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The peak runoff model based on Existing Land Use and Masterplan in Sentul City area, Bogor
Suheri A.a,b, Kusmana C.a, Purwanto M.Y.J.a, Setiawan Y.a
a School of IPB University, Bogor, 16144, Indonesia
b School of Life Sciences and Technology, Bandung Institute of Technology, Bandung, 40116, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.This research aimed to create a peak runoff mode based on existing land use (LU) and masterplan in Sentul City area. To determine the peak runoff by rational method, the study uses the formulation as follows: Q = 0.2778.C.I.A, in which Q is the peak runoff, C is the runoff coefficient of area, I is the average rain rate intensity, and A is the area of study. For recognizing the existing LU, the researcher used image analysis SPOT-6 (2017) by supervised classification. It estimated the gamma distribution parameter through the maximum likelihood method by using software QGIS 2.8, SAGA GIS, dan Arc-GIS 10.4.1. According to the analysis, the study result showed the existing LU peak runoff coefficient value and masterplan are 0.40 and 0.61, respectively, in which the difference is 0.21. The peak runoff increase is 25.32 m3/sec or 6,622,560 m3/year as the impact of land-use change.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Gamma distribution,Land-use change,Maximum likelihood methods,Mode-based,Rational methods,Runoff coefficients,Runoff model,Supervised classification[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1755-1315/399/1/012039[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]