Identification of potential dam sites using OLS regression and fuzzy logic approach

Abstract In this paper potential dam sites were identified using remote sensing and GIS. Determinant factors viz., precipitation, slope, flow accumulation, soil texture, land use, and geology were analyzed in the GIS domain. Each factor was reclassified and assigned the suitable fuzzy membership values depending on their influence on the dam site potential. All the fuzzified layers were overlaid using the "Fuzzy Overlay" tool in the GIS platform. Initially, a total of 26 dam sites were proposed. Only seven sites were selected depending on their proximity to nearby dams, settlements, and flow accumulation. The selected dam sites with their flow accumulation, elevation, precipitation, slope, stream order, maximum storage capacity, and the time of concentration were calculated. The determinant factors of suitable dam sites were subjected to the ordinary least squared (OLS) regression to understand the relationship of factors and the potential dam sites. The OLS regression model statistics showed that all factors are positively correlated with potential dam sites except slope (as low slopes are more suitable for dam construction). The OLS regression diagnostics showed that the multiple R squares values and the adjusted R-square values were found to be 0.835894 and 0.872153, respectively. In this study, Koenker’s (BP) statistic was found statistically insignificant ( p > 0.01), proving that the relationship model is consistent. Jarque–Bera statistic was conducted and also found to be statistically insignificant ( p > 0.01) indicating the Gaussian distribution of residuals. This proves that the fuzzy logic approach coupled with OLS regression is a powerful tool in deciphering the potential dam sites and can be applied at a regional and continental scale.