Landslide Hazard: Risk Zonation and Impact Wave Analysis for the Bumbuma Dam—Sierra Leone

The rocks in the Thankot–Chalnakhel area constitute the Chandragiri Range bordering the Kathmandu valley. The Phulchauki Group of rocks comprise its steep and rugged south slope, whereas the gentle north slope is covered by fluvio-lacustrine deposits of the Kathmandu basin with some recent alluvial fans. During the field study, 94 landslides (covering about 0.24 sq km) were mapped. Most of them were triggered by intense rainfall within the last two years. Landslides are generally found on steep colluvial slope (25°–35°) and dry cultivated land. Based on a computer-based geographical information system, a landslide hazard map, a vulnerability map, and a risk map were prepared. The landslide hazard map shows 20% of the area under high hazard zone, 41% under moderate hazard zone, and 39% under low hazard zone. The risk map generated by combining the hazard map and vulnerability map shows 19% of the area under high and very high risk zones, 33% under moderate risk zone, and 48% under low and very low risk zones.

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Landslide hazard risk modeling in north-west of Iran using optimized machine learning models

Modeling Earth Systems and Environment ◽

10.1007/s40808-020-00871-1 ◽

2020 ◽

Author(s):

Ali Jamali

Keyword(s):

Machine Learning ◽

Landslide Hazard ◽

Risk Modeling ◽

Learning Models ◽

North West ◽

Hazard Risk ◽

Machine Learning Models

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Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110695 ◽

2020 ◽

Vol 9 (11) ◽

pp. 695

Author(s):

Yang Zhang ◽

Weicheng Wu ◽

Yaozu Qin ◽

Ziyu Lin ◽

Guiliang Zhang ◽

...

Keyword(s):

Human Life ◽

Landslide Hazard ◽

Landslide Risk ◽

Disaster Prevention ◽

Risk Modeling ◽

Prevention Measures ◽

Training Set ◽

Hazard Risk ◽

Calculated Probability ◽

Validation Set

Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

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Assessing Risk Economically on Landslide in Youyu County, Shanxi Province

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1051.779 ◽

2014 ◽

Vol 1051 ◽

pp. 779-785

Author(s):

Wen Xin Hao ◽

Li Jie Wang ◽

Feng Ding ◽

Jia Guo

Keyword(s):

Economic Loss ◽

Structure Type ◽

Landslide Hazard ◽

Shanxi Province ◽

Ecological Environment ◽

Quantitative Indices ◽

Hazard Risk ◽

Slope Structure ◽

Deformation Features ◽

Geological Background

This paper studies landslide and background in Youyu County, Shanxi Province and provides geological bases for the economic and social development, ecological environment construction and protection. The authors investigated the environmental geological background, collected and arranged the information in this field. By analyzing Landslide-causing factors, the area is divided into four types: high susceptible area, middle susceptible are, low susceptible area, difficult susceptible area. A map of Landslide hazard susceptible degree is made, based on the studies of the quantitative indices (formation lithology, slope structure type, gradient, rainfall, slope height, engineering activities, slope deformation features, death and threatened toll, direct and indirect economic loss and so on.).And the Landslide hazard risk of the area is comprehensively assessed.

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Multi-Hazard Risk Zonation Based on Functions Responsible for Hazards Like Landslides, Floods, Forest Fires and Earthquakes in Mandakini Valley

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e5602.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 3874-3885

Keyword(s):

Risk Assessment ◽

Forest Fires ◽

Disaster Risk ◽

Climatic Conditions ◽

Scientific Models ◽

Tectonic Activity ◽

Hazard Zonation ◽

Extreme Rainfall Events ◽

Hazard Risk ◽

Risk Zonation

The Himalayas are considered youngest mountain on Earth. Region is highly vulnerable to hazards because of tectonic activity, steep slopes, highly variable altitudes and uncertain climatic conditions. As a result, key hazards experienced in the region are earthquakes, landslides, forest fires, snow/ice avalanches, flash floods and extreme rainfall events which lead to great losses to human lives and property every year. The aim of study is to find most vulnerable area in terms of multi-hazards as per UNISDR guidelines. Here, GIS based techniques were used for disaster risk assessment towards various hazards and then integrating vulnerable areas with demography to perform detailed multi-hazard zonation of the area. Various Geo-spatial and statistical techniques were used in analysis of different types of disaster risk, determining the factors affecting incidents and in preparation of multi-hazard risk maps. The work involved the qualitative study, through in depth scientific observations, study available models for early warnings, develop models using sample data and generate multi-hazard vulnerability of study area. Using advanced geo-spatial techniques, Hazard Zonation maps were generated for different hazards in the Study Area. These maps were overlaid with Socio-economic and Demographic Profile of the habitations in the study area and multi-hazard risk assessment maps were generated. On the basis of complete geo-spatial analytics and scientific models, it was derived that 74 + villages are highly prone to various disaster. Scripts were written to automate various processes. Results were verified and validated during field visits.

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