Geospatial Based Landslide Sensitivity Mapping in Addis Ababa, Ethiopia

Geospatial Based Landslide Sensitivity Mapping in Addis Ababa, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Geospatial Based Landslide Sensitivity Mapping in Addis Ababa, Ethiopia Ebrahim Esa Hassen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6629967/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Numerous triggering variables tend to determine the frequency and intensity of landslides, and human activity frequently makes them worse. Disaster risks stemming from landform features and meteorological factors are frequently reported in the central highlands of Ethiopia. It is where the research region is located, which have a higher possibility of experiencing landslide hazards. Topographic undulation, high rates of urbanization, and the ensuing boom in building activity are characteristics of the current research region. As a result, landslide hazard susceptibility zonation is extremely important for the research area. The objective of the present study is, thus, to delineate the landslide susceptibility map (LSM) using Analytical Hierarchical Process (AHP) model in the study area. Methods The landslide inventory mapping that was conducted through field observations and Google Earth image interpretation identified about 250 landslide locations, and was randomly classified into training datasets (70%) and validation datasets (30%). Major parameters such rainfall intensity, topography, geology, soil type, land use and land cover, distance from the Earthquake Center, stream and road network data, and more were discovered as causative factors for landslide susceptibility in this study. The relative importance of each landslide causative factors was determined using the AHP Model in ArcGIS10x. Results The AHP results indicated that slope steepness, Lineament and/or fault density, rainfall and lithology were found to be the dominant triggering factors of landslides contributing about 83.1% of the area of the study region. Subsequently, a landslide susceptibility index (LSSI) was calculated based on the relative influence of these causative factors in an overall landslide susceptibility analysis. The land slide sensitivity map (LSM) of the study area indicated that around 65% (26885 hectares) of the study area was designated as Very low and low landslide susceptibility category. The remaining 34% (13831 ha), which are often located in regions with high slope gradients, poor geological structure, and high drainage density and rainfall are classed as medium and high landslide vulnerability. The performance of the LSM produced by AHP model, in the present study, was evaluated using Receiver Operating Characteristics (ROC) and area under the curve (AUC) method. The validated AHP model result revealed that the AUC success rate curves was 0.807, which demonstrated a very good model predicting performance. Conclusion Therefore, landslide susceptibility mapping using AHP model and geospatial approaches can deliver very good prediction performance and can be helpful for urban land use planners and other sectorial offices for proper land use planning. Urbanization Triggering Factors AHP LSSI LSM ROC AUC Land Use Planning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6629967","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455967671,"identity":"ae0725d1-b729-4c83-9a71-a5f3d86c47c3","order_by":0,"name":"Ebrahim Esa Hassen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYDACCQYGZgYDCPvghwobIMXYeIBYLYyPJc6kgegGIrRAALMBb8thMAuvFn7p3oefCwq2ycm3H38mIdlw3m5t+2GgLTU20bi0SM45biw9w+C2scGZHDOJwh23k7edSQRqOZaW24BDi8GNNAZpHoPbiRsYctgkJM/cTjY7ANTC2HAYpxb7G2nMv4Fa6uf3P38mwdt2Ltns/EP8Wgwk0thAtiQw3EgwNuBtO2BndoOALRJ3jrFZA7UYbrjxxhAYyMkJZjeAtiTg8Qv/7Dbm2zx/bsvL96c/AEalnb3Z+fSHDz7U2ODUggESwSoTiFUOAvakKB4Fo2AUjIKRAQBhm2VgEKbeVQAAAABJRU5ErkJggg==","orcid":"","institution":"Ethiopian Civil Service University","correspondingAuthor":true,"prefix":"","firstName":"Ebrahim","middleName":"Esa","lastName":"Hassen","suffix":""}],"badges":[],"createdAt":"2025-05-09 15:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6629967/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6629967/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82680720,"identity":"8a43825b-7656-4dc7-a7c0-ebc3e4d904d1","added_by":"auto","created_at":"2025-05-14 05:40:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1618199,"visible":true,"origin":"","legend":"","description":"","filename":"EbrahimLSIMManuscript2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6629967/v1_covered_97ba6a2e-a406-42b0-ac6f-3354d555a3b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGeospatial Based Landslide Sensitivity Mapping in Addis Ababa, Ethiopia\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urbanization, Triggering Factors, AHP, LSSI, LSM, ROC, AUC, Land Use Planning","lastPublishedDoi":"10.21203/rs.3.rs-6629967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6629967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNumerous triggering variables tend to determine the frequency and intensity of landslides, and human activity frequently makes them worse. Disaster risks stemming from landform features and meteorological factors are frequently reported in the central highlands of Ethiopia. It is where the research region is located, which have a higher possibility of experiencing landslide hazards. Topographic undulation, high rates of urbanization, and the ensuing boom in building activity are characteristics of the current research region. As a result, landslide hazard susceptibility zonation is extremely important for the research area. The objective of the present study is, thus, to delineate the landslide susceptibility map (LSM) using Analytical Hierarchical Process (AHP) model in the study area.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe landslide inventory mapping that was conducted through field observations and Google Earth image interpretation identified about 250 landslide locations, and was randomly classified into training datasets (70%) and validation datasets (30%). Major parameters such rainfall intensity, topography, geology, soil type, land use and land cover, distance from the Earthquake Center, stream and road network data, and more were discovered as causative factors for landslide susceptibility in this study. The relative importance of each landslide causative factors was determined using the AHP Model in ArcGIS10x.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe AHP results indicated that slope steepness, Lineament and/or fault density, rainfall and lithology were found to be the dominant triggering factors of landslides contributing about 83.1% of the area of the study region. Subsequently, a landslide susceptibility index (LSSI) was calculated based on the relative influence of these causative factors in an overall landslide susceptibility analysis. The land slide sensitivity map (LSM) of the study area indicated that around 65% (26885 hectares) of the study area was designated as Very low and low landslide susceptibility category. The remaining 34% (13831 ha), which are often located in regions with high slope gradients, poor geological structure, and high drainage density and rainfall are classed as medium and high landslide vulnerability. The performance of the LSM produced by AHP model, in the present study, was evaluated using Receiver Operating Characteristics (ROC) and area under the curve (AUC) method. 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