Landslide hazard zone mapping Using Geospatial Technologies: A Case Study of Duna Wereda Hadiya Zone, Central Ethiopia

Landslide hazard zone mapping Using Geospatial Technologies: A Case Study of Duna Wereda Hadiya Zone, Central 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 Landslide hazard zone mapping Using Geospatial Technologies: A Case Study of Duna Wereda Hadiya Zone, Central Ethiopia Dinkineh Abebe, Tariku Kebede, Adama Dessalgn, Aster Chalichisa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4549060/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 The main purpose of this study is to assess landslide hazard zones for Duna wereda by using Geospatial technology. The causative factors considered to landslide analysis in the area was, slope, aspect, distance from water, soil type, land use land cover and Elevation. Analytical Hierarchy Processes the weight of each factor were calculated and assigned in GIS. To add these factors and produce landslide hazard map weighted linear combination was, used.by using IDRIS software. Thus, by using obtained weight value Landslide hazard map prepared was, categorized in to high, low and very low hazard zone. Therefore the result of the study revealed that the area coverage of the level of landslide risk very low and high are 69.63%,22.94% and 7.43% respectively. As identified there is high landslide risk area that may cause different6 effect on the human being and on property. Therefore The result of analysis were verified using landslide inventory data this study may have great importance in giving awareness in landslide hazard area and also helps to mitigate the landslide impact. Finally, this landslide hazard zonation of Duna wereda suggested for further researcher, land management and concerned body should have based on the present finding. Landslide Duna Woreda GIS and remote sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Globally, Landslides are among the most common natural dangers and are the most damaging, leading to a variety of human and environmental impacts [ 1 ]. The word landslide refer to a wide variety of processes that result in the movement of slope forming materials including rock, soil, artificial fill, or a material may move by falling, toppling, sliding, spreading or flowing. Landslide is the movement of a mass of rock, debris or earth down a slope [ 2 ]. Landslides may be ignored if they occur in uninhabited places and places of no human interest. But, if they occur in places of importance such as highways, railway lines, valleys, reservoirs, human settled areas and agricultural lands, obviously such instances lead to blocking of traffic, collapse of buildings, harm to fertile lands and so on apart from heavy loss of life and property[ 2 ]. These damages can be reduced by understanding the mechanism of occurrence, prediction through hazard assessment, hazard zonation and early warning system [ 3 ].In such case, preparation of landslide hazard maps can be an preliminary footstep to mitigation and control. Hazard assessment can help authorities prevent and reduce damage through proper land use management for infrastructural development and environmental protection [ 4 ].The potential area which is prone to slope failure can be identified using Landslide Hazard Zonation (LHZ) mapping. A Landslide Hazard Zonation is clarifying “the split of the land area in homogeneous domains and their ranked based on degree of actual hazard reason by movements of mass” [ 5 ]. LHZ mapping is necessary to development of the planning and disaster management of landslide vulnerable area [ 6 ]. LHZ mapping is very important for identifying and predicting the possible sliding zones [ 6 ]. Landslide is a common environmental problem in highlands of Ethiopia [ 7 ]. Rainfall is major triggering factor for debris/earth slides, debris/earth flows and, medium to large- scale rockslides[ 8 ]. Furthermore, lithology, soil deposit, slope angle, aspect, elevation, land use/land cover and groundwater condition have influential factors for the occurrence of landslide[ 9 ]. In addition, the steep slope area that covered by deeply weathered rock, closely spaced faults, fractures jointed and sheared basaltic rocks are initiated to slope instability[ 7 ].Similarly, in Ethiopia, most of the people are populated in the highland areas; due this to they are seriously suffered for landslide hazards [ 10 ]. Especially the southern notion is the most vulnerable areas for landslide hazards[ 9 ]. In the last decades various types of landslides were occurred in southern and southwestern highlands of Ethiopian. This region is the most populated part of the country and has different topographies[ 11 ]. Particularly, Duna Wereda in southwestern highlands is one of the most landslide vulnerable areas in Hadiya Zone. In Before one decade, the landslide occurred in Duna wereda caused for the death of people, some injured and loss of properties. In addition, it caused damages on infrastructures, crops and lands. The aim of this study was to map landslide hazard zonation in Duna wereda through the integration of GIS and remote sensing techniques. Landslide hazard zonation map of the present study mainly aimed to find out previously existed landslide hazard sites and sensitivity areas that would be happen in the future. The study will have an important role for appropriate sites selection processes for agriculture practices, construction and recommend the way minimize the upcoming impact in landslide prone areas. In addition to this, it is important to planners, local administrations, and decision makers in disaster planning for reducing the losses of life and property. 2. Methods and Data 2.1 Study area Description The study was conducted in Duna Wereda Hadiya Zone, Central Ethiopia. Duna wereda is located in Central Ethiopia Region, in the South West central part of Ethiopia about a distance of 270 km south of Addis Ababa, 211 km from regional city, Hawassa, in the South West and 42 km from the Zonal Town, south away from Hossana, the capital of Hadiya Zone and it is one of the 11 District of Hadiya Zone and latitudinal and longitudinally located between 7 0 37′19ʹʹ N latitude and 37 0 37′ 14ʹʹE longitudes. According to the recent Wereda population reports (2013), the total number of household in Duna wereda is 18,752. Out of these, 18,109 (95.57%) are men headed households and 643 (3.43) are women headed households. A total number of households in 30 rural kebeles is 17,580 (93.75%). Out of these, 17,080 (97.15%) are men headed households and 500 (2.85%) are women headed households and a total number of households in 2 town kebeles is 1172 (6.25%). Out of these, 722 (61.60%) are men headed households and 450 (38.4) are women headed households. The total population of the Duna District is about 148,566 (75,383 (50.74℅) is male and 73,183 (49.26%) is female). The Wereda has an agriculturally suitable land in terms of topography. Agro ecologically, the Duna Wereda is classified in to three categories like as Dega 85%, Weina Dega 10% and kola 5%. The annual rainfall varies from 1500mm to 1896 mm, the mean annual total rainfall is about 1896mm, and has an average temperature of wereda is 19CO[13]. The large part of Duna wereda topographically falls within the southeastern highlands of Ethiopia, data obtained from[12] According to [12] the elevation within the wereda ranges from 2,970m mean sea level Sengiye which is the highest mountain in Hadiya Zone and 1000m mean sea level above which is the lowest place in the wereda. The average elevation of the wereda is taken as to be 1985m from the mean sea level. 2.2 Data collection 2.2.1 Primary data This type of data is unprocessed data which is directly acquired from the field of study area. These datas are Photo of some landslide hazard area, GCP points for validation and georeferncing purpose and landsat image. 2.2.2. Secondary Data These types of data are the processed data which is very important for this study. Some these datas are written document of landslide hazard in the study area from the wereda Communication office page, published and unpublished articles related to the study and books The summaries of both primary and secondary data used are listed in table below: Table 1 : Data to be used for the study No Data to be used Source Purpose 1 Aerial image GII LULC classification 2 Soil FAO For landslide hazard mapping 3 DEM GII For landslide hazard mapping 4 SLOPE GII For landslide hazard mapping 5 Rainfall NMA For landslide hazard mapping 6 Distance from water For landslide hazard mapping 7 GCP Field For validation 2.3 Method Methods are the set of procedures which are selected to attain the required objective of the study. Thus, some of the methods to be applied for the study are as discussed below. 