Mapping favorable groundwater potential recharge zones using a GIS-based analytical hierarchical process and probability frequency ratio model:A case study from an agro-urban region of Pakistan

In Punjab(Pakistan),the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater resources.Intensive pumping has resulted in a rapid decline in the level of the water table as well as its quality.Better management practices and artificial recharge are needed for the development of sustainable groundwater resources.This study proposes a methodology to delineate favorable groundwater potential recharge zones(FPRI)by integrating maps of groundwater potential recharge index(PRI)with the DRASTIC-based groundwater vulnerability index(VI).In order to evaluate both indexes,different thematic layers corresponding to each index were overlaid in ArcGIS.In the overlay analysis,the weights(for various thematic layers)and rating values(for sub-classes)were allocated based on a review of published literature.Both were then normalized and modified using the analytical hierarchical process(AHP)and a frequency ratio model respectively.After evaluating PRI and FPRI,these maps were validated using the area under the curve(AUC)method.The PRI map indicates that 53%of the area assessed exists in very low to low recharge zones,22%in moderate,and 25%in high to excellent potential recharge zones.The VI map indicates that 38%of the area assessed exists in very low to low vulnerability,33%in moderate,and 29%in high to very high vulnerability zones.The FPRI map shows that the central region of Punjab is moderately-to-highly favorable for recharge due to its low vulnerability and high recharge potential.During the validation process,it was found that the AUC estimated with modified weights and rating values was 79%and 67%,for PRI and VI indexes,respectively.The AUC was less when evaluated using original weights and rating values taken from published literature.Maps of favorable groundwater potential recharge zones are helpful for planning and implementation of wells and hydraulic structures in this region.

Using geospatial technologies to delineate Ground Water Potential Zones(GWPZ)in Mberengwa and Zvishavane District,Zimbabwe

The main objective of the study was to delineate Ground Water Potential Zones(GWPZ)in Mberengwa and Zvishavane districts,Zimbabwe,utilizing geospatial technologies and thematic mapping.Various factors,including geology,soil,rainfall,land use/land cover,drainage density,lineament density,slope,Terrain Ruggedness Index(TRI),and Terrain Wetness Index(TWI),were incorporated as thematic layers.The Multi Influencing Factor(MIF)and Analytical Hierarchical Process(AHP)techniques were employed to assign appropriate weights to these layers based on their relative significance,prioritizing GWPZ mapping.The integration of these weighted layers resulted in the generation of five GWPZ classes:Very high,high,moderate,low,and very low.The MIF method identified 3%of the area as having very high GWPZ,19%as having high GWPZ,40%as having moderate GWPZ,24%as having low GWPZ,and 14%as having very low GWPZ.The AHP method yielded 2%for very high GWPZ,14%for high GWPZ,37%for moderate GWPZ,37%for low GWPZ,and 10%for very low GWPZ.A strong correlation(ρof 0.91)was observed between the MIF results and groundwater yield.The study successfully identified regions with abundant groundwater,providing valuable target areas for groundwater exploitation and highvolume water harvesting initiatives.Accurate identification of these crucial regions is essential for effective decision-making,planning,and management of groundwater resources to alleviate water shortages.

Studies on Multi-Criteria Decision-Making-Based Healthcare Systems Using the Machine Learning

There is a lot of information in healthcare and medical records.However,it is challenging for humans to turn data into information and spot hidden patterns in today’s digitally based culture.Effective decision support technologies can help medical professionals find critical information concealed in voluminous data and support their clinical judgments and in different healthcare management activities.This paper presented an extensive literature survey for healthcare systems using machine learning based on multi-criteria decision-making.Various existing studies are considered for review,and a critical analysis is being done through the reviews study,which can help the researchers to explore other research areas to cater for the need of the field.

Delineation of groundwater potential zones in Wadi Saida Watershed of NW-Algeria using remote sensing,geographic information system-based AHP techniques and geostatistical analysis

Sustainable management of groundwater resources has now become an obligation,especially in arid and semi-arid regions given the socio-economic importance of this resource.The optimization in zoning for groundwater exploitation helps in planning and managing groundwater supply works such as boreholes and wells in the catchment.The objective of this study is to use remote sensing and GIS-based Analytical Hierarchy Process(AHP)techniques to evaluate the groundwater potential of Wadi Saida Watershed.Spatial analysis such as geostatistics was also used to validate results and ensure more accuracy.Through the GIS tools and remote sensing technique,earth observation data were converted into thematic layers such as lineament density,geology,drainage density,slope,land use and rainfall,which were combined to delineate groundwater potential zones.Based on their respective impact on groundwater potential,the AHP approach was adopted to assign weights on multi-influencing factors.These results will enable decision-makers to optimize hydrogeological exploration in large-scale catchment areas and map areas.According to the results,the southern part of the Wadi Saida Watershed is characterized as a higher groundwater potential area,where 32%of the total surface area falls in the excellent and good class of groundwater potential.The validation process revealed a 71%agreement between the estimated and actual yield of the existing boreholes in the study area.

An Integrated Framework for Cloud Service Selection Based on BOM and TOPSIS

Many businesses have experienced difficulties in selecting a cloud service provider(CSP)due to the rapid advancement of cloud computing services and the proliferation of CSPs.Many independent criteria should be considered when evaluating the services provided by different CSPs.It is a case of multi-criteria decision-making(MCDM).This paper presents an integrated MCDM cloud service selection framework for determining the most appropriate service provider based on the best only method(BOM)and technique for order of preference by similarity to ideal solution(TOPSIS).To obtain the weights of criteria and the relative importance of CSPs based on each criterion,BOM performs pairwise comparisons of criteria and also for alternatives on each criterion,and TOPSIS uses these weights to rank cloud alternatives.An evaluation and validation of the proposed framework have been carried out through a use-case model to prove its efficiency and accuracy.Moreover,the developed framework was compared with the analytical hierarchical process(AHP),a popular MCDM approach,based on two perspectives:efficiency and consistency.According to the research results,the proposed framework only requires 25%of the comparisons needed for the AHP approach.Furthermore,the proposed framework has a CR of 0%,whereas AHP has 38%.Thus,the proposed framework performs better than AHPwhen it comes to computation complexity and consistency,implying that it is more efficient and trustworthy.