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This paper presents rainfall-induced landslide thresholds and predicts landslide hazard in Kuala Lumpur metropolitan city and surrounding areas. Landslide events from 2000 to 2012 were collected. The long and short antecedent rainfall days were prepared for landslide and non-landslide days simultaneously. First, threshold analysis was conducted by using data obtained from rainfall stations located in highly urbanized areas of Kuala Lumpur metropolis. Six rainfall gauges were selected, and the study area was divided into six zones according to rainfall gauges: Taman Desa Station (TD-KL), Genting Klang (GK-KL), LDG Edinburgh Station (LDGE-KL), SG Raya Hulu Langat Station (SRHL-Slg), Puchong Drop Station (PD-Slg), and Bukit Antarabangsa (BTA-Slg). After the threshold analysis was conducted for different periods (10, 15, and 30 days) in each station, reliability index test was conducted to optimize the best threshold that limits the predicted events along the study period for each region. Second, the threshold analysis results were used as input in the Poisson probability model to estimate landslide temporal probability ( P). Third, the spatial probability ( P) analysis was prepared by using the evidential belief function multiplied by the P to obtain the hazard maps for 1-, 3-, and 5-year scenarios. Finally, a validation process was conducted to test the prediction performance of the resultant hazard map for a 1- and 2-year prediction by using the landslide inventory of 2012 to early 2014, which was not included in the modeling of the hazard map. Results showed a valid correlation between the high and moderate hazardous areas for the six zones. The predicted hazard maps indicated a quantitative assessment of the prone areas and proved to be a valid disaster management tool. The produced hazard maps may play a vital role as input component in risk analysis. [ABSTRACT FROM AUTHOR]

Landslide susceptibility (LS) assessment by indirect approaches presents some limitations due to (1) the tendency to simplify the environmental factors (i.e., variables) and (2) the assumptions that landslides occur under the same combination of variables for a study site. Recently, some authors have discussed the interest to introduce expert knowledge in the indirect approaches in order to improve the quality of indirect LS maps. However, if the results are reliable, the procedures used seem fastidious and a very good knowledge of the study site is essential. The objectives of this paper are to discuss a methodology to introduce the expert knowledge in the indirect mapping process. After the definition of the expert rules associated to three landslide types, several indirect LS maps are produced by two indirect exploratory approaches, based on fuzzy set theory and on a modification of a bivariate method called expert weight of evidence. Then, the indirect LS maps are confronted to a landslide inventory and a LS map produced by a direct approach. The analyses indicate that the methodology used to introduce the expert rules in the mapping process increases the predictive power of indirect LS map. Finally, some indications about advantages and drawbacks of each approach are given to help the geoscientist to introduce his expert knowledge in the landslide susceptibility mapping process. [ABSTRACT FROM AUTHOR]

The hilly region of Manipur especially along the NH-39 road, which is the lifeline of the State, is prone to landslides every year particularly during the monsoon season. Anthropological factors, such as excessive deforestation, unsystematic changes in land use and land cover pattern and slope cultivation, etc. are indirectly initiate the process of landslides. In the present study, landslide susceptibility mapping was carried out using frequency ratio and fuzzy gamma operator models with the help of geomatics techniques. The landslide susceptibility mapping was prepared using landslide inventory data and nine landslide causative factors, i.e. lithology, land use and land cover, geomorphology, drainage density, lineament density, slope gradient, slope aspect, curvature, and elevation. These causative factors were prepared with the help of toposheet, high resolution IRS P6 LISS IV satellite imagery, cartosat DEM data and extensive field work. The landslide susceptibility maps were prepared by calculating the relationship between the landslide causative parameters with landslide areas using a frequency ratio model. To get the fuzzy membership values, the frequency ratio values were normalized between the ranges of 0 and 1. The landslide susceptibility maps were compared and prediction accuracy of both the models was derived using the area under curve (AUC) method. The success rate curves were obtained using both training and all landslide inventory dataset. For training landslide inventory dataset, the AUC value of the success rate curve for the frequency ratio model was found to be 0.8056, whereas for the fuzzy gamma operator (using γ = 0.99) model, it was calculated as 0.9150. In the case of all landslide inventory dataset, the AUC value of the success rate curve for the frequency ratio model and the fuzzy gamma operator model were 0.7921 and 0.8188, respectively. The landslide susceptibility index was also compared with the landslide validation inventory dataset to obtain the prediction rate curves. The AUC value of the prediction rate curve for the frequency ratio model was 0.5681, whereas in the case of the fuzzy gamma operator model, it was 0.6721. [ABSTRACT FROM AUTHOR]