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Landuse Change Prediction and Its Impact on Surface Run-off Using Fuzzy C-Mean, Markov Chain and Curve Number Methods

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dc.contributor.author Mondal, Arun
dc.contributor.author Khare, Deepak
dc.contributor.author Kundu, Sananda
dc.contributor.author Mishra, P. K.
dc.contributor.author Meena, Pramod K.
dc.date.accessioned 2020-02-14T06:12:10Z
dc.date.available 2020-02-14T06:12:10Z
dc.date.issued 2014
dc.identifier.citation M. Pant et al. (eds.), Proceedings of the Third International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 259, en_US
dc.identifier.uri http://117.252.14.250:8080/jspui/handle/123456789/4095
dc.description.abstract The landuse change has considerable impact on the surface run-off of a catchment. With the changing landuse there is reduction in the initial abstraction which results in increasing run-off. This also has effect on future because of constant change in landuse due to urbanization. The Soil Conservation Service Curve Number (SCS-CN) model was used in the study for calculating run-off in a sub-catchment of Narmada River basin for the years 1990, 2000 and 2011 which was further validated with the observed data from the gauges. Stream flow of future for 2020 and 2030 was estimated by this method to observe the impact of landuse change on run-off. The landuse classification was done by Fuzzy C-Mean algorithm. The future landuse prediction for 2020 and 2030 was performed with the Markov Chain Model with 2011 validation. Future run-off was generated on the basis of changing landuse which shows increasing rate of run-off. en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.subject Landuse change en_US
dc.subject Run-off en_US
dc.subject SCS Curve Number en_US
dc.subject Fuzzy C-Mean en_US
dc.subject Prediction en_US
dc.subject Markov Chain Model en_US
dc.title Landuse Change Prediction and Its Impact on Surface Run-off Using Fuzzy C-Mean, Markov Chain and Curve Number Methods en_US
dc.type Other en_US


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