Thesis Open Access

# A GIS-BASED LANDSLIDE SUSCEPTIBILITY ASSESSMENT AND MAPPING USING BIVARIATE STATISTICAL METHODS IN SIMADA AREA, NORTHWEST ETHIOPIA

TILAHUN MERSHA AYELE

### DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<creators>
<creator>
<creatorName>TILAHUN MERSHA AYELE</creatorName>
<affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
</creator>
</creators>
<titles>
<title>A GIS-BASED LANDSLIDE SUSCEPTIBILITY ASSESSMENT AND MAPPING USING BIVARIATE STATISTICAL METHODS IN SIMADA AREA, NORTHWEST ETHIOPIA</title>
</titles>
<publisher>National Academic Digital Repository of Ethiopia</publisher>
<publicationYear>2019</publicationYear>
<subjects>
<subject>Frequency Ratio, Landslide Susceptibility map, Northwestern Ethiopia Simada, Weight of Evidence</subject>
</subjects>
<contributors>
<contributor contributorType="Supervisor">
<contributorName>Matebie Meten (PhD)</contributorName>
<affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
</contributor>
</contributors>
<dates>
<date dateType="Issued">2019-11-27</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="Text">Thesis</resourceType>
<alternateIdentifiers>
</alternateIdentifiers>
<relatedIdentifiers>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Simada area is located 780Km from Addis Ababa in south Gondar Zone of the Amhara National Regional State in northwestern highlands of Ethiopia. The area is a part of the Guna Mountain, which is characterized by rugged topography with deep-cut gorges, weathered volcanic rocks, heavy rainfall and active surface processes. Many landslides have occurred in the area in August 2018 after a period of heavy rainfall and they have caused several types of damages to the local people. The main objective of this study is to characterize landslide causative factors and to generate a landslide susceptibility map (LSM) using frequency ratio (FR) and Weight of evidence (WoE) methods. Landslide inventory map was firstly developed from 576 active and passive landslide scarps using intensive fieldwork and Google Earth image interpretation. These landslide locations were randomly divided into 80% training and 20% validation datasets. Seven landslide causal factors were selected and combined with 80% training dataset using geographic information system (GIS). Then, the corresponding landslide factor maps and LSMs were prepared using Arc GIS software for both FR and WoE models. Finally, the resulted LSMs have been validated by using area under the curve and landslide density index methods. The result shows that, the predictive rate of FR and WoE models were 88.2% and 84.8% respectively. The validation showed that LSM produced by FR model is better in performance. Finally, LSM produced by the FR model had a profound geoengineering significance in terms of landslide hazard prevention and mitigation in the study area.&lt;br&gt;
&amp;nbsp;&lt;/p&gt;</description>
</descriptions>
</resource>

65
74
views