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This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/ geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network. [ABSTRACT FROM AUTHOR]

The present work aimed to assess the feasibility of three methods, namely, analytical hierarchy process, frequency ratio and binary logistic regression, that are used for landslide susceptibility analysis. Twelve causal factors such as slope, relative relief, road buffer, fault/fold and thrust (FFT) buffer, topographic wetness index (TWI), aspect, stream power index (SPI), drainage buffer, profile curvature, land used/land cover (LULC), soil and geology as well as a landslide inventory were retrieved using multi-source spatial data. Correlation between landslides and causative factors was estimated using frequency ratio on a geospatial platform with 234 landslide locations, extracted using field visits and remote sensing imageries and found to be reasonably influencing landslides in the study region. These factors were then used in all three landslide susceptibility models to calculate and generate a landslide susceptibility map (LSM). LSM of the study region Mao-Maram (tehsil) of Manipur was generated with five classes: very low, low, medium, high, and very high. Cumulative percentage curve and receiver operating characteristic (ROC) approaches were used for validation of the model performance. Cumulative percentage curve area under curve (AUC) accuracy for frequency ratio, analytical hierarchy process and binary logistic regression were found to be 77.24%, 73.22%, and 80.32%, respectively. ROC curve under AUC for frequency ratio 88.7%, analytical hierarchy process 77.4%, and binary logistic regression 90%. A landslide susceptibility map derived from every three models can be helpful for city or village planners for planning, development, mitigation, and disaster prevention. [ABSTRACT FROM AUTHOR]

Landslide susceptibility maps are modelled either by knowledge-driven methods (qualitative) or data-driven methods (quantitative) depending on apriori expert knowledge or complete landslide inventory data, respectively, which hinders land regulatory authorities from taking quick decisions for land use planning at a regional scale. To overcome these limitations, a new modelling methodology is proposed which can perform equally with the existing established models and needs minimum knowledge of geo-environmental variables without any model testing data. In this method, common variances of the geo-environmental variables are extracted from initially equally weighted variables and are termed as equal weight method (EWM). The extracted common variances are used to weight the variables for the preparation of a landslide susceptibility map on a regional scale. The output of the model can be interpreted to understand the inter-relationship of different geo-environmental variables along with the identification and prioritization of variables on model performance by delineating the non-performing variables. The proposed modelling methodology is tested at the eastern Himalayan road corridor, the result of which is compared with the results of the analytical hierarchy process (qualitative) and frequency ratio (quantitative) modelling methods by area under curve (AUC) of receiver operating characteristic (ROC) curves and cost analysis. AUC of ROC curves for the analytical hierarchy process, frequency ratio method and equal weight method models are 0.755, 0.809 and 0.774, respectively, indicating little performance differences. Cost analysis of all models shows a maximum bounding cost of 0.4 indicating that all models are cost-effective. The study concludes that the equal weight method is quick, robust and cost-effective at par with other well tested knowledge-driven and data-driven methods. [ABSTRACT FROM AUTHOR]

Landslide and related mass movement activities are common and one of the most destructive natural hazards in the mountainous terrain including the Himalayas. Of the 11 administrative states in the Indian Himalayan region, the state of Uttarakhand has witnessed enhanced activities of these phenomena. It is therefore essential to understand the regional scale landslide susceptibility assessment of the state and in the present study, landslide susceptibility mapping for the entire state has been carried out using bivariate weight of evidence and information value methods which depict that around 51% of the area is located in the high and very high landslide susceptible zones, 22–23% in the moderate and ~26–27% in the low and very low landslide susceptible zones, and slopes ranging between 40° and 60°, located at an elevation of 2000–4000 m, facing towards southern sides and covered with limestone, gneiss, quartzite and phyllite, have higher propensity towards development of landslides in the region. [ABSTRACT FROM AUTHOR]