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A platform for research: civil engineering, architecture and urbanism
Identification of homogenous regions in rain-fed watershed using Kohonen neural networks
Identification of homogenous regions and their ranking is important to formulate appropriate strategies for suitable conservation and management practices within the watershed. In this study, Kohonen neural network (KNN) is employed to classify the micro-watersheds of Kaddam watershed in middle Godavari basin (India) into homogeneous groups. KNN algorithm learns to cluster groups of similar input patterns from a high-dimensional input space in a non-linear fashion onto a low-dimensional layer of neurons. Ten geo-morphological parameters are used for classification of micro-watersheds. An optimal number of groups is chosen based on two cluster validation measures, the Davies–Bouldin Index and the Dunn’s Index. By using the KNN method, 18 micro-watersheds are grouped into five homogenous groups based on selected watershed parameters. The clustering results showed concurrence with general ranking of micro-watersheds. Further, the KNN micro-watershed classification results are validated by comparing with the results of K-means clustering algorithm (KCA). From the comparative analysis, it is observed that both the algorithms give approximately similar kinds of classification of micro-watersheds. The obtained results help in identifying the groups of micro-watersheds that should be given top priority (i.e. those that require immediate conservation measures). The study suggests that identifying homogeneous regions can be helpful for effective planning and management of watersheds, and KNN can be applied effectively for micro-watershed zonation.
Identification of homogenous regions in rain-fed watershed using Kohonen neural networks
Identification of homogenous regions and their ranking is important to formulate appropriate strategies for suitable conservation and management practices within the watershed. In this study, Kohonen neural network (KNN) is employed to classify the micro-watersheds of Kaddam watershed in middle Godavari basin (India) into homogeneous groups. KNN algorithm learns to cluster groups of similar input patterns from a high-dimensional input space in a non-linear fashion onto a low-dimensional layer of neurons. Ten geo-morphological parameters are used for classification of micro-watersheds. An optimal number of groups is chosen based on two cluster validation measures, the Davies–Bouldin Index and the Dunn’s Index. By using the KNN method, 18 micro-watersheds are grouped into five homogenous groups based on selected watershed parameters. The clustering results showed concurrence with general ranking of micro-watersheds. Further, the KNN micro-watershed classification results are validated by comparing with the results of K-means clustering algorithm (KCA). From the comparative analysis, it is observed that both the algorithms give approximately similar kinds of classification of micro-watersheds. The obtained results help in identifying the groups of micro-watersheds that should be given top priority (i.e. those that require immediate conservation measures). The study suggests that identifying homogeneous regions can be helpful for effective planning and management of watersheds, and KNN can be applied effectively for micro-watershed zonation.
Identification of homogenous regions in rain-fed watershed using Kohonen neural networks
Sivasena Reddy, A. (author) / Janga Reddy, M. (author)
ISH Journal of Hydraulic Engineering ; 19 ; 55-66
2013-03-01
12 pages
Article (Journal)
Electronic Resource
English
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