2.3.1 Image pre-processing. The downloaded Landsat images are not directly used for the required analysis. To use the Landsat image for the required purpose image preprocessing is the first stage. Therefore, pre-processing like, image layer stake, geometric and radiometric correction are very helpful to improve the quality of data to be used. 2.3.2 Classification Regarding the existing land use type of the study area, classification of these land use land cover category is very important. Therefore, the study area will be classified by using supervised classification techniques due the researcher’s prior knowledge about the study area. 2.3.3. Weight overlay This is the selected method to identify the landslide hazard area depending up on the selected criteria’s, like slope, rainfall, DEM, LULC factors. Thus, for the stated factors the weight value will be assigned to attain the required objective of the study. 3. Result and Discussion 3.1 Slope Slope angle is a vital factor in landslide Hazard valuation as such it is frequently, used in creating landslide hazard maps [ 14 ]. For the current study area, slope was, divided into five categories classes: (0–10), (10–20), (20–30), (30–40) and (greater than 40). Slope class and area coverage are, recorded in Table 3.1 . Table 3.1 Slope and area coverage S.No Slope class (degree) Area(ha) Area (%) 1 0–10 4940.55 23.98 2 10–20 5019.03 24.36 3 20–30 3805.29 18.47 4 30–40 3288.33 15.96 5 > 40 3545.37 17.21 Total 22273.83 100 As indicated in the table, most part of the area was, dominated by gentle topography. Thus, 23.98% of the total area was under the scale class of 0_10 and it has less effect on landslide. However, from the slope class greater than 20 the area cover was greater than 50% which has great effect to landslide occurrence. For more clarification it was shown in figure below. 3. 2. Soil Type Soil type is significant parameter in landslide analysis because type of soil determine tendency of soil particle to resist sliding across each other. The nature of the movement is, controlled by the earth materials involved (Peter. 1970). Soil type with large particles such as sandy soils are the most cohesive while clayey soils with fine particles almost cohesiveness. Friction force are dependent on the load placed on soil surface. The greater the load the greater the likelihood the force of friction will overcome. This result in the movement of soil particles within the soil layer and potential to slope failure [ 14 ] .Two different soil type exist in the study area. Although, the presence of different soil type in the area determine the nature of mass movement and slope failure. The soil type observed in study area have different soil property and area coverage. The total area coverage and soil type exist was, presented in table. Table 3.2 Soil type and area coverage S.no Soil Type Area (ha) Area (%) 1 Humic Nitisols 11.80 0.06 2 Chromic Luvisols 20586.77 99.94 Total 22247.3 100 From the table above, the less soil type in the area is Humic Nitosols covers (0.06%) of total area. This type of soil was, Deep, dark red, brown or yellow clayey soils having a pronounced shiny, nut-shaped structure . The second soil type dominate the area was chromic luvisols which account only 99.04% of total area. This soil type located at western part of the study area by covering highest area. This soil type have loam texture. It was, dominated by medium texture. Most part of the area found at northeastern have no information about soil type. The map of all the explained soil are shown in figure below. 3.3 Aspect Aspect show the inclination or angle of slope in a given area. Although, aspect strongly influences potential direct incident radiation and thus temperature. Therefore, the humidity of the soil on the ground may change. Aspect as a landslide-conditioning factor has been, considered in various studies [ 15 ]. Aspect gravely affects hydrological processes such as evapotranspiration, weathering, and vegetation growth particularly in arid environments and areas with weak soil types [ 16 ]. As a result, this parameter was, also considered as a conditioning factor for the present study area. The slope aspect for the study area was, divided into five categories: North, Northeast, Northwest, Southwest and south. Aspect class and area coverage was shown in table below. Table 3.3 Aspect and area coverage S.no Aspect class Area (ha) Area% 1 North 3612.24 16.23 2 Northeast 4557.96 20.48 3 Northwest 4272.75 19.20 4 Southeast 4633.47 20.82 5 South 3862.62 23.24 Total 22247.3 100 From the table most of the area was, dominated by slope facing to south, which account 23.24% of total area. This aspect class commonly covered southeastern part of the study area. The second aspect class dominate the area was, southeast facing slope, which cover 20.82% of total area. It was, distributed in all part of the study area except northeastern. However, various researcher prove that slope facing to north and northeaster have high probability to leads to landslide. Thus, the combined area of these aspect class are 37%. In present study area, Slope facing to northwest in the study area was, cover 19.2% of total area. This aspect class mainly distributed at northwestern and scattered at all part of the study except northeast. It was the third dominating aspect class in the area. The fourth dominant aspect class in the area was, aspect class aspect facing to north direction. It cover 16.23% of total area. This aspect class in the study area mostly falls on highly elevated area located at western and northwester of the study area. Furthermore it was illustrated in figure below. 3.4 Land use Land cover Land use and land cover play an important role in influencing landslides. Several landslides occur mainly due to the inappropriate activities such as deforestation and increase in the urbanization over a period. Although, Rapid changes in land use and land cover, as well as land degradation processes, are pioneers to mass movement events. Consequently, for the present study land use land cover was, considered as a contributing factor for landslide hazard analysis. The Identified land cover types that were, reclassified included: Agriculture, open space, Built-up, and Vegetation. Table 3.4 LULC type and area coverage S.no LuLc Type Area (ha) Area (%) 1 Vegetation 5922.72 26.59 2 Agriculture 10369.8 46.55 3 Bare land 2155.05 9.67 4 Buit_up 3826.26 17.17 Total 22247.3 100.00 As indicated in the table, large parts of the study area were, covered by agricultural land, which account about 46.55% of total area. The second land cover dominate the area were Vegetation, which account 26.59% of total area. The area also characterized by bare land which account 9.67% of total area. It was, mainly found at southeastern of the area and northeaster along water body. In addition, forest have significant contribution to characterize the area. Built-up area cover 17.17% of total area. 3.5 Distance from the stream Rivers with a number of drainage networks have a great chance of landslide rate as they erode the slope base and saturate the underwater section of the slope forming material [ 17 ].Streamline was produced from Digital Elevation Model and .categorized depending on stream order classication. Landslides with this study area were typically related in some sort of stream order. The maps were generated from the Euclidean distance extension buffering technique with the spatial analyst tool of Arc GIS 10.1. These maps were ordered into five sub-classes: 0–200,200– 400, 400–600, 600–800 and 800–1000 meters. Table 3.5 Distance from the stream and area coverage s.no Distance from stream Area (ha) Area (%) 1 0-200 3618.835 16.24 2 200–400 3276.534 14.71 3 400–600 2885.234 12.95 4 5 600–800 800–1000 2470.531 10022.696 11.09 44.99 Total 22247.3 100.00 3. 6 Rainfall Rainfall mainly concentrated and protracted precipitations were preventing weights that activate landslide by provided that water thus enhancing underground hydrostatics levels as well as pore water pressures. Once soil undertakes as pressure varies, water with it can be produced pessimistic or upward pressure, as it could not exhaust rapidly. While the pore water pressures are comparable to higher pressures, shearing resistance of material reduces as well as would go ahead to breakdown of materials. A data of stations that enclose research areas were gathered from the National Metrology Agency map of Ethiopia. The rainfall map of the research areas was organized with GIS. The maps of research areas were divided into three annual rainfalls ordered of 900–1200, 1200–1500, and1500–1800 millimeters. Table 3.6 Rainfall s.no Rainfall/mm Area (ha) Area (%) 1 0-900 4718.835 21.28 2 900–1200 4486.534 20.16 3 1200–1500 7883.317 35.43 4 1500–1800 > 1800 4998.083 0 22.46 Total 22247.3 100.00 3. 7 Elevation Topographic-related factors such as elevations were generated from the 30m*30m Digital Elevation Model of research region. Elevations are broadly applied for evaluation of landslide susceptibility. Elevation difference might be correlated in the direction of unlike environmental situates like rainfall and plants category [ 18 ].Thus, for this study the elevation was categorized in five category as shown in below. Table 3.7 Elevation s.no Elevation Area (ha) Area (%) 1 1,683–2,015 685.89 3.07 2 2,015.0–2,213 1256.04 5.63 3 2,213.0–2,383 6518.52 29.26 4 5 6 2,383.0–2,533 2,533.0–2,673 2,673.0–2,966 5499.54 5298.39 2024.28 24.69 23.78 13.53 Total 22247.3 100.00 3.8 Landslide Hazard Zonation Landslide hazard zonation mainly studies the relative of landslide hazard inducing factors, create factor analysis models and finally perform hazard zonation. Firstly, the key hazard triggering factors associated with the studied area are selected and integrated into GIS platform. The result of the landslide hazard zonation revealed that: 7.43% or 1655 ha of the total area is high landslide risk area and 22.292% and 69.63% of the area are low and very low risk areas respect. Table 3.8 Area Coverage of Land slide hazard zonation map. S.no Hazard level Area (ha) Area (%) 1 Very low 15492.3 69.63 2 Low 5100 22.92 3 High 1655 7.43 Total 22247.3 100.00 In conclusion, this research project has successfully achieved its objectives of preparing a landslide hazard zone map of the study area using geospatial technologies. Through extensive analysis and data collection, we have identified the causative factors of landslides in the study area and have identified high-risk areas vulnerable to landslides within the wereda. 5. Conclusion and Recommendation 4.1 conclusion Our findings highlight the importance of utilizing geospatial technologies in assessing and mapping landslide hazards. By providing timely and accurate information to local authorities and communities about potential landslide hazards, we can enhance preparedness and response efforts, ultimately reducing the impact of landslides on human lives and infrastructure. 4.2. Recommendation Based on our research findings, we would like to make the following recommendations: Continued monitoring and assessment Landslide hazards are dynamic and can change over time. We recommend implementing a continuous monitoring and assessment system using geospatial technologies to update the landslide hazard zone map regularly. This will ensure that local authorities and communities have access to up-to-date information and can take appropriate measures to mitigate risks. Capacity building Given the importance of geospatial technologies in assessing landslide hazards, we recommend providing training and capacity building programs to local authorities and communities. This will enable them to effectively utilize these technologies and make informed decisions regarding land use planning, infrastructure development, and emergency response. Community engagement It is crucial to engage local communities in the process of identifying high-risk areas and developing mitigation strategies. We recommend establishing community-based committees or task forces to actively participate in landslide hazard management efforts. This will foster a sense of ownership and empower communities to take proactive measures in reducing their vulnerability to landslides. Integration with existing systems To maximize the effectiveness of the landslide hazard zone map, we recommend integrating it with existing disaster management systems and platforms. This will facilitate seamless information sharing and coordination among different stakeholders, enhancing overall preparedness and response capabilities. Further research While this study has provided valuable insights, there is still much more to explore in relation to landslide hazards and geospatial technologies. We recommend that future researchers build upon our findings and delve deeper into specific aspects such as the impact of climate change on landslide hazards or the development of advanced predictive models. By implementing these recommendations, we believe that local authorities, communities, and researchers can collectively contribute to the mitigation of landslide hazards and the protection of lives and infrastructure in the study area. Abbreviations GCPs Ground Control Points GIS Geographic Information System GPS Global Positioning System LHZ Land hazard Zonation RS Remote Sensing ERDAS Earth Resource Data Analysis System DEM Digital Elevation model LULC Land use land cover USGS United States Geological Survey. FAO Food and Agriculture Organization GII Geospatial Information Institute NMA National Metrology Agency Declarations Availability of data and materials The data and materials used for analysis in this manuscript are available at the corresponding author. It is possible to reasonably request the corresponding author. Competing interests The authors declare that they have no competing interests Funding There is no fund provided to publish the research work Authors’ contributions First and second authors contributed by conducting research and writing the manuscript. The other authors contributed by arranging, organizing, and directing the article starting from research idea up to manuscript full write-up. All authors reviewed the results and agreed the final version of the manuscript. Acknowledgements First of all we would like to thanks our Almighty God for his tireless protection. We would like to express our deepest gratitude to Wachemo University for providing opportunity to conduct our research. Without their generous support, this research project would not have been possible. Lastly, we would like to express our gratitude to all those who have directly or indirectly contributed to the completion of this research project.. 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Available: https://doi.org/10.1371/journal.pone.0133262 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-4549060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312503545,"identity":"a5e70130-715c-4de9-88f6-2ebf94daf253","order_by":0,"name":"Dinkineh Abebe","email":"","orcid":"","institution":"Wachemo University","correspondingAuthor":false,"prefix":"","firstName":"Dinkineh","middleName":"","lastName":"Abebe","suffix":""},{"id":312503547,"identity":"fa12a83e-bfcb-448e-83e6-596cd3d5f179","order_by":1,"name":"Tariku 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Chalichisa","email":"","orcid":"","institution":"Wachemo University","correspondingAuthor":false,"prefix":"","firstName":"Aster","middleName":"","lastName":"Chalichisa","suffix":""}],"badges":[],"createdAt":"2024-06-08 05:53:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4549060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4549060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59514121,"identity":"ae5dc479-c639-4a12-972a-9c0e6f5987a3","added_by":"auto","created_at":"2024-07-02 17:26:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":315118,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.1: slope map\u003c/p\u003e","description":"","filename":"3.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/78e2f2421881e9933b04743c.png"},{"id":59514120,"identity":"aa9e2937-fca6-4597-a543-a1ef866af3c1","added_by":"auto","created_at":"2024-07-02 17:26:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88347,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.2: Soil map\u003c/p\u003e","description":"","filename":"3.