Accuracy assessment of landslide prediction models

8th International Symposium of the Digital Earth

地球科学;计算机科学

Othman, A.N.^1 ; Mohd, W.M.N.W.^1 ; Noraini, S.^2

Department of Surveying Science and Geomatics, Faculty of Architecture, Planning and Surveying, Universiti Teknologi MARA, Selangor, Shah Alam, 40100, Malaysia^1

Faculty of Engineering and Built Environment, UCSI University, 58000, Cheras, Kuala Lumpur, Malaysia^2

关键词: Accuracy assessment;    Data processing and modeling;    Flow accumulation;    Landslide hazard;    Landslide prediction;    Natural disasters;    Pair-wise comparison;    Parameter combination;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/18/1/012078/pdf
DOI  :  10.1088/1755-1315/18/1/012078

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones.