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GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria)
Abstract The city of Constantine has suffered frequent landslides during the last few decades because of its geological, geomorphological, climatic and seismotectonic setting as well as anthropic activities. In this work we perform a landslide susceptibility zonation (LSZ) zonation by using bivariate statistical and expert approaches in GIS technology for this area. Firstly, a landslide inventory map was constructed from interpretation of aerial photographs, high resolution satellite images, field surveys and bibliographies. Then various causal factors such as lithology, slope, aspect, precipitation, land use, distance to streams, distance to faults and anthropogenic factors like distance to roads associated with landslide activity were considered and the corresponding thematic layers generated using GIS techniques. The relative importance of these layers in causing landslides has been evaluated using bivariate statistics and expert methods to generate LSZ maps. The expert-based method provided a subjective classification of the study area in terms of landslide susceptibility which does not completely fit the landslide field survey. However unlikely, the bivariate statistics-based method provided the most satisfying results and appears to be the most accurate. Indeed, results show that 27.2 % of the study area lies within a very high to high susceptibility zone that encompasses 73.64 % of the existing landslides. Moderate, low and very low susceptibility zones cover, respectively, 25.7, 21.7 and 25.4 % of the study area. The LSZ maps generated may serve as useful tools for land management and planning in the Constantine region.
GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria)
Abstract The city of Constantine has suffered frequent landslides during the last few decades because of its geological, geomorphological, climatic and seismotectonic setting as well as anthropic activities. In this work we perform a landslide susceptibility zonation (LSZ) zonation by using bivariate statistical and expert approaches in GIS technology for this area. Firstly, a landslide inventory map was constructed from interpretation of aerial photographs, high resolution satellite images, field surveys and bibliographies. Then various causal factors such as lithology, slope, aspect, precipitation, land use, distance to streams, distance to faults and anthropogenic factors like distance to roads associated with landslide activity were considered and the corresponding thematic layers generated using GIS techniques. The relative importance of these layers in causing landslides has been evaluated using bivariate statistics and expert methods to generate LSZ maps. The expert-based method provided a subjective classification of the study area in terms of landslide susceptibility which does not completely fit the landslide field survey. However unlikely, the bivariate statistics-based method provided the most satisfying results and appears to be the most accurate. Indeed, results show that 27.2 % of the study area lies within a very high to high susceptibility zone that encompasses 73.64 % of the existing landslides. Moderate, low and very low susceptibility zones cover, respectively, 25.7, 21.7 and 25.4 % of the study area. The LSZ maps generated may serve as useful tools for land management and planning in the Constantine region.
GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria)
Bourenane, Hamid (author) / Bouhadad, Youcef (author) / Guettouche, Mohamed Said (author) / Braham, Massinissa (author)
2014
Article (Journal)
Electronic Resource
English
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB18
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