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 |