Weights of Evidence Modeling for Landslide Susceptibility Mapping of Kabi-Gebro Locality, Gundomeskel area, Central Ethiopia

Agricultural Land ◽

Google Earth ◽

Weights Of Evidence ◽

Susceptibility Map ◽

Data Set ◽

Causative Factors ◽

Central Highland ◽

The Impact

Abstract Kabi-Gebro area is located within the Abay Basin at Dera District of North Shewa Zone near Gundomeskel town in the Central highland of Ethiopia and it is about 320 Km from Addis Ababa. This is characterized by undulating topography, intense rainfall, active erosion and highly cultivated area. Geologically characterized by weathered sedimentary and volcanic rocks. Currently, landslides are creating serious challenges in road construction, farming practices and affecting people in this area. Active landslides in this area damaged the gravel road, houses and agricultural land. The main objective of this research is to prepare the landslide susceptibility map. To overcome the landslide problem in this area, landslide susceptibility map was prepared using GIS- based Weights of Evidence model. Based on detailed field assessment and Google Earth image interpretation, 514 landslide locations were identified and classified randomly as training landslide (80%) and validation landslide (20%). The training landslide data set include nine landslide causative factors such as lithology, slope angle, aspect, curvature, land use/land cover, distance to stream, distance to lineament, distance to spring and rainfall inorder to prepare landslide susceptibility map in this study. The landslide susceptibility maps were prepared by adding the weights of contrast values of the nine causative factors using rater calculator in the spatial analyst tool of ArcGIS. The final landslide susceptibility map was reclassified as very low, low, moderate, high and very high landslide susceptiblity classes. This susceptibility map was validated using landslide density index and Area Under the Curve (AUC). The result from this validation showed a success rate and avalidaton rate accuracies of 82.4% and 83.4% respectively for this model. Finally, this study recommends application of appropriate mitigation or corrective measures in order to lessen the impact of landslide in the area.

Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia

Geoenvironmental Disasters ◽

10.1186/s40677-021-00177-z ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Nega Getachew ◽

Matebie Meten

Keyword(s):

Google Earth ◽

Weights Of Evidence ◽

Slope Aspect ◽

Susceptibility Map ◽

Causative Factors ◽

Central Highland ◽

Slide Debris ◽

The Impact

AbstractKabi-Gebro locality of Gundomeskel area is located within the Abay Basin at Dera District of North Shewa Zone in the Central highland of Ethiopia and it is about 320Km from Addis Ababa. This is characterized by undulating topography, intense rainfall, active erosion and highly cultivated area. Geologically, it comprises weathered sedimentary and volcanic rocks. Active landslides damaged the gravel road, houses and agricultural land. The main objective of this research is to prepare the landslide susceptibility map using GIS-based Weights of Evidence model. Based on detailed field assessment and Google Earth image interpretation, 514 landslides were identified and classified randomly into training landslides (80%) and validation landslides (20%). The most common types of landslides in the study area include earth slide (rotational and translational slide), debris slide, debris flow, rock fall, topple, rock slide, creep and complex. Nine landslide causative factors such as lithology, slope, aspect, curvature, land use/land cover, distance to stream, distance to lineament, distance to spring and rainfall were used to prepare a landslide susceptibility map of the study area by adding the weights of contrast values of these causative factors using a rater calculator of the spatial analyst tool in ArcGIS. The final landslide susceptibility map was reclassified as very low, low, moderate, high and very high susceptibility classes. This susceptibility map was validated using landslide density index and area under the curve (AUC). The result from this model validation showed a success rate and a validation rate accuracy of 82.4% and 83.4% respectively. Finally, implementing afforestation strategies on bare land, constructing surface drainage channels & ditches, providing engineering reinforcements such as gabion walls, retaining walls, anchors and bolts whenever necessary and prohibiting hazardous zones can be recommended in order to lessen the impact of landslides in this area.

THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA

Geoplanning Journal of Geomatics and Planning ◽

10.14710/geoplanning.4.2.157-170 ◽

2017 ◽

Vol 4 (2) ◽

pp. 157 ◽

Cited By ~ 2

Author(s):

Andang Suryana Soma ◽

Tetsuya Kubota

Keyword(s):

Land Use ◽

Land Use Change ◽

Time Series Data ◽

Google Earth ◽

Series Data ◽

Susceptibility Map ◽

Causative Factors ◽

Predictive Rate

The study aims to develop and apply land use change (LUC) performance on landslide susceptibility map using frequency ratio (FR), and Logistic regression (LR) method in a geographic information system. In the study area, Upper Ujung-loe Watersheds area of Indonesia, landslides were detected using field survey and air photography from time series data image of Google Earth Pro from 2012 to 2016 and LUC from 2004 to 2011. Landslide susceptibility map (LSM) was constructed using FR and LR with nine causative factors. The result indicated that LUC affect the production of LSM. Validation of landslide susceptibility was carried out in this study at both with and without LUC causative factors. First, performances of each landslide model were tested using AUC curve for success and predictive rate. The highest value of predictive rate at with LUC in both FR and LR method were 83.4 % and 85.2 %, respectively. In the second stage, the ratio of landslides falling on high to a very high class of susceptibility was obtained, which indicates the level of accuracy of the method.LR method with LUC had the highest accuracy of 80.24 %. Taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence.

Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-loe watershed in South Sulawesi

Forest and Society ◽

10.24259/fs.v2i1.3594 ◽

2018 ◽

Vol 2 (1) ◽

pp. 79 ◽

Cited By ~ 2

Author(s):

Andang Suryana Soma ◽

Tetsuya Kubota

Keyword(s):

Causative Factor ◽

Susceptibility Map ◽

Landslide Occurrence ◽

Sparse Vegetation ◽

Dense Vegetation ◽

South Sulawesi ◽

Landslide Hazards ◽

Causative Factors

This study aims to build a landslide susceptibility map (LSM) by using certainty factor (CF) models for mitigation of landslide hazards and mitigation for people who live near to the forest. In the study area, the mountainous area of the Ujung-loe watersheds of South Sulawesi, Indonesia, information on landslides were derived from aerial photography using time series data images from Google Earth Pro© from 2012 to 2016 and field surveys. The LSM was built by using a CF model with eleven causative factors. The results indicated that the causative factor with the highest impact on the probability of landslide occurrence is the class of change from dense vegetation to sparse vegetation (4-1), with CF value 0.95. The CF method proved to be an excellent method for producing a landslide susceptibility map for mitigation with an area under curve (AUC) success rate of 0.831, and AUC predictive rate 0.830 and 85.28% of landslides validation fell into the high to very high class. In conclusion, correlations between landslide occurrence with causative factors shows an overall highest LUC causative factor related to the class of change from dense vegetation to sparse vegetation, resulting in the highest probability of landslide occurrence. Thus, forest areas uses at these locations should prioritize maintaining dense vegetation and involving the community in protection measures to reduce the occurrence of landslide risk. LSM models that apply certainty factors can serve as guidelines for mitigation of people living in this area to pay attention to landslide hazards with high and very high landslide vulnerability and to be careful to avoid productive activities at those locations.

Evaluating the impact of road construction on landslide susceptibility- A case study of Mandi district, Himachal Pradesh, India.

10.22541/au.162132838.80285009/v1 ◽

2021 ◽

Author(s):

Amol Sharma ◽

Chander Prakash

Keyword(s):

Statistical Models ◽

Road Construction ◽

Himachal Pradesh ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

The Impact ◽

Very High

Landslide susceptibility mapping has proved to be crucial tool for effective disaster management and planning strategies in mountainous regions. The present study is perused to investigate the changes in the landslide susceptibility of the Mandi district of Himachal Pradesh due to road construction. For this purpose, an inventory of 1723 landslides was generated from various sources. Out of these, 1199 (70%) landslides were taken in the training dataset to be used for modelling and prediction purposes, while 524 (30%) landslides were taken in the testing dataset to be used for validation purposes. Eleven landslide causative factors were selected from numerous hydrological, geological and topographical factors and were analyzed for landslide susceptibility mapping using three bivariate statistical models, namely; Frequency Ratio (FR), Certainty Factor (CF) and Shanon Entropy (SE). Two sets of LSM maps i.e. landslide susceptibility map natural (LSMN) and landslide susceptibility map road (LSMR), were generated using the above mentioned bivariate models and were divided into five landslide susceptibility classes namely; very low, low, medium, high and very high. These maps were analyzed for accuracy of prediction and validation using receiver operating characteristic (ROC) curves and area under curve (AUC) technique which indicated that all three bivariate statistical models performed satisfactorily with the SE model had the highest prediction and validation accuracy of 83-86%. Further analysis LSM maps confirmed that the percentage area in high and very high classes of land-slide susceptibility increased by 2.67-4.17% due to road construction activities in the study area.

