Artificial Neural Networks applied to landslide susceptibility assessment

By L. Ermini, Filippo Catani, N. Casagli. 2005

556 citations recorded by the source corpus.

Subfields: Atmospheric Science, Management, Monitoring, Policy and Law, Soil Science

View Paper: https://www.semanticscholar.org/paper/a1888b0f185b11d9496a1519e2d64c86e80e6c0c

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Citations in the corpus

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS (2012, methodology): “In recent years, some newer approaches for landslide analysis have been carried out such as artificial neural networks (Ercanoglu, 2005; Ermini et al., 2005; Lee et al., 2003, 2004; Nefeslioglu et al., Contents lists available at SciVerse ScienceDirect journal homepage:…”
Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows (2014, unknown): “…(BonhamCarter, 1994; Neuhäuser and Terhorst, 2006) or “certainty factor” (e.g. Binaghi et al., 1998) – and artificial neural networks (e.g.Lee et al., 2003; Ermini et al., 2005; Liu et al., 2006), logistic regression belongs to the most frequently chosen approaches to spatial modelling of…”
Predicting gully initiation: comparing data mining techniques, analytical hierarchy processes and the topographic threshold (2012, background): “The power and usefulness of ANNs have been demonstrated in numerous applications, including speech synthesis, medicine, finance, andmany other pattern-recognition problems, including the geomorphology domain (Shu and Burn, 2004; Ermini et al., 2005; Gomez and Kavzoglu, 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): “…and Lee 2009; Saboya et al. 2006) and artificial neural network models (Biswajeet and Saro 2007; Chauhan et al. 2010; Choi et al. 2012; Conforti et al. 2014; Ermini et al. 2005; Gomez and Kavzoglu 2005; Kawabata and Bandibas 2009; Nefeslioglu et al. 2008; Pradhan and Lee 2010b; Yilmaz 2009).”
Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China) (2016, methodology): “…classifications techniques such as fuzzy systems (Ercanoglu and Gokceoglu 2002, 2004; Kanungo et al. 2006; Pourghasemi et al. 2012a; Pradhan et al. 2010) and neural networks (Lee et al. 2003, 2004; Ermini et al. 2005; Pradhan and Lee 2010) etc., have also been mentioned in the various literature.”
A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland (2008, methodology): “Discussion Recently, novel modelling techniques based on different mathematical algorithms have been used to produce predictions of the distribution of different geomorphological landforms (e.g. Brenning, 2005; Ermini et al., 2005).”
Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, background): “The MLP can separate non-linear data (Lee et al. 2003), exploit helpful information from imprecise data, and produce acceptable results even when the inputs are not perfect (Ermini et al. 2005; Kanungo et al. 2006).”
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling (2010, methodology): “For large-scale analysis of landslide activity, data acquisition regarding principal casual factors is necessary, followed by the construction of base maps representing the factors controlling the distribution of landslides in the area under study (Ermini et al., 2005; Catani et al., 2005; Ercanoglu”
Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues (2013, background): “…reliable and in some cases more flexible, such as e.g. artificial neural networks (ANN) (Bianchi and Catani, 2002; Lee et al., 2003, 2004; Ermini et al., 2005, Yilmaz, 2009a), linear regression (Atkinson and Massari, 1998), fuzzy membership (Kanungo et al., 2006), conditional probability…”
GIS-based logistic regression method for landslide susceptibility mapping in regional scale * (2006, methodology): “This conservative approach should be used in territorial planning or in preliminary phases of risk analysis, in which the focus must be concentrated on predicting possible hazards and revealing the location of key areas ( Ermini et al., 2005 ).”
Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia (2010, methodology): “Ermini et al. (2005) compared two neural architectures (probabilistic neural network and multi-layered perceptor), obtaining a better prediction with Fig. 1 a Tectonic setting of Peninsular Malaysia (after).”