Landslides susceptibility mapping using Geographically Weighted Regression with Gaussian distance weighting kernel and comparison with Logistic regression algorithm : a case study in Semirom, Isfahan, Iran
سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 507
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شناسه ملی سند علمی:
ICCACS03_381
تاریخ نمایه سازی: 23 آذر 1397
چکیده مقاله:
Huge economic detriment and victims are the results of Landslide. Since there are no physical sensors that would detect landslides directly, detection of landslides is a substantial challenge. Hence, the purpose of this article is to enhance the prediction results of landslide occurrence in Semirom region in Isfahan Province, Iran using a new methodology. Accordingly, the slope, aspect, lithology, land use, distance from faults, distance from river, distance from main roads, precipitation, plan curvature, profile curvature and the distance from residential regions are considered as the effective factors in landslide occurrence. By means of Geographically Weighted Regression (GWR) using the Gaussian distance weighting kernel with landslide inventory map, the Landslide susceptibility map was created. However, before combining the generated factors mentioned above, their independence among each other has been determined. For this reason, the correlation matrix was calculated and it showed that most of the correlations between factors were very minor, proposing that all landslide factors are adequately independent. Coefficient of Determination for GWR with Gaussian distance weighting kernel was 0.8557. The root mean squared error (RMSE) was 0.0804 and the mean root mean squared error (NRMSE) was 0.4468.
نویسندگان
Hamidreza Yavari
GIS M.Sc. Student at School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran,
Parham Pahlavani
Assistant Professor at School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran,
Behnaz Bigdeli
Assistant Professor at School of Civil Engineering, Shahrood University of Technology, Shahrood, Iran,