Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey)

By E. Yeşilnacar, T. Topal. 2005

825 citations recorded by the source corpus.

Abstract Landslide susceptibility mapping is one of the most critical issues in Turkey. At present, geotechnical models appear to be useful only in areas of limited extent, because it is difficult to collect geotechnical data with appropriate resolution over larger regions. In addition, many of the physical variables that are necessary for running these models are not usually available, and their acquisition is often very costly. Conversely, statistical approaches are currently pursued to assess landslide hazard over large regions. However, these approaches cannot effectively model complicated landslide hazard problems, since there is a non-linear relationship between nature-based problems and their triggering factors. Most of the statistical methods are distribution-based and cannot handle multisource data that are commonly collected from nature. In this respect, logistic regression and neural networks provide the potential to overcome drawbacks and to satisfy more rigorous landslide susceptibility mapping requirements. In the Hendek region of Turkey, a segment of natural gas pipeline was damaged due to landslide. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map. For this purpose, logistic regression analysis and neural networks are applied to prepare landslide susceptibility map of the problematic segment of the pipeline. At the end, comparative analysis is conducted on the strengths and weaknesses of both techniques. Based on the higher percentages of landslide bodies predicted in very high and high landslide susceptibility zones, and compatibility between field observations and the important factors obtained in the analyses, the result found by neural network is more realistic.

Subfields: Atmospheric Science, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/82d3a72287aa419f5534da015c1890ec994934c5

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Citations in the corpus

Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India (2017, background): “Distance to roads is an important factor for landslide occurrences as excavation of roads creates instability in the surrounding rock mass and ground slope (Yesilnacar and Topal 2005).”
Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran (2015, background): “The most ideal model shows a curve that has the largest AUC; the AUC varying from 0.5 (random prediction, represented by the diagonal straight line) to 1.0 (perfect prediction) (Yesilnacar and Topal 2005; Mohammady et al. 2012).”
A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, methodology): “LR has also been applied widely in landslide studies (Van Den Eeckhaut et al., 2006a; Yesilnacar and Topal, 2005).”
GIS-based logistic regression method for landslide susceptibility mapping in regional scale * (2006, methodology): “Yesilnacar and Topal (2005) used the total number of landslide pixel and randomly selected cells from landslide free areas, with these data divided into six different training datasets with the accuracy percentage being used to determine which datasets will work well in the logistic model.”
Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island (2005, background): “Marquinez et al. 2003; Menendez-Duarte et al. 2003; Rowbotham and Dudycha 1998 ;D ai and Lee 2001, 2002, 2003; Dai et al. 2001 ;M yster et al.1997 ;L ee and Min 2001 ;C hau et al.2004b; Ohlmacher and Davis 2003; Yesilnacar and Topal 2005; Wang and Sassa 2005 ;L ee2004, 2005a, b ;A yalew and Yamagi”
Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) (2009, methodology): “…networks (ANNs), neuro-fuzzy model, etc. (Pistocchi et al., 2002; Lee et al., 2003a, b, 2004; Ercanoglu and Gokceoglu, 2004; Gomez and Kavzoglu, 2005; Yesilnacar and Topal, 2005; etc.) were used to evaluate the landslide susceptibility, because of the limitations mentioned in the above paragraphs.”
Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China) (2016, methodology): “The statistical models, such as the logistic regression, bivariate and multivariate models, have also been applied to landslide susceptibility mapping (Ayalew and Yamagishi 2005; Lee 2005a; Yesilnacar and Topal 2005; Lee and Sambath 2006; Van Den Eeckhaut et al. 2006; Pradhan 2010).”