Classification of microwatersheds based on morphological characteristics

Abstract

Three classification techniques, namely, K-means Cluster Analysis (KCA), Fuzzy Cluster Analysis (FCA), and Kohonen Neural Networks (KNN) were employed to group 25 microwatersheds of Kherthal watershed, Rajasthan into homogeneous groups for formulating the basis for suitable conservation and management practices. Ten parameters, mainly, morphological, namely, drainage density (Dd), bifurcation ratio (Rb), stream frequency (Fu), length of overland flow (Lo), form factor (Rf), shape factor (Bs), elongation ratio (Re), circulatory ratio (Rc), compactness coefficient (Cc) and texture ratio (T) are used for the classification. Optimal number of groups is chosen, based on two cluster validation indices Davies–Bouldin and Dunn’s. Comparative analysis of various clustering techniques revealed that 13 microwatersheds out of 25 are commonly suggested by KCA, FCA and KNN i.e., 52%; 17 microwatersheds out of 25 i.e., 68% are commonly suggested by KCA and FCA whereas these are 16 out of 25 in FCA and KNN (64%) and 15 out of 25 in KNN and CA (60%). It is observed from KNN sensitivity analysis that effect of various number of epochs (1000, 3000, 5000) and learning rates (0.01, 0.1–0.9) on total squared error values is significant even though no fixed trend is observed. Sensitivity analysis studies revealed that microwatersheds have occupied all the groups even though their number in each group is different in case of further increase in the number of groups from 5 to 6, 7 and 8.