Fuzzy logic and exploratory regression-based dam site identification

This study uses Geographic Information System (GIS) and Remote sensing (RS) to identify suitable dam sites. In the GIS domain, determinant elements like geology, land use, slope, precipitation, and soil texture were examined. The impact of each element on the potential of a dam site led to a reclassification and the assignment of appropriate fuzzy membership values. Based on their closeness to neighboring settlements, dams, and flow accumulation, eight sites were chosen. Based on flow accumulation, elevation, precipitation, slope, stream order, and maximum storage capacity, the dam locations that were chosen were computed. Lower Variance Inflation Factor (VIF) values suggested that there were no redundant variables, and the analysis found that each independent variable had a significant robust probability at a 0.05 level. Despite conflicting correlations hypothesized because of non-stationarity or heteroskedasticity, the Koenker's studentized Bruesch-Pagan statistic was determined to be statistically inconsequential. Also statistically insignificant was the Jarque-Bera statistic, which shows a Gaussian distribution of residuals. For land use, precipitation, and fuzzified geology, the corresponding corrected coefficient of correlation (R2) values were 0.58, 0.56, and 0.17. The potential of the chosen dam sites was found to be entirely explained by land use and geology, with rainfall, soil texture, stream order, flow accumulation, slope, and elevation coming in second and third. Validated using spatial autocorrelation and Global Moran's I, the model's Gaussian pattern validates its effectiveness in identifying potential dam sites at regional and continental levels when combined with exploratory regression.