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/16f5714a06918156d6f41b9f.png"},{"id":59515764,"identity":"4219986e-27f2-4fae-92be-74578cf91489","added_by":"auto","created_at":"2024-07-02 17:34:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":311283,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.3: Aspect Map\u003c/p\u003e","description":"","filename":"3.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/c9184648a8ca4efdad7d2595.png"},{"id":59514126,"identity":"073eae7f-3783-4313-8df1-c8c568fe9cd9","added_by":"auto","created_at":"2024-07-02 17:26:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":255973,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.4: LULC map\u003c/p\u003e","description":"","filename":"3.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/1f18fb4a245c32819cc20108.png"},{"id":59514123,"identity":"0674fb03-559b-454f-876d-296896f97488","added_by":"auto","created_at":"2024-07-02 17:26:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181263,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.5: Distance from the stream Map\u003c/p\u003e","description":"","filename":"3.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/848b41dacf7100024d985bd4.png"},{"id":59514119,"identity":"99367d2e-d616-4695-82aa-14f8a97d5a80","added_by":"auto","created_at":"2024-07-02 17:26:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":43251,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.6: Rainfall Map\u003c/p\u003e","description":"","filename":"3.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/80c3b385746767d2d89cf413.png"},{"id":59514124,"identity":"cb5a280a-3c54-4252-bdce-61b1fe0b3319","added_by":"auto","created_at":"2024-07-02 17:26:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":144686,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.7: Elevation Map\u003c/p\u003e","description":"","filename":"3.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/653fa725ee782421d7b4eb15.png"},{"id":59515770,"identity":"ea524054-9260-44c8-af36-6353b7803d35","added_by":"auto","created_at":"2024-07-02 17:34:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":216234,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.8: Landslide hazard zonation Map\u003c/p\u003e","description":"","filename":"3.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/1df68c6bf4f056d3f5bf2d2b.png"},{"id":59962482,"identity":"64a06648-cef0-4506-b6ef-a6809d8313d0","added_by":"auto","created_at":"2024-07-10 01:08:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1960376,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4549060/v1/758c0492-f624-45e9-92db-6a1703024527.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Landslide hazard zone mapping Using Geospatial Technologies: A Case Study of Duna Wereda Hadiya Zone, Central Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, Landslides are among the most common natural dangers and are the most damaging, leading to a variety of human and environmental impacts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The word landslide refer to a wide variety of processes that result in the movement of slope forming materials including rock, soil, artificial fill, or a material may move by falling, toppling, sliding, spreading or flowing. Landslide is the movement of a mass of rock, debris or earth down a slope [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Landslides may be ignored if they occur in uninhabited places and places of no human interest. But, if they occur in places of importance such as highways, railway lines, valleys, reservoirs, human settled areas and agricultural lands, obviously such instances lead to blocking of traffic, collapse of buildings, harm to fertile lands and so on apart from heavy loss of life and property[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These damages can be reduced by understanding the mechanism of occurrence, prediction through hazard assessment, hazard zonation and early warning system [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].In such case, preparation of landslide hazard maps can be an preliminary footstep to mitigation and control. Hazard assessment can help authorities prevent and reduce damage through proper land use management for infrastructural development and environmental protection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].The potential area which is prone to slope failure can be identified using Landslide Hazard Zonation (LHZ) mapping. A Landslide Hazard Zonation is clarifying \u0026ldquo;the split of the land area in homogeneous domains and their ranked based on degree of actual hazard reason by movements of mass\u0026rdquo; [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. LHZ mapping is necessary to development of the planning and disaster management of landslide vulnerable area [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. LHZ mapping is very important for identifying and predicting the possible sliding zones [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLandslide is a common environmental problem in highlands of Ethiopia [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Rainfall is major triggering factor for debris/earth slides, debris/earth flows and, medium to large- scale rockslides[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, lithology, soil deposit, slope angle, aspect, elevation, land use/land cover and groundwater condition have influential factors for the occurrence of landslide[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, the steep slope area that covered by deeply weathered rock, closely spaced faults, fractures jointed and sheared basaltic rocks are initiated to slope instability[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].Similarly, in Ethiopia, most of the people are populated in the highland areas; due this to they are seriously suffered for landslide hazards [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Especially the southern notion is the most vulnerable areas for landslide hazards[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the last decades various types of landslides were occurred in southern and southwestern highlands of Ethiopian. This region is the most populated part of the country and has different topographies[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticularly, Duna Wereda in southwestern highlands is one of the most landslide vulnerable areas in Hadiya Zone. In Before one decade, the landslide occurred in Duna wereda caused for the death of people, some injured and loss of properties. In addition, it caused damages on infrastructures, crops and lands. The aim of this study was to map landslide hazard zonation in Duna wereda through the integration of GIS and remote sensing techniques. Landslide hazard zonation map of the present study mainly aimed to find out previously existed landslide hazard sites and sensitivity areas that would be happen in the future. The study will have an important role for appropriate sites selection processes for agriculture practices, construction and recommend the way minimize the upcoming impact in landslide prone areas. In addition to this, it is important to planners, local administrations, and decision makers in disaster planning for reducing the losses of life and property.\u003c/p\u003e"},{"header":"2. Methods and Data","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study area Description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in Duna Wereda Hadiya Zone, Central Ethiopia. Duna wereda is located in Central Ethiopia Region, in the South West central part of Ethiopia about a distance of 270 km south of Addis Ababa, 211 km from regional city, Hawassa, in the South West and 42 km from the Zonal Town, south away from Hossana, the capital of Hadiya Zone and it is one of the 11 District of Hadiya Zone and latitudinal and longitudinally \u0026nbsp;located between 7 0 37\u0026prime;19ʹʹ N latitude and 37 0 37\u0026prime; 14ʹʹE longitudes.