also showed good performance and reliability for all models. Map removal sensitivity analysis (MRSA) revealed that the FS maps have the highest sensitivity to elevation. Based on the findings of the validation methods, the FS maps prepared using the machine learning ensemble techniques have high robustness and can be used to advise flood management initiatives in flood-prone areas.

Groundwater potential zone identification using an analytic hierarchy process in Dhaka City, Bangladesh

Water scarcity is increasing throughout the world due to the effects of increasing groundwater abstraction and climatic change. The present study was undertaken to evaluate groundwater potential zones (GP) in Dhaka city using GIS and remote sensing (RS) techniques. A weighted linear combination (WLC) was used in association with GIS techniques to identify GP zones for an area that encompassed the city to 5 km outside of the city boundaries. Declining groundwater levels are a significant issue for the city, and these are influenced by the rate of groundwater abstraction, potential evapotranspiration (PET), rainfall, land use/land cover pattern, surface lithology, drainage pattern, recharge conditions, and geomorphology. Seven thematic layers were used for the GP calculation, where drainage density and infiltration zone maps were modified for the settlement area to reduce the negative influence on the final result. The GP calculation indicated that 188 km2 (24%) had a ‘very low’ groundwater potential; 150 km2 (19%) had a ‘low’ potential; 138 km2 (18%) had a ‘moderate’ potential; 143 km2 (19%) had a ‘high’ potential; and 154 km2 (20%) had a ‘very high’ potential. Surface lithology was found to be the most sensitive parameter. This parameter had standard deviations of 4.18% and 36.39% were found for a map removal sensitivity analysis (MRSA) and a single parameter sensitivity analysis (SPSA), respectively. The validation result shows that 93% area with a ‘high’ groundwater potential zone falls in the area where the groundwater depth is in the range 6–22 m below the land surface.

A GIS-based DRASTIC Model and an Adjusted DRASTIC Model (DRASTICA) for Groundwater Susceptibility Assessment along the China–Pakistan Economic Corridor (CPEC) Route

Land use types and anthropogenic activities represent considerable threats to groundwater pollution. To effectively monitor the groundwater quality, it is vital to measure pollution levels before they become severe. In our research area, located in Gilgit Baltistan in northern Pakistan, groundwater supplies are diminishing due to urban sprawl. In this study, we used a GIS-based DRASTIC model (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, Hydraulic conductivity) to analyze the area’s hydrological attributes to assess the groundwater susceptibility to pollution. Considering the importance of anthropogenic activities, this research primarily utilizes an adjusted DRASTIC model called DRASTICA, which incorporates anthropogenic impact as a parameter in the model. The resulting map, which depicts vulnerability to groundwater contamination, reveals that 19% of the study area is classed as having high vulnerability, 42% has moderate vulnerability, 37% has low vulnerability, and 2% has very low vulnerability to groundwater contamination. The adopted validation process (nitrate parameter of water quality) revealed that the suggested DRASTICA model achieved better results than the established DRASTIC model in a built-up environment. We used the nitrate concentration in groundwater to verify the formulated results, and the single parameter sensitivity analysis and map removal sensitivity analysis to analyze the model sensitivity. The sensitivity analysis indicated that the groundwater vulnerability to pollution is largely influenced by anthropogenic impact and depth to the water table, thereby suggesting that anthropogenic impact must be explicitly tackled in such studies. The groundwater zones exposed to anthropogenic pollution can be better classified with the help of the proposed DRASTICA model, particularly in and around built-up environments. The responsible authorities can use this groundwater contamination data as an early warning sign, so they can take practical actions to avoid extra pressure on this vital resource.

Groundwater potential zone identification at Plio-Pleistocene elevated tract, Bangladesh: AHP-GIS and remote sensing approach

Water is pondered as a strategic resource that has a great ascendancy on human life and socio-economic development. The present study has toted to understand the groundwater potential (GP) zone at the drought-prone Plio-Pleistocene elevated tract in Bangladesh using geographical information system (GIS) and remote sensing (RS) technique. Ten thematic layers have been escorted for the current study that is extracted from different secondary sources. Thematic layers have been co-registered and rectified to the UTM 45 projection and world geodetic system (WGS) 1984 datum. All thematic layers have been assimilated using weighted linear combination (WLC) and GIS-based multi-criteria evaluation for class normalization using Saaty's Analytic Hierarchy Process (AHP). Different classes of each layer are prepared for the determination of weight and ranking to generate the pairwise comparison matrix (PWCM). Thematic layers viz. drainage density (DD), slope (SLO), geomorphology (GM), soil thickness (ST), groundwater table depth (GWTD) have a negative influence, besides lineament density (LD), rainfall (RF), infiltration rate (IR) have a positive influence with increasing class range values for GP. Surface lithology (SL) and land use/land cover (LULC) are reliant on porosity and presence of water bodies for percolation into the aquifer. Map removal sensitivity analysis (MRSA) and the single parameter sensitivity analysis (SPSA) are also carried for assessing the effect after removing a thematic layer and the effective weight of thematic layers on the GP map, respectively. GP map has been classified into five zones i.e. very low (26% area or 511 km2); low (56% area or 1075 km2); moderate (15% area or 282 km2); high (2% area or 38 km2); and very high (1% area or 25 km2). MRSA results show that groundwater table depth is more sensitive with a mean variation index of 8.9%, besides SPSA results show that the effective thematic layer is slope (weight 29.7%).

