Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia

Shano, Leulalem; Raghuvanshi, Tarun Kumar; Meten, Matebie

doi:10.1007/s12517-021-06995-7

Cited by 62 publications

(15 citation statements)

References 82 publications

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“…The highest negative correlation was found in the buffer zone greater than 500 m of stream in both models' results. Steep slopes along the stream and consistent lateral erosion destabilize the slopes and cause landslides (Shano et al, 2021). In the study area, the stream undercut the banks that result in sloughing and stimulate slope failures.…”

Section: Proximity To Stream/torrentmentioning

confidence: 93%

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

et al. 2022

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This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 12.84% according to the WoE and IV models, respectively. Finally, the resultant maps were validated using the success and prediction rate curves, seed cell area index (SCAI), and R-index approaches. The success rate curve validated the results at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV was 83.34 and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined, and the risk map of the study area was generated. According to the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a very high-risk zone. It was found from the analysis that for assessing landslide susceptibility, both the models are suitable and applicable in the Hindu Kush region.

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Section: Proximity To Stream/torrentmentioning

confidence: 93%

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

et al. 2022

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“…If FR = 1, then FR is the average. If FR >1, then FR is superior to the average (Shano et al, 2021).…”

Section: Modelling and Mapping Of S Arvensis Using The Fr And Wofementioning

confidence: 99%

An evaluation of alternative statistical models for predicting habitat suitability for weeds

et al. 2022

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Sinapis arvensis (wild mustard) is a competitive weed of oilseed rape in Iran. The spatial distribution of S. arvensis and its habitat suitability zonation in Fars Province, Iran, was studied using frequency ratio (FR) and weights-of-evidence (WofE) statistical and probabilistic models in a geographic information system. For this purpose, a dataset was prepared of weed presence/absence in oilseed rape fields across Fars Province and measures of different effective factors, including elevation, distance from roads, distance from rivers, pH, electrical conductivity, mean annual temperature, mean annual rainfall, slope degree, slope aspect, plan curvature and physical properties of the soil were collected from the sites. Boruta machine learning method was used to determine the significance of each variable that was used to map the geographical distribution of wild mustard and the appropriateness of its habitat. Using the area under the curve and receiver operating characteristic, the accuracy of the habitat suitability zonation maps generated from the FR and WofE models was assessed (AUC-ROC). The results indicated that AUC-ROC values for the FR and WofE models were 91% and 88% respectively. FR and WofE are good methods for predicting the likelihood of S. arvensis occurring in a oilseed rape field. Finally, the resulting weed distribution map can aid decision makers and managers in identifying areas that require enhanced management in future plans.

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“…Landslides are more widespread than any other geological event and can occur anywhere in the world. Tey occur when large masses of soil, rocks, or debris move down a slope due to the efect of a natural phenomenon or human activity [5,6].…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Landslide Susceptibility Mapping Using Frequency Ratio and Shannon Entropy Models in Dejen District, Northwestern Ethiopia

Addis

2023

Journal of Engineering

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A GIS-based study has been carried out to map areas landslide susceptibility using both frequency ratio (FR) and Shannon entropy (SE) bivariate statistical models. A total of 270 landslides were identified and classified randomly into training landslides datasets (70%) and the remaining (30%) of landslides datasets were used for validation purpose. The 11 landslides conditioning factors like slope, elevation, aspect, curvature, topographic wetness index, normalized difference vegetation index, distance from road, distance from river, distance from faults, land use, and rainfall were integrated with training landslides to determine the weights of each landslide conditioning factor and factor classes using both frequency ratio and Shannon entropy models. The landslide susceptibility maps were produced by overlay the weights of all the landslide conditioning factors using raster calculator of the spatial analyst tool in ArcGIS 10.4. The final landslide susceptibility maps were reclassified as very low, low, moderate, high, and very high susceptibility classes both FR and SE models. This susceptibility maps were validated using landslide area under the curve (AUC). The results of AUC accuracy models showed that the success rates of the FR and SE models were 0.761 and 0.822, while the prediction rates were 0.753 and 0.826, respectively.

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Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia

Cited by 62 publications

References 82 publications

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

An evaluation of alternative statistical models for predicting habitat suitability for weeds

GIS-Based Landslide Susceptibility Mapping Using Frequency Ratio and Shannon Entropy Models in Dejen District, Northwestern Ethiopia

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