\u003c/p\u003e\n\u003cp\u003eAccording to the recent Wereda population reports (2013), the total number of household in Duna wereda is 18,752. Out of these, 18,109 (95.57%) are men headed households and 643 (3.43) are women headed households. A total number of households in 30 rural kebeles is 17,580 (93.75%). Out of these, 17,080 (97.15%) are men headed households and 500 (2.85%) are women headed households and a total number of households in 2 town kebeles is 1172 (6.25%). Out of these, 722 (61.60%) are men headed households and 450 (38.4) are women headed households. The total population of the Duna District is about 148,566 (75,383 (50.74℅) is male and 73,183 (49.26%) is female). The Wereda has an agriculturally suitable land in terms of topography. Agro ecologically, the Duna Wereda is classified in to three categories like as Dega 85%, Weina Dega 10% and kola 5%. The annual rainfall varies from 1500mm to 1896 mm, the mean annual total rainfall is about 1896mm, and has an average temperature of wereda is 19CO[13]. The large part of Duna wereda topographically falls within the southeastern highlands of Ethiopia, data obtained from[12] According to [12] the elevation within the wereda ranges from 2,970m mean sea level Sengiye which is the highest mountain in Hadiya Zone and 1000m mean sea level above which is the lowest place in the wereda. The average elevation of the wereda is taken as to be 1985m from the mean sea level.\u003c/p\u003e\n\u003ch2\u003e2.2 Data collection\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Primary data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis type of data is unprocessed data which is directly acquired from the field of study area. These datas are Photo of some landslide hazard area, GCP points for validation and georeferncing purpose and landsat image.\u003c/p\u003e\n\u003ch3\u003e2.2.2. Secondary Data\u003c/h3\u003e\n\u003cp\u003eThese types of data are the processed data which is very important for this study. Some these datas are written document of landslide hazard in the study area from the wereda Communication office page, published and unpublished articles related to the study and books\u003c/p\u003e\n\u003cp\u003eThe summaries of both primary and secondary data used are listed in table below:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable\u0026nbsp;\u003c/em\u003e\u003cem\u003e1\u003c/em\u003e\u003cem\u003e: Data to be used for the study\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.589785831960461%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.45469522240527%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eData to be used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780889621087315%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.17462932454695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.589785831960461%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.45469522240527%\" valign=\"top\"\u003e\n \u003cp\u003eAerial image\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780889621087315%\" valign=\"top\"\u003e\n \u003cp\u003eGII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.17462932454695%\" valign=\"top\"\u003e\n \u003cp\u003eLULC classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.589785831960461%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.45469522240527%\" valign=\"top\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780889621087315%\" valign=\"top\"\u003e\n \u003cp\u003eFAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.17462932454695%\" valign=\"top\"\u003e\n \u003cp\u003eFor landslide hazard mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.589785831960461%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.45469522240527%\" valign=\"top\"\u003e\n \u003cp\u003eDEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780889621087315%\" valign=\"top\"\u003e\n \u003cp\u003eGII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.17462932454695%\" valign=\"top\"\u003e\n \u003cp\u003eFor landslide hazard mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.589785831960461%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.45469522240527%\" valign=\"top\"\u003e\n \u003cp\u003eSLOPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780889621087315%\" valign=\"top\"\u003e\n \u003cp\u003eGII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.17462932454695%\" valign=\"top\"\u003e\n \u003cp\u003eFor landslide hazard mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.589785831960461%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.45469522240527%\" valign=\"top\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780889621087315%\" valign=\"top\"\u003e\n \u003cp\u003eNMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.17462932454695%\" valign=\"top\"\u003e\n \u003cp\u003eFor landslide hazard mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.589785831960461%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.45469522240527%\" valign=\"top\"\u003e\n \u003cp\u003eDistance from water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780889621087315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.17462932454695%\" valign=\"top\"\u003e\n \u003cp\u003eFor landslide hazard mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.589785831960461%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.45469522240527%\" valign=\"top\"\u003e\n \u003cp\u003eGCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780889621087315%\" valign=\"top\"\u003e\n \u003cp\u003eField\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.17462932454695%\" valign=\"top\"\u003e\n \u003cp\u003eFor validation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethods are the set of procedures which are selected to attain the required objective of the study. Thus, some of the methods to be applied for the study are as discussed below.\u003c/p\u003e\n\u003ch3\u003e2.3.1 Image pre-processing.\u003c/h3\u003e\n\u003cp\u003eThe downloaded Landsat images are not directly used for the required analysis. To use the Landsat image for the required purpose image preprocessing is the first stage. Therefore, pre-processing like, image layer stake, geometric and radiometric correction are very helpful to improve the quality of data to be used.\u003c/p\u003e\n\u003ch3\u003e2.3.2 Classification\u003c/h3\u003e\n\u003cp\u003eRegarding the existing land use type of the study area, classification of these land use land cover category is very important. Therefore, the study area will be classified by using supervised classification techniques due the researcher\u0026rsquo;s prior knowledge about the study area.\u003c/p\u003e\n\u003ch3\u003e2.3.3. Weight overlay\u003c/h3\u003e\n\u003cp\u003eThis is the selected method to identify the landslide hazard area depending up on the selected criteria\u0026rsquo;s, like slope, rainfall, DEM, LULC factors. Thus, for the stated factors the weight value will be assigned to attain the required objective of the study.\u003c/p\u003e"},{"header":"3. Result and Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Slope\u003c/h2\u003e \u003cp\u003eSlope angle is a vital factor in landslide Hazard valuation as such it is frequently, used in creating landslide hazard maps [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For the current study area, slope was, divided into five categories classes: (0\u0026ndash;10), (10\u0026ndash;20), (20\u0026ndash;30), (30\u0026ndash;40) and (greater than 40). Slope class and area coverage are, recorded in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSlope and area coverage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope class (degree)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4940.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5019.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3805.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3288.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3545.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22273.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs indicated in the table, most part of the area was, dominated by gentle topography. Thus, 23.98% of the total area was under the scale class of 0_10 and it has less effect on landslide. However, from the slope class greater than 20 the area cover was greater than 50% which has great effect to landslide occurrence. For more clarification it was shown in figure below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3. 