A GIS-based modified DRASTIC approach for geospatial modeling of groundwater vulnerability and pollution risk mapping in Korba district, Central India

Intensive use of fertilizers in the agricultural lands and a swift-flying of coal and allied industries in Korba district, India in an unprecedented manner has led to groundwater contamination. Accordingly, an integrated modified DRASTIC and risk index model combined with other statistical techniques are applied to evaluate groundwater susceptibility and pollution risk in the region. The ArcGIS based spatial distribution map of the DRASTIC vulnerability index (DVI) reveals that the high (63%) and very high (23.61%) vulnerable zones identified in DVI map are reduced to that of 57.86% and 17.74%, respectively, when compared with pollution risk map. Results of sensitivity analysis, i.e., map removal sensitivity analysis and single parameter sensitivity analysis confirms that amongst seven DRASTIC parameters the net recharge parameter is the most influencing parameter in view of the groundwater contamination. The linear correlation coefficient (r = 0.89) obtained between risk index values and NO3 concentrations alongside nearly 75% of the study area comprises of agricultural lands and forest/tree clad area corresponds to high to very high risk contamination zones reveal the model validation in the light of the influence of anthropogenic contamination factor in these zones. Furthermore, elevated iron concentration also supports the certain influence of geogenic contamination within the study area. In essence, this study can be effectively utilized in the planning and management of precious groundwater resources in high to very high vulnerable and risk zones of the study area, for its overall sustainable development.

Comparative Analysis between Morphometry and Geo-Environmental Factor Based Soil Erosion Risk Assessment Using Weight of Evidence Model: a Study on Jainti River Basin, Eastern India

Assessment of spatial soil erosion risk is a viable effort signifying the needs of conservation measures due to the deterioration of land as well as soil quality degradation at various scales. Among several non-quantitative approaches regarding erosion risk prediction, watershed morphometry and other geo-environmental parameter based assessments were performed largely and separately which showed varied results. In the present work, using 15 morphometric and 13 geo-environmental parameters, spatial soil erosion risk was modelled in order to inspect the performances and consistency of both approaches in predicting Spatial Soil Erosion Risk (SSER). Field site erosion patch inventory (a total of 164 erosion patches), google earth imagery and a probabilistic model, i.e., Weight of Evidence (WoE) enabled the analysis. Training patches (115 patches) were used to model the SSER while validation patches (49 patches) were used to assess the consistency of model output. Both approaches quantify 25.41% and 20.18% of the area to high to very high susceptibility class, separately. The contribution of each factor of both parameter groups in risk predicting was analysed through Map Removal Sensitivity Analysis (MRSA). Further, the results of performance were evaluated through Repetitive Operator Choice (ROC) curve (success rate and prediction rate curves) measuring Area Under Curve (AUC). The success and prediction rate curves show that when considering morphometric parameters, the AUC is 0.775 and 0.729, respectively, whereas in the case of geo-environmental parameters, AUC = 0.892 and 0.878 accordingly. This reveals the better consistency of geo-environmental parameters in context with the spatial erosion risk zoning in the present scenario.

A Customized FuzzyAHP - GIS based DRASTIC-L model for intrinsic groundwater vulnerability assessment of urban and peri urban phreatic aquifer clusters

The groundwater vulnerability analysis addresses the degree of sensitivity of groundwater system towards changes in both intrinsic/inherent and extrinsic factors. With this aim, an integrated FAHP-GIS-based modified DRASTIC (DRASTIC-L) method was followed in order to address the response of various hydro-geological indicators towards attenuation of pollutant to sub-surface hydrological regime in two differently urbanized clusters of Kozhikode district in Kerala, Southern India. Rating for the DRASTIC-L factors was derived with the aid of Fuzzy based Analytical Hierarchical Process (FAHP). Spatial and non-spatial information was integrated into a GIS environment. The resultant spatial data thus generated depicted that about 71% of urban and 22% of peri-urban zones of the study area, which are characterised by porous and permeable vadose media with shallow groundwater table, have a high to very high vulnerability index value. Majority of highly vulnerable urban area extends 0–10 km from the coastal line and has a dense population. Of the total study area, 8% urban and 38.5% of peri-urban area with low index value indicated that the groundwater in the zone is protected from contaminant's leaching due to its inherent hydrogeology. Low porous vadose media and the presence of steep slope terrain favors this low vulnerability. Besides the remaining area shows moderate vulnerability to contamination. Single - parameter and map-removal sensitivity analyses highlighted that vadose zone characteristic has the highest influence on vulnerability assessment. The resultant vulnerability index values validated with composite groundwater quality index data depicted the significant correlation among them. Thus, the groundwater vulnerability index map is an efficient tool for water-sensitive urban development.

GIS-based assessment of groundwater quality for drinking purpose in northern part of Fars province, Marvdasht

With increasing population and freshwater shortages worldwide, it is necessary to protect vital groundwater resources using innovative methods. The main objective of this study is to use a geographic information systems (GIS)-based approach with the Groundwater Quality Index (GWQI) to analyze groundwater quality in Marvdasht located in the semi-arid region of Iran. For this purpose, we used groundwater quality data that were collected in a five-year period (2010–2015). The most influential water quality parameters were determined by performing map removal sensitivity analysis. Mean maps of the groundwater parameters showed that total dissolved solids (TDS), electrical conductivity (EC) and total hardness (TH) were the most important parameters that exceed the maximum permissible limits for drinking water. The groundwater quality of the study area is generally desirable for drinking (GWQI ¼ 71). The GWQI map indicated that groundwater was higher quality in northern regions of the study area. The GWQI also revealed that only 2% of the study area (11 km 2 ) was below the low quality class. According to map removal sensitivity analysis, Mg 2þ , TH and Na þ were identified as the most sensitive water quality parameters. Therefore, these parameters need to be monitored regularly and with increased precision