The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods

2002 ◽  
Vol 41 (7) ◽  
pp. 765-775 ◽  
Author(s):  
Alberto Pistocchi, Lucia Luzi, Paola Napoli
Keyword(s):  
Expert System ◽  
Predictive Modeling ◽  
Spatial Databases ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
Optimal Exploitation ◽  
Modeling Techniques ◽  
Landslide Hazard Mapping

Landslide Hazard Mapping Using a Poisson Distribution: A Case Study in Penang Island, Malaysia

2014 ◽  
pp. 521-525 ◽  
Author(s):  
Lea Tien Tay ◽  
Habibah Lateh ◽  
Md Kamrul Hossain ◽  
Anton Abdulbasah Kamil
Keyword(s):  
Poisson Distribution ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
Landslide Hazard Mapping

Earthquake-Induced Landslide Hazard Mapping: A Case Study in Lebanon

2016 ◽  
Author(s):  
Grace Abou-Jaoude ◽  
Angela Saade ◽  
Joseph Wartman ◽  
Alex Grant
Keyword(s):  
Landslide Hazard ◽  
Hazard Mapping ◽  
Landslide Hazard Mapping

A Case Study on Landslide Hazard Mapping in Changunarayan VDC, Nepal

2013 ◽  
pp. 551-559 ◽  
Author(s):  
Bikas Chandra Bhattarai ◽  
Ananta Man Singh Pradhan
Keyword(s):  
Landslide Hazard ◽  
Hazard Mapping ◽  
Landslide Hazard Mapping

Research on Non-Landslide Selection Method for Landslide Hazard Mapping

2021 ◽  
Author(s):  
Xia Li ◽  
Jiulong Cheng ◽  
Dehao Yu ◽  
Yangchun Han
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Selection Method ◽  
Landslide Hazard ◽  
River Channel ◽  
Information Value ◽  
Hazard Mapping ◽  
Landslide Prediction ◽  
Conditioning Factors ◽  
Landslide Hazard Mapping

Abstract Most landslide prediction models need to select non-landslides. At present, non-landslides mainly use subjective inference or random selection method, which makes it easy to select non-landslides in high-risk areas. To solve this problem and improve the accuracy of landslide prediction, the method of selecting non-landslide by Information value (IV) is proposed in this study. Firstly, 230 historical landslides and 10 landslide conditioning factors are extracted and interpreted by using Remote Sensing (RS) image, Geographic Information System (GIS) and field survey. Secondly, random, buffer, river channel or slope, and IV methods are used to obtain non-landslides, and the obtained non-landslides are applied to the popular SVM model for landslide hazard mapping (LHM) in western area of Tumen City. The landslide hazard map based on the river channel or slope method is seriously inconsistent with the actual situation of study area, Therefore, the three methods of random, buffer, and IV are verified and compared by accuracy, receiver operating characteristic (ROC) curve and the area under curves (AUC). The results show that the landslide prediction accuracy of the three methods is more than 80%, and the prediction accuracy is high, but the IV is higher. In addition, IV can identify the very high hazard regions with smaller area. Therefore, it is more reasonable to use IV to select non-landslides, and IV method is more practical in landslide prevention and engineering construction. The research results may be useful to provide basic information of landslide hazard for decision makers and planners.

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