2. Soil Type\u003c/h3\u003e\n\u003cp\u003eSoil type is significant parameter in landslide analysis because type of soil determine tendency of soil particle to resist sliding across each other. The nature of the movement is, controlled by the earth materials involved (Peter. 1970). Soil type with large particles such as sandy soils are the most cohesive while clayey soils with fine particles almost cohesiveness.\u003c/p\u003e \u003cp\u003eFriction force are dependent on the load placed on soil surface. The greater the load the greater the likelihood the force of friction will overcome. This result in the movement of soil particles within the soil layer and potential to slope failure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] .Two different soil type exist in the study area. Although, the presence of different soil type in the area determine the nature of mass movement and slope failure. The soil type observed in study area have different soil property and area coverage. The total area coverage and soil type exist was, presented in table.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoil type and area coverage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumic Nitisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromic Luvisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20586.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22247.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the table above, the less soil type in the area is Humic Nitosols covers (0.06%) of total area. This type of soil was, Deep, dark red, brown or yellow clayey soils having a pronounced shiny, nut-shaped structure .\u003c/p\u003e \u003cp\u003eThe second soil type dominate the area was chromic luvisols which account only 99.04% of total area. This soil type located at western part of the study area by covering highest area. This soil type have loam texture. It was, dominated by medium texture. Most part of the area found at northeastern have no information about soil type. The map of all the explained soil are shown in figure below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Aspect\u003c/h2\u003e \u003cp\u003eAspect show the inclination or angle of slope in a given area. Although, aspect strongly influences potential direct incident radiation and thus temperature. Therefore, the humidity of the soil on the ground may change. Aspect as a landslide-conditioning factor has been, considered in various studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Aspect gravely affects hydrological processes such as evapotranspiration, weathering, and vegetation growth particularly in arid environments and areas with weak soil types [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As a result, this parameter was, also considered as a conditioning factor for the present study area. The slope aspect for the study area was, divided into five categories: North, Northeast, Northwest, Southwest and south. Aspect class and area coverage was shown in table below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAspect and area coverage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspect class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3612.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4557.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4272.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoutheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4633.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3862.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22247.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the table most of the area was, dominated by slope facing to south, which account 23.24% of total area. This aspect class commonly covered southeastern part of the study area. The second aspect class dominate the area was, southeast facing slope, which cover 20.82% of total area. It was, distributed in all part of the study area except northeastern. However, various researcher prove that slope facing to north and northeaster have high probability to leads to landslide. Thus, the combined area of these aspect class are 37%. In present study area, Slope facing to northwest in the study area was, cover 19.2% of total area. This aspect class mainly distributed at northwestern and scattered at all part of the study except northeast. It was the third dominating aspect class in the area. The fourth dominant aspect class in the area was, aspect class aspect facing to north direction. It cover 16.23% of total area. This aspect class in the study area mostly falls on highly elevated area located at western and northwester of the study area. Furthermore it was illustrated in figure below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Land use Land cover\u003c/h2\u003e \u003cp\u003eLand use and land cover play an important role in influencing landslides. Several landslides occur mainly due to the inappropriate activities such as deforestation and increase in the urbanization over a period. Although, Rapid changes in land use and land cover, as well as land degradation processes, are pioneers to mass movement events. Consequently, for the present study land use land cover was, considered as a contributing factor for landslide hazard analysis. The Identified land cover types that were, reclassified included: Agriculture, open space, Built-up, and Vegetation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC type and area coverage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLuLc Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5922.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10369.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2155.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuit_up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3826.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22247.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs indicated in the table, large parts of the study area were, covered by agricultural land, which account about 46.55% of total area. The second land cover dominate the area were Vegetation, which account 26.59% of total area.\u003c/p\u003e \u003cp\u003eThe area also characterized by bare land which account 9.67% of total area. It was, mainly found at southeastern of the area and northeaster along water body. In addition, forest have significant contribution to characterize the area. Built-up area cover 17.17% of total area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Distance from the stream\u003c/h2\u003e \u003cp\u003eRivers with a number of drainage networks have a great chance of landslide rate as they erode the slope base and saturate the underwater section of the slope forming material [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Streamline was produced from Digital Elevation Model and .categorized depending on stream order classication. Landslides with this study area were typically related in some sort of stream order. The maps were generated from the Euclidean distance extension buffering technique with the spatial analyst tool of Arc GIS 10.1. These maps were ordered into five sub-classes: 0\u0026ndash;200,200\u0026ndash; 400, 400\u0026ndash;600, 600\u0026ndash;800 and 800\u0026ndash;1000 meters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistance from the stream and area coverage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003es.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from stream\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3618.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3276.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2885.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u0026ndash;800\u003c/p\u003e \u003cp\u003e800\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2470.531\u003c/p\u003e \u003cp\u003e10022.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.09\u003c/p\u003e \u003cp\u003e44.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22247.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3. 