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The purposes of this study is to create a landslide susceptibility map (LSM) for Lompobattang Mountain area in Indonesia. The foot of the Lompobattang Mountain area suffered flash flood and landslides in 2006, which led to significant adverse impact on the nearby settlements. There were 158 identified landslides covering a total area of 3.44 km 2 . Landslide inventory data were collected using google earth image interpretations. The landslide inventories were prepared out of the past landslide events, and future landslide occurrence was predicted by correlating landslide causal factors. In this study landslide inventories were divided into landslide data for training and landslide data for validation. The LSM was prepared by Frequency Ratio (FR) and Logistic Regression (LR) statistical methods. Lithology, distance from the road, distance from the river, distance from the fault, land use, curvature, aspect, and slope degree were used as conditioning parameters. Area under the curve (AUC) of the Receiver Operating Characteristic (ROC) was used to check the performance of the models. In the analysis, the FR model results in 85.8 % accuracy in the AUC success rate while the LR model was found to have 86.9 % accuracy. However, the accuracy of both these models in AUC predictive rate is the same at around 85.1 %. The LR model is 6.34 % higher than the FR model in comparison to its accuracy for ratio of landslide validation. The landslide susceptibility map consist of the predicted landslide area, hence it can be used to reduce the potential hazard associated with the landslides in this study area.

Background: Nepal is highly vulnerable to natural disasters. A high proportion of the national GDP is lost every year in landslides, floods, and many other forms of disasters. A high number of human casualties and loss of public and private property in Nepal due to natural disasters may be attributed to inadequate public awareness, lack of disaster preparedness, weak governance, lack of coordination among the concerned government agencies, inadequate financial resources, and inadequate technical knowledge for mitigating the natural disasters. In this context, quite a few awareness and training programs for disaster risk reduction (DRR) have already been initiated in Nepal and their impact assessments are also already documented. However, effectiveness of the various implemented DRR programs is not yet evaluated through an independent study. Results: The work presented in this paper explores local people's knowledge on disaster risk reduction (DRR). Altogether, 124 local people from 18 to 74 years of age from randomly selected 19 districts of Nepal were interviewed focusing on various questions on disaster information, disaster knowledge, disaster readiness, disaster awareness, disaster adaptation, and disaster risk perception. The collected response data were statistically analyzed using histogram and independent sample t-tests to examine the DRR knowledge of people. An independent t-test analysis (Table 1) suggests that there is no statistically significant gender-based difference in disaster knowledge, disaster readiness, disaster awareness, and disaster risk perception of the surveyed people. Disaster adaptation capacity of the local people was evaluated and more than 60 percent of the respondents were determined to adapt state of disaster in the community. Conclusions: Findings of this independent research confirmed that the DRR education initiatives implemented in Nepal are not enough. The questionnaire survey results have pointed out at a few deficiencies in disseminating DRR knowledge in Nepal. We hope these findings will encourage the line agencies working in DRR issues in Nepal to modify their programs targeted for the local communities.

Database construction for landslide factors (slope, aspect, profile curvature, plan curvature, lithology, land use, distance from lineament & distance from river) and landslide inventory map is an important step in landslide susceptibility modelling. Using the frequency ratio model, the weights for each factor classes were calculated and assigned in GIS so as to add these factors and produce landslide susceptibility index maps based on mathematical combination theory. However, before combining them, their independence among each other should be ascertained. For this, the correlation matrix of logistic regression was applied and this showed that most of the correlations between factors were either absent or very insignificant suggesting that all landslide factors are independent. From a set of eight landslide factors, a total of 247 landslide susceptibility map combinations can be generated. However, for simplification, only 28 landslide susceptibility maps were chosen. Then the best landslide susceptibility map was selected based on high prediction accuracy. But, when there is similarity in the prediction accuracies of different combinations, the landslide susceptibility index difference values can be used as another selection criterion. Hence, the susceptibility map from a combination of all landslide factors except distance from river was found to be the best one. Among the 28 representative combinations, landslide susceptibility maps with the same prediction accuracy of 87.7% have been found in spite of their dissimilarity in their difference values. The combination, with a limited number of landslide factors and the highest prediction accuracy of 87.7%, was found from a combination of slope, lithology, land use and distance from lineament. In order to validate the prediction model, landslides were overlaid over the landslide susceptibility map and the number of landslides that fall into each susceptibility class was calculated. From this analysis 0.39%, 1.84%, 9.1%, 32.04% and 56.63% of the landslides fall in the very low, low, medium, high and very high landslide susceptibility classes respectively. Since 88.67% of the landslides fall in the high and very high susceptibility classes, the landslide susceptibility map can be considered reliable to predict future landslides.

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