MODELLING THE HAZARD OF LANDSLIDES BY USING STATISTICAL METHOD OF LOGISTIC REGRESSION

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ABEDINI MOUSA; SHIRZADI ATAOLLAH; GASEMYAN BAHAR

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Journal: Geography and Development Year:2015 | Volume:12 | Issue:37 Page(s): 85-102

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Title

MODELLING THE HAZARD OF LANDSLIDES BY USING STATISTICAL METHOD OF LOGISTIC REGRESSION

Author(s)

ABEDINI MOUSA | SHIRZADI ATAOLLAH | GASEMYAN BAHAR | Issue Writer Certificate

Keywords

LANDSLIDESQ2

SUSCEPTIBILITY MAP LOGISTIC REGRESSIONQ2

ROC INDEXQ1

SCAI INDEXQ2

BIJARQ2

Abstract

LANDSLIDES and instability slopes are major dangers for human activities which often cause the waste of economic resources and damage to properties and installations. These occur in the natural slopes or in the changed slopes by human. The main objectives of this study are identifying the effective factors on LANDSLIDES occurrence in Kurdistan Province, BIJAR and evaluating the regions prone to landslide to prepare the susceptibility map using the logistic regression. At first, in this study, by using field visits, questionnaires, geological and topographic maps and reviewing the studies, ten effective factors including elevation from sea level, slope degree, slope aspect, geology, distance from the linear elements (fault, road and river), rainfall and land use were employed. After identifying the factors, they were processed using ARC GIS 10 and ILWIS 33 software. Dependent variable is 144 slopes prone to landslide selected across the region as the landslide data (code 1) and 144 slopes stable against landslide were randomly as land slide free data (code 0). With overlay these data on each of the independent variables, the data necessaries were collected for entry into SPSS 18. The results showed that “slope degree” has the most significant role on LANDSLIDES. Then, land-use, slope aspect, fault, distance from the drainage network, elevation from sea level, distance from road and litho logy are next effective factors, respectively. The results of the evaluation showed that logistic regression model with PCPT index equal to 83.4; -2LL index equal to 229.226 and ROC INDEX equal to 98.5 percent and landslide susceptibility map based on SCAI INDEX has high verification in the case study.Therefore, 75.489 % of the area has very low susceptibility, 10.037% with low susceptibility, 3.628% with moderate susceptibility, 4.062% with high susceptibility and 6.784% with very high susceptibility. These results can be used in predicting the occurrence of future LANDSLIDES, decreasing their risks and planning for the land use.

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APA: Copy

ABEDINI, MOUSA, SHIRZADI, ATAOLLAH, & GASEMYAN, BAHAR. (2015). MODELLING THE HAZARD OF LANDSLIDES BY USING STATISTICAL METHOD OF LOGISTIC REGRESSION. GEOGRAPHY AND DEVELOPMENT, 12(37), 85-102. SID. https://sid.ir/paper/77106/en

Vancouver: Copy

ABEDINI MOUSA, SHIRZADI ATAOLLAH, GASEMYAN BAHAR. MODELLING THE HAZARD OF LANDSLIDES BY USING STATISTICAL METHOD OF LOGISTIC REGRESSION. GEOGRAPHY AND DEVELOPMENT[Internet]. 2015;12(37):85-102. Available from: https://sid.ir/paper/77106/en

IEEE: Copy

MOUSA ABEDINI, ATAOLLAH SHIRZADI, and BAHAR GASEMYAN, “MODELLING THE HAZARD OF LANDSLIDES BY USING STATISTICAL METHOD OF LOGISTIC REGRESSION,” GEOGRAPHY AND DEVELOPMENT, vol. 12, no. 37, pp. 85–102, 2015, [Online]. Available: https://sid.ir/paper/77106/en

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