6 Rainfall\u003c/h3\u003e\n\u003cp\u003eRainfall mainly concentrated and protracted precipitations were preventing weights that activate landslide by provided that water thus enhancing underground hydrostatics levels as well as pore water pressures. Once soil undertakes as pressure varies, water with it can be produced pessimistic or upward pressure, as it could not exhaust rapidly. While the pore water pressures are comparable to higher pressures, shearing resistance of material reduces as well as would go ahead to breakdown of materials. A data of stations that enclose research areas were gathered from the National Metrology Agency map of Ethiopia. The rainfall map of the research areas was organized with GIS. The maps of research areas were divided into three annual rainfalls ordered of 900\u0026ndash;1200, 1200\u0026ndash;1500, and1500\u0026ndash;1800 millimeters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003es.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall/mm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4718.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e900\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4486.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1200\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7883.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;1800\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4998.083\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22247.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3. 7 Elevation\u003c/h3\u003e\n\u003cp\u003eTopographic-related factors such as elevations were generated from the 30m*30m Digital Elevation Model of research region. Elevations are broadly applied for evaluation of landslide susceptibility. Elevation difference might be correlated in the direction of unlike environmental situates like rainfall and plants category [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Thus, for this study the elevation was categorized in five category as shown in below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003es.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,683\u0026ndash;2,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e685.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,015.0\u0026ndash;2,213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1256.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,213.0\u0026ndash;2,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6518.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,383.0\u0026ndash;2,533\u003c/p\u003e \u003cp\u003e2,533.0\u0026ndash;2,673\u003c/p\u003e \u003cp\u003e2,673.0\u0026ndash;2,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5499.54\u003c/p\u003e \u003cp\u003e5298.39\u003c/p\u003e \u003cp\u003e2024.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.69\u003c/p\u003e \u003cp\u003e23.78\u003c/p\u003e \u003cp\u003e13.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22247.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Landslide Hazard Zonation\u003c/h2\u003e \u003cp\u003eLandslide hazard zonation mainly studies the relative of landslide hazard inducing factors, create factor analysis models and finally perform hazard zonation. Firstly, the key hazard triggering factors associated with the studied area are selected and integrated into GIS platform.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe result of the landslide hazard zonation revealed that: 7.43% or 1655 ha of the total area is high landslide risk area and 22.292% and 69.63% of the area are low and very low risk areas respect.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea Coverage of Land slide hazard zonation map.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15492.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22247.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn conclusion, this research project has successfully achieved its objectives of preparing a landslide hazard zone map of the study area using geospatial technologies. Through extensive analysis and data collection, we have identified the causative factors of landslides in the study area and have identified high-risk areas vulnerable to landslides within the wereda.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Recommendation","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.1 conclusion\u003c/h2\u003e \u003cp\u003eOur findings highlight the importance of utilizing geospatial technologies in assessing and mapping landslide hazards. By providing timely and accurate information to local authorities and communities about potential landslide hazards, we can enhance preparedness and response efforts, ultimately reducing the impact of landslides on human lives and infrastructure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Recommendation\u003c/h2\u003e \u003cp\u003eBased on our research findings, we would like to make the following recommendations:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eContinued monitoring and assessment\u003c/strong\u003e \u003cp\u003eLandslide hazards are dynamic and can change over time. We recommend implementing a continuous monitoring and assessment system using geospatial technologies to update the landslide hazard zone map regularly. This will ensure that local authorities and communities have access to up-to-date information and can take appropriate measures to mitigate risks.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCapacity building\u003c/strong\u003e \u003cp\u003eGiven the importance of geospatial technologies in assessing landslide hazards, we recommend providing training and capacity building programs to local authorities and communities. This will enable them to effectively utilize these technologies and make informed decisions regarding land use planning, infrastructure development, and emergency response.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCommunity engagement\u003c/strong\u003e \u003cp\u003eIt is crucial to engage local communities in the process of identifying high-risk areas and developing mitigation strategies. We recommend establishing community-based committees or task forces to actively participate in landslide hazard management efforts. This will foster a sense of ownership and empower communities to take proactive measures in reducing their vulnerability to landslides.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIntegration with existing systems\u003c/strong\u003e \u003cp\u003eTo maximize the effectiveness of the landslide hazard zone map, we recommend integrating it with existing disaster management systems and platforms. This will facilitate seamless information sharing and coordination among different stakeholders, enhancing overall preparedness and response capabilities.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFurther research\u003c/strong\u003e \u003cp\u003eWhile this study has provided valuable insights, there is still much more to explore in relation to landslide hazards and geospatial technologies. We recommend that future researchers build upon our findings and delve deeper into specific aspects such as the impact of climate change on landslide hazards or the development of advanced predictive models.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eBy implementing these recommendations, we believe that local authorities, communities, and researchers can collectively contribute to the mitigation of landslide hazards and the protection of lives and infrastructure in the study area.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGCPs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Ground Control Points\u003c/p\u003e\n\u003cp\u003eGIS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Geographic Information System\u003c/p\u003e\n\u003cp\u003eGPS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Global Positioning System\u003c/p\u003e\n\u003cp\u003eLHZ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Land hazard Zonation\u003c/p\u003e\n\u003cp\u003eRS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Remote Sensing\u003c/p\u003e\n\u003cp\u003eERDAS \u0026nbsp; \u0026nbsp; Earth Resource Data Analysis System\u003c/p\u003e\n\u003cp\u003eDEM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Digital Elevation model\u003c/p\u003e\n\u003cp\u003eLULC \u0026nbsp; \u0026nbsp; \u0026nbsp; Land use land cover\u003c/p\u003e\n\u003cp\u003eUSGS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;United States Geological Survey.