Combination of GIS and fuzzy-AHP for delineating groundwater recharge potential zones in the critical Goghat-II block of West Bengal, India

Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer's highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims to enhance groundwater vulnerability assessment (GWVA) to manage aquifer quality effectively. The study focuses on the El Orjane Aquifer in the Moulouya basin, Morocco, which is facing significant degradation due to olive mill wastewater. Groundwater vulnerability maps (GVMs) were generated using the DRASTIC, Pesticide DRASTIC, SINTACS, and SI methods. To assess the effectiveness of the proposed improvements, 24 piezometers were installed to measure nitrate concentrations, a common indicator of groundwater contamination. This study aimed to enhance GWVA by incorporating new layers, such as land use, and adjusting parameter rates based on a comprehensive sensitivity analysis. The results demonstrate a significant increase in Pearson correlation values (PCV) between the produced GVMs and measured nitrate concentrations. For instance, the PCV for the DRASTIC method improved from 0.42 to 0.75 after adding the land use layer and adjusting parameter rates using the Wilcoxon method. These findings offer valuable insights for accurately assessing groundwater vulnerability in areas with similar hazards and hydrological conditions, particularly in semi-arid and arid regions. They contribute to improving groundwater and environmental management practices, ensuring the long-term sustainability of aquifers.

Groundwater spring potential zonation using AHP and fuzzy-AHP in Eastern Himalayan region: Papum Pare district, Arunachal Pradesh, India

In the present study, the groundwater spring potential zone (GSPZ) was identified using an integrated approach of remote sensing (RS) and geographic information system (GIS), analytic hierarchy process (AHP), and fuzzy-AHP based on multicriteria decision-making (MCDM). Thus, ten associated factors with groundwater springs have been considered: slope, drainage density, lineament density, geomorphology, lithology, soil texture, land use and land cover, rainfall, groundwater level, and spring discharge. The analysis output was categorized into low, moderate, and high. The result of the AHP model shows the area under the high potential zone (16.61%), the moderate potential zone (60.42%), and the low potential zone (22.61%). The result of the fuzzy-AHP model shows that the area falls under the high potential zone (30.40%), moderate potential zone (41.29%), and low potential zone (22.61%). The validation results showed fuzzy-AHP with the area under the curve 0.806, which is slightly better than 0.779 of AHP. So, the resulting GSPZ map confirms that the thematic layers used in the study have a significant role in groundwater spring occurrence and distribution. It was recommended that any groundwater spring rejuvenation or protection activities must be implemented in medium to very high potential zones.

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.

Fuzzy AHP point factored inference system for detection of cardiovascular disease

The World health organization (WHO) reported that cardiovascular disease is the leading cause of death worldwide, particularly in developing countries. But while diagnosing cardiovascular disease, medical practitioners might have differences of opinions and faced challenging when there is inadequate information and uncertainty of the problem. Therefore, to resolve ambiguity and vagueness in diagnosing disease, a perfect decision-making model is required to assist medical practitioners in detecting the disease at an early stage. Thus, this study designs a fuzzy analytic hierarchy process (FAHP) point-factored inference system to detect cardiovascular disease. The attributes are selected and classified into sub-attributes and point factor scale using the clinical data, medical practitioners, and literature review. Fuzzy AHP is used in calculating the attribute weights, the strings are generated using the Mamdani fuzzy inference system, and the strength of each set of fuzzy rules is calculated by multiplying the attribute weights with the point factor scale. The string weights determine the output ranges of cardiovascular disease. Moreover, the results are validated using sensitivity analysis, and comparative analysis is performed with AHP techniques. The results show that the proposed method outperforms other methods, which are elucidated by the case study.

Evaluation of Ergonomics-Related Disorders in Online Education Using Fuzzy AHP

The aim of the presented work is to analyze the ergonomics-related disorders in online education using the fuzzy AHP approach. A group dialogue with online education academicians, online education students, biotechnologists, and sedentary computer users has been performed to spot ergonomics-related disorders in online education. Totally eight ergonomics-related disorders in online education have been identified, and the weight of each disorder has been computed with triangle-shaped fuzzy numbers in pairwise comparison. Furthermore, the ergonomics-related disorders in online education are kept in four major categories such as afflictive disorders, specific disorders, psychosocial disorders, and chronic disorders. These four categories of ergonomics-related disorders in online education are evaluated and compared using fuzzy analytical hierarchical process methodology to get ranked in terms of priorities. The results may be instrumental for taking appropriate corrective actions to prevent ergonomics-related disorders.

Using Analytical Hierarchy Process and Multi-Influencing Factors to Map Groundwater Recharge Zones in a Semi-Arid Mediterranean Coastal Aquifer

Mapping groundwater recharge zones (GWRZs) is essential for planning artificial recharge programs to mitigate groundwater decline and saltwater intrusion into coastal aquifers. We applied two multi-criteria decision-making approaches, namely the analytical hierarchy process (AHP) and the multi-influencing factors (MIF), to map GWRZs in the Korba aquifer in northeastern Tunisia. GWRZ results from the AHP indicate that the majority (69%) of the area can be classified as very good and good for groundwater recharge. The MIF results suggest larger (80.7%) very good and good GWRZs. The GWRZ maps improve groundwater balance calculations by providing estimates of recharge-precipitation ratios to quantify percolation. Lithology, land use/cover and slope were the most sensitive parameters followed by geomorphology, lineament density, rainfall, drainage density and soil type. The AHP approach produced relatively more accurate results than the MIF technique based on correlation of the obtained GWRZs with groundwater well discharge data from 20 wells across the study area. The accuracy of the approaches ultimately depends on the classification criteria, mean rating score and weights assigned to the thematic layers. Nonetheless, the GWRZ maps suggest that there is ample opportunity to implement aquifer recharge programs to reduce groundwater stress in the Korba aquifer.

Using machine learning algorithms to map the groundwater recharge potential zones

Groundwater recharge is indispensable for the sustainable management of freshwater resources, especially in the arid regions. Here we address some of the important aspects of groundwater recharge through machine learning algorithms (MLAs). Three MLAs including, SVM, MARS, and RF were validated for higher prediction accuracies in generating groundwater recharge potential maps (GRPMs). Accordingly, soil permeability samples were prepared and are arbitrarily grouped into training (70%) and validation (30%) samples. The GRPMs are generated using sixteen effective factors, such as elevation (denoted using a digital elevation model; DEM), aspect, slope angle, TWI (topographic wetness index), fault density, MRVBF (multiresolution index of valley bottom flatness), rainfall, lithology, land use, drainage density, distance from rivers, distance from faults, annual ETP (evapo-transpiration), minimum temperature, maximum temperature, and rainfall 24-hr. Subsequently, the VI (variables importance) is assessed based on the LASSO algorithm. The GRPMs of three MLAs were validated using the ROC-AUC (receiver operating characteristic-area under curve) and various techniques including true positive rate (TPR), false positive rate (FPR), F-measures, fallout, sensitivity, specificity, true skill statistics (TSS), and corrected classified instances (CCI). Based on the validation, the RF algorithm performed better (AUC = 0.987) than the SVM (AUC = 0.963) and the MARS algorithm (AUC = 0.962). Furthermore, the accuracy of these MLAs are included in excellent class, based on the ROC curve threshold. Our case study shows that the GRPMs are potential guidelines for decision-makers in drafting policies related to the sustainable management of the groundwater resources.