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Evaluating landslide susceptibility using environmental factors, fuzzy membership functions and GISView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000695</doi_batch_id><timestamp>1714017845</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 1</title></titles><publication_date><month>03</month><year>2024</year></publication_date><journal_volume><volume>13</volume></journal_volume><issue>1</issue></journal_issue><journal_article><titles><title>Evaluating landslide susceptibility using environmental factors, fuzzy membership functions and GIS</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>A methodology for landslide susceptibility assessment to delineate landslide prone areas is presented using factor analysis and fuzzy membership functions and Geographic Information Systems (GIS). A landslide inventory of 51 landslides was created in the mountainous part of Xanthi prefecture (North Greece) and the associated conditioning factors were determined for each landslide by field work. Six conditioning factors were evaluated: slope angle, slope aspect, land use, geology, distance to faults and topographical elevation. Fuzzy membership functions were defined for each factor using the landslide frequency data. Factor analysis provided weights (i.e., importance for landslide occurrences) for each one of the above conditioning factors, indicating the most important factors as geology and slope angle. An overlay and index method was adopted to produce the landslide susceptibility map. In this map 96% of the observed landslides are located in very high and high susceptibility zones, indicating a suitable approach for landslide susceptibility mapping.</p></abstract><publication_date><month>04</month><day>30</day><year>2013</year></publication_date><pages><first_page>28</first_page><last_page>40</last_page></pages><publisher_item><identifier id_type="pii">734</identifier></publisher_item><doi_data><doi>10.30955/gnj.000734</doi><timestamp>1714017845</timestamp><resource>https://journal.gnest.org/publication/734</resource></doi_data></journal_article></journal></body></doi_batch> </div>