\u003c/p\u003e\n\u003cp\u003eFAO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Food and Agriculture Organization\u003c/p\u003e\n\u003cp\u003eGII \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Geospatial Information Institute\u003c/p\u003e\n\u003cp\u003eNMA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;National Metrology Agency\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials used for analysis in this manuscript are available at the corresponding author. It is possible to reasonably request the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no fund provided to publish the research work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst and second authors contributed by conducting research and writing the manuscript. The other authors contributed by arranging, organizing, and directing the article starting from research idea up to manuscript full write-up. All authors reviewed the results and agreed the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst of all we would like to thanks our Almighty God for his tireless protection. We would like to express our deepest gratitude to Wachemo University for providing opportunity to conduct our research. Without their generous support, this research project would not have been possible. Lastly, we would like to express our gratitude to all those who have directly or indirectly contributed to the completion of this research project..\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eA. S. Dhakal, T. Amada, and M. Aniya, \u0026ldquo;Landslide Hazard Mapping and its Evaluation Using GIs : An Investigation of Sampling Schemes for a Grid-Cell Based Quantitative Method,\u0026rdquo; vol. 66, no. 8, pp. 981\u0026ndash;989, 2000.\u003c/li\u003e\n \u003cli\u003eR. S. Negi, M. K. Parmar, Z. A. Malik, and M. Godiyal, \u0026ldquo;Landslide Hazard Zonation using Remote Sensing and GIS : A Case Study of Giri Valley , District Sirmaur Himachal Pradesh,\u0026rdquo; vol. 1, no. 1, pp. 26\u0026ndash;39, 2012.\u003c/li\u003e\n \u003cli\u003eK. Sassa and P. Canuti, \u003cem\u003eLandslides \u0026ndash; Disaster Risk Reduction\u003c/em\u003e. 2009. doi: 10.1007/978-3-540-69970-5.\u003c/li\u003e\n \u003cli\u003eN. Dong, Z. Liu, M. Luo, C. Fang, and H. Lin, \u0026ldquo;The Effects of Anthropogenic Land Use Changes on Climate in China Driven by Global Socioeconomic and Emission Scenarios,\u0026rdquo; \u003cem\u003eEarth\u0026rsquo;s Futur.\u003c/em\u003e, vol. 7, no. 7, pp. 784\u0026ndash;804, 2019, doi: 10.1029/2018EF000932.\u003c/li\u003e\n \u003cli\u003e\u0026ldquo;Varnes, D. J. (1984).pdf.\u0026rdquo;\u003c/li\u003e\n \u003cli\u003eS. Kanwal, S. Atif, and M. Shafiq, \u0026ldquo;GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins,\u0026rdquo; \u003cem\u003eGeomatics, Nat. Hazards Risk\u003c/em\u003e, vol. 8, no. 2, pp. 348\u0026ndash;366, 2017, doi: 10.1080/19475705.2016.1220023.\u003c/li\u003e\n \u003cli\u003eK. Woldearegay, \u0026ldquo;Review of the occurrences and influencing factors of landslides in the highlands of Ethiopia : With implications for infrastructural development Review of the occurrences and influencing factors of landslides in the highlands of Ethiopia : With implicatio,\u0026rdquo; no. April, 2019, doi: 10.4314/mejs.v5i1.85329.\u003c/li\u003e\n \u003cli\u003eK. Woldearegay, \u0026ldquo;Review of the occurrences and influencing factors of landslides in the highlands of Ethiopia: With implications for infrastructural development,\u0026rdquo; \u003cem\u003eMomona Ethiop. J. Sci.\u003c/em\u003e, vol. 5, p. 3, Feb. 2013, doi: 10.4314/mejs.v5i1.85329.\u003c/li\u003e\n \u003cli\u003eM. Mengistu and A. Senamaw, \u0026ldquo;Remote Sensing and GIS Based Potential Landslide Hazard Zonation in Ambo Woreda : Central Ethiopia,\u0026rdquo; vol. 10, no. 2, pp. 19\u0026ndash;27, 2020, doi: 10.7176/JEES/10-2-03.\u003c/li\u003e\n \u003cli\u003eL. Ayalew, \u0026ldquo;The effect of seasonal rainfall on landslides in the highlands of Ethiopia,\u0026rdquo; \u003cem\u003eBull. Eng. Geol. Environ.\u003c/em\u003e, vol. 58, no. 1, pp. 9\u0026ndash;19, 1999, doi: 10.1007/s100640050065.\u003c/li\u003e\n \u003cli\u003eA. Genene and M. Meten, \u0026ldquo;Landslide Susceptibility Mapping Using GIS-based Information Value and Frequency Ratio Methods in Gindeberet area, West Shewa Zone, Oromia Region, Ethiopia,\u0026rdquo; 2021, [Online]. Available: http://dx.doi.org/10.21203/rs.3.rs-219331/v1\u003c/li\u003e\n \u003cli\u003eTamirat, J. A. Ababulgu, and E. T. Wolde, \u0026ldquo;Adoption and Impact of Row Planting of Wheat Crop on Household Livelihood : - A Case Study of Duna Woreda in Hadiya Zone , Ethiopia . Adoption and Impact of Row Planting of Wheat Crop on Household Livelihood : - A Case Study of Duna Woreda in Hadiya Zone,\u0026rdquo; \u003cem\u003eThesis\u003c/em\u003e, pp. 1\u0026ndash;22, 2017.\u003c/li\u003e\n \u003cli\u003eJ. S. Oromiyaa and O. L. Journal, \u0026ldquo;LAND GOVERNANCE IN ETHIOPIA: TOWARDS EVALUATING GLOBAL TRENDS Daniel Behailu * Adisu Kasa **,\u0026rdquo; vol. 7, no. 1, pp. 117\u0026ndash;149, 2018.\u003c/li\u003e\n \u003cli\u003eA. Clerici, \u0026ldquo;A GRASS GIS based shell script for landslide susceptibility zonation by the conditional analysis method,\u0026rdquo; \u003cem\u003eProc. Open source GIS\u0026ndash;GRASS users Conf. 2002\u003c/em\u003e, vol. 48, no. September, pp. 1\u0026ndash;17, 2002.\u003c/li\u003e\n \u003cli\u003eG. Ntelis, S. Maria, and L. Efthymios, \u0026ldquo;Landslide Susceptibility Estimation Using GIS. Evritania Prefecture: A Case Study in Greece,\u0026rdquo; \u003cem\u003eJ. Geosci. Environ. Prot.\u003c/em\u003e, vol. 07, no. 08, pp. 206\u0026ndash;220, 2019, doi: 10.4236/gep.2019.78015.\u003c/li\u003e\n \u003cli\u003eR. C. Sidle and H. Ochiai, \u0026ldquo;Landslides: Processes, Prediction, and Land Use,\u0026rdquo; \u003cem\u003eLandslides Process. Predict. L. Use\u003c/em\u003e, vol. 18, no. June, pp. 1\u0026ndash;312, 2013, doi: 10.1029/WM018.\u003c/li\u003e\n \u003cli\u003eA. Akgun, C. Kıncal, and B. Pradhan, \u0026ldquo;Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey),\u0026rdquo; \u003cem\u003eEnviron. Monit. Assess.\u003c/em\u003e, vol. 184, no. 9, pp. 5453\u0026ndash;5470, 2012, doi: 10.1007/s10661-011-2352-8.\u003c/li\u003e\n \u003cli\u003eJ. Dou \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan,\u0026rdquo; \u003cem\u003ePLoS One\u003c/em\u003e, vol. 10, no. 7, p. e0133262, Jul. 2015, [Online]. Available: https://doi.org/10.1371/journal.pone.0133262\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"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":"Landslide, Duna Woreda, GIS and remote sensing","lastPublishedDoi":"10.21203/rs.3.rs-4549060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4549060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe main purpose of this study is to assess landslide hazard zones for Duna wereda by using Geospatial technology. The causative factors considered to landslide analysis in the area was, slope, aspect, distance from water, soil type, land use land cover and Elevation. Analytical Hierarchy Processes the weight of each factor were calculated and assigned in GIS. To add these factors and produce landslide hazard map weighted linear combination was, used.by using IDRIS software. Thus, by using obtained weight value Landslide hazard map prepared was, categorized in to high, low and very low hazard zone. Therefore the result of the study revealed that the area coverage of the level of landslide risk very low and high are 69.63%,22.94% and 7.43% respectively. As identified there is high landslide risk area that may cause different6 effect on the human being and on property. Therefore The result of analysis were verified using landslide inventory data this study may have great importance in giving awareness in landslide hazard area and also helps to mitigate the landslide impact. Finally, this landslide hazard zonation of Duna wereda suggested for further researcher, land management and concerned body should have based on the present finding.\u003c/p\u003e","manuscriptTitle":"Landslide hazard zone mapping Using Geospatial Technologies: A Case Study of Duna Wereda Hadiya Zone, Central Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 17:26:08","doi":"10.21203/rs.3.rs-4549060/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"52cfb595-8e64-4d1f-87fb-48646214346e","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-27T02:38:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 17:26:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4549060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4549060","identity":"rs-4549060","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}