Landslides are common natural disasters in Bogor, Indonesia, triggered by a combination of factors including slope aspect, soil type and bedrock lithology, land cover and land use, and hydrologic conditions. In the Bogor area, slopes with volcanic lithologies are more susceptible to failure. GIS mapping and analysis using a Frequency Ratio Model was implemented in this study to assess the contribution of conditioning factors to landslides, and to produce a landslide susceptibility map of the study area. A landslide inventory map was prepared from a database of historic landslides events. In addition, thematic maps (soil, rainfall, land cover, and geology map) and Digital Elevation Model (DEM) were prepared to examine landslide conditioning factors. A total of 173 landslides points were mapped in the area and randomly subdivided into a training set (70%) with 116 points and test set with 57 points (30%). The relationship between landslides and conditioning factors was statistically evaluated with FR analysis. The result shows that lithology, soil, and land cover are the most important factors generating landslides. FR values were used to produce the Landslide Susceptibility Index (LSI) and the study area was divided into five zones of relative landslide susceptibility. The result of landslide susceptibility from the mid-region area of Bogor to the southern part was categorized as moderate to high landslide susceptibility zones. The results of the analysis have been validated by calculating the Area Under a Curve (AUC), which shows an accuracy of success rate of 90.10% and an accuracy of prediction rate curve of 87.30%, which indicates a high-quality susceptibility map obtained from the FR model.

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Sigi Biromaru is an area prone to landslides. This study aims to apply the statistical method of Weight of Evidence (WoE) in landslide susceptibility mapping using Geographic Information Systems (GIS). The 265 landslides that occurred 2009-2019 were randomly divided into two groups, 70% of the data were used as training dataset for susceptibility modelling and 30% of the data were used as test data for validation of the susceptibility model. Twenty-one parameters were tested for their influence on landslides. Based on the Area Under Curve (AUC), parameters that significant controlling the landslides are slope gradient, elevation, aspect, flow direction, peak ground acceleration, clay content (<0,002 mm), land cover, terrain ruggedness index (TRI), river density, soil type, lineament density, lithology, rainfall and stream power index (SPI) respectively. The validation results show that the AUC success rate is 0,811 using the training dataset and AUC prediction rate is 0,756 using the test dataset. These results indicate that the WoE method produces a good landslide susceptibility map in the Sigi Biromaru area.

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This paper reports on our experience in Native language teacher training in two contexts in British Columbia: one a university sponsored course offered in a northern community, and one a workshop-based in service course offered in the Fraser Valley. Work done with the students is described, excerpts of student work are presented and suggestions about Native language teacher training in Canada are made.