caa a22 4500 605453969 CHVBK 20210128100204.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s10064-014-0607-7 doi (NATIONALLICENCE)springer-10.1007/s10064-014-0607-7 A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece) [Elektronische Daten] [Christos Polykretis, Maria Ferentinou, Christos Chalkias] The main scope of this study is to compare the performance of a conventional statistical method like the landslide susceptibility index (LSI) and a soft computing method like artificial neural networks (ANNs). These models were applied in order to realistically map landslide susceptibility (LS) in the Krathis and Krios drainage basins in northern Peloponnesus. The relationship between landslides and various conditioning factors contributing to their occurrence was investigated through geographic information system-based analysis. A landslide inventory was realised using aerial-photos, satellite images and field surveys. Eight conditioning factors, including land cover, geology, elevation, slope, aspect, distance to road network, distance to drainage network, distance to structural elements, were considered. Subsequently, LS maps were produced using LSI and ANNs, and they were then compared and validated accordingly. Model performance was checked by an independent validation set of landslide events. For the validation process, the receiver operating curve was drawn and the area-under-the-curve (AUC) values were calculated. The calculated AUC values were 0.852 for the LSI model, and 0.842 for the ANNs; thus, both methods seem to lead to quite similar results. Based on these results, with an average percentage of correctly predicting landslides of about 84%, model validation confirms that extrapolation results are very good, and that both models can be used to mitigate hazards related to landslides, and to aid in generalised land-use planning assessment purposes. Springer-Verlag Berlin Heidelberg, 2014 Landslide susceptibility nationallicence GIS nationallicence Landslide susceptibility index nationallicence Artificial neural networks nationallicence Peloponnesus nationallicence Greece nationallicence Polykretis Christos Department of Geography, Harokopio University, El. Venizelou 70, 17671, Athens, Greece aut Ferentinou Maria Department of Geological Sciences, University of KwaZulu-Natal, Private Bag X54001, Westville, South Africa aut Chalkias Christos Department of Geography, Harokopio University, El. Venizelou 70, 17671, Athens, Greece aut Bulletin of Engineering Geology and the Environment Springer Berlin Heidelberg 74/1(2015-02-01), 27-45 1435-9529 74:1<27 2015 74 10064 https://doi.org/10.1007/s10064-014-0607-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10064-014-0607-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Polykretis Christos Department of Geography, Harokopio University, El. Venizelou 70, 17671, Athens, Greece aut NATIONALLICENCE 700 1- Ferentinou Maria Department of Geological Sciences, University of KwaZulu-Natal, Private Bag X54001, Westville, South Africa aut NATIONALLICENCE 700 1- Chalkias Christos Department of Geography, Harokopio University, El. Venizelou 70, 17671, Athens, Greece aut NATIONALLICENCE 773 0- Bulletin of Engineering Geology and the Environment Springer Berlin Heidelberg 74/1(2015-02-01), 27-45 1435-9529 74:1<27 2015 74 10064