Landslide Susceptibility in Majalengka Regency, West Java Province

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/884/1/012053 ◽

2021 ◽

Vol 884 (1) ◽

pp. 012053

Author(s):

S Selaby ◽

E Kusratmoko ◽

A Rustanto

Keyword(s):

Public Works ◽

Disaster Mitigation ◽

Susceptibility Map ◽

Multi Criteria Evaluation ◽

Susceptibility Maps ◽

The Government ◽

The Impact ◽

Landslide Susceptibility Maps

Abstract Majalengka is one of districts in Indonesia which is susceptible to landslides. Landslides in Majalengka caused enormous losses such as damage to infrastructure, loss of property, and even human fatalities. Seeing of the impact, mitigation efforts are needed to reduce risks and losses by making landslide susceptibility maps. This study aims to map areas landslide susceptibility and as a reference for the government and related agencies to reduce losses. The method used overlay using Spatial Multi-Criteria Evaluation (SMCE), using weighting values from the Minister Public Works Regulation NO.22/PRT/M/2007, Puslittanak Bogor (2014) and Directorate Volcanology and Disaster Mitigation (DVMBG) (2004). Then comparison of these sources is carried out to determine weighting value with the highest accuracy. The variables are slope, rainfall, soil type, lithology, and land use. The results of this study indicate that landslide susceptibility areas are divided into non-susceptible, low, moderate, and high areas. Where areas Majalengka Regency is dominated by moderate susceptibility level. For the accuracy value of the landslide susceptibility map produced by the weighted value source from the Minister of Public Works Regulation NO.22/PRT/M/2007 has the highest accuracy value of 76%. For weighting from the Bogor Puslittanak is 73%, while weighting source from DVMBG is 68%.

ANALISIS TINGKAT KERAWANAN TANAH LONGSOR MENGGUNAKAN METODE FREKUENSI RASIO DI DAERAH ALIRAN SUNGAI BIALO

PERENNIAL ◽

10.24259/perennial.v15i1.6317 ◽

2019 ◽

Vol 15 (1) ◽

pp. 42

Author(s):

Nurul Fadilah ◽

Usman Arsyad ◽

Andang Suryana Soma

Keyword(s):

Frequency Ratio ◽

High Accuracy ◽

The Other ◽

Susceptibility Map ◽

High Susceptibility ◽

Causative Factors ◽

Very High ◽

Slope Direction

Landslide is a movement of soil with slope direction and moves it on a slide. This study aimed to predict the landslide susceptibility map by using a frequency ratio. It used seven causative factors, such as slope, curvature, land use, lithology, distance to a river, distance to lineament, and rainfall. The result showed the AUC of success rate and predicted rate produced high accuracy with 0.907 and 0.904, respectively. According to the frequency ratio, the slope was the most influential than the other causative factors with 7.15. The landslide susceptibility divided into five classes, i.e. very low, low, moderate, high, and very high. Landslide susceptibility with very high and high was 19%. Moreover, classes susceptibility of very low, low, and moderate were 71%. The presentation of very high and high susceptibility is low, but it was located on an upper stream, and it will be a danger if to the downstream.

Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030114 ◽

2021 ◽

Vol 10 (3) ◽

pp. 114

Author(s):

Amit Kumar Batar ◽

Teiji Watanabe

Keyword(s):

Land Use ◽

Susceptibility Mapping ◽

Landslide Inventory ◽

Susceptibility Map ◽

Future Directions ◽

Causative Factors ◽

Recent Developments ◽

Very High

The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.