Land suitability modelling for enhancing fishery resource development in Central Himalayas (India) using GIS and multi-criteria evaluation approach

Classification of Areas Suitable for Fish Farming Using Geotechnology and Multi-Criteria Analysis

“…Teixeira et al [41] proposed a multi-criteria decision-making approach to identify priority areas for semi-intensive and extensive aquaculture in former salterns. Nayak et al [40] used GIS to select sites suitable for fish production in the Nainital district of Uttarakhand State in India and incorporated data on water quality, soil, and infrastructure that influenced the suitability of the region.…”

“…Table 4 presents the paired matrix spreadsheet calculations for the criteria, and Table 5 presents the value of the paired matrix for each of the sub-criteria. Some examples of the construction of these matrices may be seen in Rekha et al [39], Rezende et al [24], Bagdanavičiūtė et al [18], and Nayak, et al [40], among others. The eigenvector (ω) is the average of the values in each line of the normalized comparison matrix and results in the score (weights); c The eigenvalue (λmax) results from the product of Matrix (A) multiplied by Matrix (ω) and the result is obtained by dividing each line by the corresponding ω in order to obtain each λ i of a λ estimate vector, and the approximate λmax is the mean of the λ i , resulting in λmax = 4.0192; d The consistency index (CI) is calculated as (λ i max − n)/(n − 1), resulting in a CI of 0.0064; e The random consistency index (RI), suggested by Saaty [10] for four criteria, is 0.90; f The consistency ratio (CR) is CI/RI, being 0.0064/0.90 = 0.007.…”

Land Suitability for Sustainable Aquaculture of Rainbow Trout (Oncorhynchus mykiss) in Molinopampa (Peru) Based on RS, GIS, and AHP

“…The key advantages of MCE techniques are that they are well established for aquaculture zoning, they can combine multiple criteria (many more than those used in this study; [19,20,55]), and may develop models under different hierarchies, priorities, and scenarios [8]. However, the main problem in the analysis of land suitability for aquaculture in ponds dug in the soil, and in general for any activity, lies in the lack of spatial data of the criteria [16].…”

“…Despite this being stated, no such approach was applied, because it depends on the georeferenced locations of the fish farms, and even though SDM such as MaxEnt has an extremely small sample size tolerance [69], it was considered inappropriate to use only the five farms, as it could affect the performance of the model. For further studies, the additional confidence that a combined approach to MCE and SDM would bring would be of vital importance The key advantages of MCE techniques are that they are well established for aquaculture zoning, they can combine multiple criteria (many more than those used in this study; [19,20,55]), and may develop models under different hierarchies, priorities, and scenarios [8]. However, the main problem in the analysis of land suitability for aquaculture in ponds dug in the soil, and in general for any activity, lies in the lack of spatial data of the criteria [16].…”

“…The reclassified thematic maps for each sub-criterion ( Figure 4) were integrated according to the hierarchical group (Figure 3) using the weighted overlay in QGIS. The Easy AHP plugin integrated the raster layers according to Equation (3) [20], depending on the pixel score of the reclassified map (GRIDi) and the weight of importance (WEIGHTi) of the sub-criterion. The integration by the hierarchical group of sub-criteria generated the sub-models of environmental, economic, and social suitability, and the integration of these generated the spatial suitability model.…”