Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey
By Hakan Ahmet Nefeslioglu, Ebru Akcapinar Sezer, Candan Gokceoglu, A. S. Bozkir, T. Duman. 2010
The main purpose of the present study is to investigate the possible application of decision tree in landslide susceptibility assessment. The study area having a surface area of 174.8 km2 locates at the northern coast of the Sea of Marmara and western part of Istanbul metropolitan area. When applying data mining and extracting decision tree, geological formations, altitude, slope, plan curvature, profile curvature, heat load and stream power index parameters are taken into consideration as landslide conditioning factors. Using the predicted values, the landslide susceptibility map of the study area is produced. The AUC value of the produced landslide susceptibility map has been obtained as 89.6%. According to the results of the AUC evaluation, the produced map has exhibited a good enough performance.
Subfields: Remote Sensing
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Citations in the corpus
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Many applications of these models fall out of the fields of computation and AI (even information technologies), meaning that experts from other fields usually feel comfortable interpreting the outputs of these models [183–185] .”
- An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm (2012, methodology): “When preparing the landslide inventory, to identify landslides, vertical black and white aerial photographs at a medium scale of 1:35,000 from 1955 to 1956 were used (Nefeslioglu et al., 2010).”
- Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS (2012, background): “…Pradhan et al., 2010a), fuzzy logic (Akgun et al., 2011; Ercanoglu and Gokceoglu, 2002, 2004; Kanungo et al., 2008; Pradhan, 2010a), decision tree (Nefeslioglu et al., 2010), and neuro-fuzzy (Kanungo et al., 2006; Oh and Pradhan, 2011; Pradhan et al., 2010b; Sezer et al., 2011; Vahidnia et al.,…”
- Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran (2013, methodology): “…Vahidnia et al. 2010; Sezer et al. 2011; Oh and Pradhan 2011; Bui et al. 2012a), decision tree methods (Akgun et al. 2008; Akgun and Turk 2010; Nefeslioglu et al. 2010; Akgun 2011), spatial multicriteria evaluation (Pourghasemi et al. 2012d), evidential belief function model (Althuwaynee et…”
- An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, methodology): “Each induction method employs a learning algorithm to identify a model that best fits the relationships between the continuous features and target of input data (Nefeslioglu et al. 2010).”
- Landslide susceptibility modelling using different advanced decision trees methods (2018, methodology): “…(Kavzoglu, Sahin, and Colkesen 2014; Pourghasemi et al. 2013; Yilmaz 2010; Dou et al. 2015; Pham et al. 2017a), decision trees (Alkhasawneh et al. 2014; Nefeslioglu et al. 2010), and neuro-fuzzy (Vahidnia et al. 2010) are widely used techniques for landslides susceptibility assessment and mapping.”