Delineating groundwater potential zones using geospatial techniques and fuzzy analytical hierarchy process (FAHP) ensemble in the data-scarce region: evidence from the lower Thoubal river watershed of Manipur, India

Rahaman, Md Hibjur; Sajjad, Haroon; Roshani, .; Masroor, Md; Bhuyan, Nirsobha; Rehman, Sufia

doi:10.1007/s12517-022-09946-y

Cited by 17 publications

(4 citation statements)

References 79 publications

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“…Upon introducing AHP into triangular fuzzy numbers and averaging the pairwise comparison matrices, this study uses the defuzzification method proposed by Chang [46] and Tyagi [47] to solve the weights. The details are in Rahaman et al [14]. Here, we only summarize the process of fuzzification and defuzzification for FAHP as follows.…”

Section: Calculation Of Normalized Weights Using the Fahp Methodsmentioning

confidence: 99%

“…A widely used commercial GIS application, ArcGIS Pro 3.2, enables the visualization and analysis of data, allowing users to generate both 2D and 3D maps. This functionality is crucial for comparing potential and actual sites [13,14]. Githinji et al [15] delineate groundwater potential zones using an effective ensemble of FAHP and geospatial technologies.…”

Section: Introductionmentioning

confidence: 99%

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Site Selection for Offshore Wind Power Farms with Natural Disaster Risk Assessment: A Case Study of the Waters off Taiwan’s West Coast

Ning,

Pien,

Liou

et al. 2024

Energies

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This research examines the risk of natural disasters for offshore wind turbines together with their potential wind energy capacity to help the site selection of offshore wind power farms. Through evaluations of expert questionnaires, we use the fuzzy analytic hierarchy process to weight how natural disasters damage the sub-assemblies of an offshore wind turbine, then obtain the natural disaster risk assessment model, and finally utilize ArcGIS Pro 3.2 to map the potential wind farm sites for the waters off Taiwan’s west coast. We identify that typhoons are the most threatening type of disaster to generators, rotor blades, and rotor hubs; earthquakes are the most threatening to towers; and lightning is the most threatening to transformers. For the whole wind turbine, wind is ironically the most threatening natural disaster, followed by lightning, sea waves, and then earthquakes. Lastly, we examine the results by overlapping the offshore wind farms developed and planned in Taiwan, which coincide with locations in relatively low risk and high wind speed areas.

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Section: Calculation Of Normalized Weights Using the Fahp Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Site Selection for Offshore Wind Power Farms with Natural Disaster Risk Assessment: A Case Study of the Waters off Taiwan’s West Coast

Ning,

Pien,

Liou

et al. 2024

Energies

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“…Nine digitized and georeferenced data layers were used as input data in the ArcGIS Pro 2.9 environment, including geology, unsaturated zone thickness, land use, distance from major rivers, precipitation, water quality, soil transmissivity, and slope maps [25,26], all of which are discussed briefly below. The preparation of this input data is the most time-consuming part of the study, and the study's findings are highly dependent on the precision of the input data.…”

Section: Input Datamentioning

confidence: 99%

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

Zaresefat

Derakhshani

Nikpeyman

et al. 2023

Water

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A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could be a strategic priority for countries to recharge groundwater. Recent advances in machine learning (ML) techniques provide valuable tools for producing an AGR site suitability map (AGRSSM). This research developed an ML algorithm to identify the most appropriate location for AGR in Iranshahr, one of the major districts in the East of Iran characterized by severe drought and excessive groundwater consumption. The area’s undue reliance on groundwater resources has resulted in aquifer depletion and socioeconomic problems. Nine digitized and georeferenced data layers have been considered for preparing the AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from the main rivers, precipitation, water quality, and transmissivity of soil. The developed AGRSSM was trained and validated using 1000 randomly selected points across the study area with an accuracy of 97%. By comparing the results of the proposed sites with those of other methods, it was discovered that the artificial intelligence method could accurately determine artificial recharge sites. In summary, this study uses a novel approach to identify optimal AGR sites using machine learning algorithms. Our findings have practical implications for policymakers and water resource managers looking to address the problem of groundwater depletion in Iranshahr and other regions facing similar challenges. Future research in this area could explore the applicability of our approach to other regions and examine the potential economic benefits of using AGR to recharge groundwater.

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“…Multi-criteria decision analysis, such as the Analytical Hierarchical Process (AHP), is a well-known method that analyzes different environmental parameters and involves ranking based on certain criteria to solve a very complex decision-making process . It is the most common method for delineating potential zones of various natural phenomena (Rahaman et al, 2022). The hierarchy of the parameters of SWPC was determined by its interdependence and interplay with other parameters, and the hierarchy was built from the top down.…”

Section: Analytical Hierarchical Process (Ahp) Based Modelingmentioning

confidence: 99%

Identifying Most Suitable Priority Areas for Soil-Water Conservation Using Coupling Mechanism in Guwahati Urban Watershed

Ahmed¹,

Talukdar²,

Naikoo³

et al. 2022

Preprint

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The urban watershed of Guwahati, Assam, has witnessed a rapid urbanization in recent years, making it to one of the most deteriorated cities in terms of environmental issues. Therefore, this work focused on finding the most suitable soil-water conservation areas at a micro-scale using coupling coordination mechanism. Principal component analysis (PCA) was used to create a priority model for sub-watershed basin based on nineteen morphometric parameters. We then quantified the soil erosion using the revised universal soil loss equation (RUSLE) for current and future scenarios based on the representative concentration pathway (RCP) 2.6 and 8.5 models (RCP2.6 and 8.5). In addition, we proposed the fuzzy logic and analytical hierarchy process (AHP) model-based soil-water conservation suitability (SWPC) model for current and future scenarios. Finally, the most suitable soil-water conservation areas for current and future scenarios were identified using coupling coordination degree model (CCD). To the author’s knowledge, this is the first in-depth study that identifies the most suitable conservation areas by analyzing the watershed prioritization, soil erosion, and topographic, hydrologic, land cover, and climatic parameters-based soil-water conservation suitability models. Sub-watersheds comprising Silsako, Bharalu, Deepor Beel, and North Guwahati have been identified as high-priority sub-watersheds. According to the current RUSLE model, soil erosion in the study area varies from 140 to 181.64 tonnes per hectare per year. In contrast, soil erosion would continue to increase in the future as per the RCP8.5 model, which varies from 305 to 332 tonnes per hectare per year. The current SWPC model predicted 46.92 km2 area as high and 54.40 km2 area as very high suitable zones. However, under the RCP 2.6 and RCP 8.5 models, the high and very high SWPC zones will experience extended areas in the future due to increased soil erosion intensity. According to the CCDM results, Bharalu, Deepor Beel, and North-Guwahati sub-watersheds have observed a very high to medium coupling degrees, which are considered the most suitable areas for conservation. The findings of this study will significantly help stakeholders and experts in long-term land-water resource management and effectively address environmental degradation issues in urban watersheds around the world.

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Delineating groundwater potential zones using geospatial techniques and fuzzy analytical hierarchy process (FAHP) ensemble in the data-scarce region: evidence from the lower Thoubal river watershed of Manipur, India

Cited by 17 publications

References 79 publications

Site Selection for Offshore Wind Power Farms with Natural Disaster Risk Assessment: A Case Study of the Waters off Taiwan’s West Coast

Site Selection for Offshore Wind Power Farms with Natural Disaster Risk Assessment: A Case Study of the Waters off Taiwan’s West Coast

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

Identifying Most Suitable Priority Areas for Soil-Water Conservation Using Coupling Mechanism in Guwahati Urban Watershed

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