Model Prediksi Kawasan Rawan Bencana Tanah Longsor di Kecamatan Karangkobar

*Izzan Arif Hutomo  -  Magister Pembangunan Wilayah dan Kota, Universitas Diponegoro, Semarang, Jawa Tengah, Indonesia
Maryono Maryono  -  Departemen Perencanaan Wilayah dan Kota, Universitas Diponegoro, Semarang, Jawa Tengah, Indonesia
Received: 29 Dec 2016; Published: 29 Dec 2016.
Open Access

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Abstract
Since 2010, landslides incident always occured in Banjarnegara District and the frequency of that incident has rapidly increase over the years. Finally at the end of 2014, one of the biggest landslide disaster ever in Indonesia happened in this region. This incident was demolished one sub-village and approximately killed 122 people. Based on those explanation, there should be a study to provide an overview and information on the phenomenon of landslides, the causative factors and which areas have the opportunity to occurs the landslides, especially the chances of landslides in residential areas in the future. In achieving these goals,  this study used a mathematical model of logistic regression analysis and application of Geographic Information Systems (GIS) that based on the data of variables cause landslides such as type of soil, the geological structure, topography, land use, precipitation, road and drainage networks. Results of the analysis that has been conducted shows that the District Karangkobar has five levels of the chances of landslides ie, 0-4%, from 4 to 12.7%, from 12.7 to 22%, from 22 to 32.5%, and 32, 5-50%, whereas this percentage is obtained based on predictive models of dua variables that have a significant influence on the occurrence of landslides these are road networks and drainage networks variable. In addition, it is known also that the settlement area that has the largest land area ratio against the highest odds landslides (32.5 to 50%) is a settlement area in the village Jlegong with a land area of 3.33 hectares, or approximately 43% of the total area settlement region.
Keywords: landslide; prediction model; logistic regression; GIS
Funding: jpwk

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Last update: 2021-05-15 04:39:18

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