Flooding is one of the serious problems that befell Parepare City, South Sulawesi Province. This problem has a negative impact on the surrounding community. Proper planning needs to be pursued so that losses from the disaster can be reduced. This study uses an overlay method with scoring between existing parameters, where each parameter is carried out a scoring process by giving the weight and value according to each classification which is then overlaid using ArcGIS 10.3 software. The use of this software utilizes a Geographic Information System (GIS) that can explain and present objects of flood hazard areas in digital form. The parameters which is used in this study are based on Kazakis et al. (2015), namely FIGUSED (Flow Accumulation, Rainfall Intensity, Land Use, Slope, Elevation, Distance to Drainage Network). The results obtained in the form of flood-prone maps where very vulnerable locations have an area of 10.72 Km2 or about 13.04% of the total area of the City of Parepare and the flood index value (FHI) 4.20 - 6.87 including high-hazard zones, have wide 48.04 Km2 or about 58.41% of the total area of Parepare City and the flood index value (FHI) 3.58 - 4.20 including the low hazard zone, and which has an area of 23.49 Km2 or about 28.55% of the total the area of Parepare City and the flood index value (FHI) 2.20 - 3.58 are included in the safe zone. Meanwhile, the causes of flooding in Parepare City are flow accumulation, slope, distance from the river, and landuse.

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Hambalang area is one of the regions susceptible to landslide events. This is due to unstable geological conditions and high rainfall. Administratively, research area included in Citeureup District, Bogor Regency, West Java Province. Astronomically, research area is located at the coordinates 106°51’30” - 106°53’30” East Longitude and 06°32’ - 06°34’ South Latitude. This study aims to determine the geological conditions of the study area and conduct susceptibility zoning in the Hambalang area using the method of Frequency Ratio and Logistic Regression. The geological conditions of the study area consist of geomorphology in the form of corrugated and flatland morphological units, stratigraphy consisting of claystone units, andesite units and alluvial units, and the structure of the Sumurbatu anticline. The parameters used to analyze the causes of landslide are slope, geology, rainfall, soil type, land use and distance from rivers. There were 19 landslide events identified in the study area. The relationship between occurrences of landslide and the parameters mentioned earlier can be quantified using the value of Frequency Ratio and Logistic Regression. Based on the results of the validation with the Frequency Ratio method, the AUC value of 0.6854 which shows the model of landslide susceptibility based on the selection of parameters and the adequacy of the landslide event data is good. The value of the frequency ratio of the model is divided into three zones of susceptibility, namely high (42.30%), moderate (20.34%) and low (37.36%). Based on the results of the validation with the Logistic Regression method, the AUC value of 0.762 also shows the landslide susceptibility model based on the selection of parameters and the adequacy of the landslide event data so that the Logistic Regression value of the model is divided into three susceptibility zones, namely high (56.89%), moderate (19.53%) and low (23.68%).

Deforestation is a permanent change of forest cover area to non-forest cover area. Social factors contribute more to the occurrence of deforestation, so this study was directed to examine the social factors that drive deforestation. Research location selected based on the key vulnerability of deforestation profiles. This study only used moderately vulnerability and vulnerability profile. Data analysis in this research using PCA (Principal Component Analysis) Method. The results explained that the Spatial Deforestation Model in South Sulawesi and West Sulawesi have differences based on the vulnerability profile. The dominant deforestation profiles affected were population density, productive age and employment. Population density affects deforestation because the site is always experiencing an increase in population and is not balanced with the extent of its territory, especially on vulnerable profiles. Productive age is very influential and increasing. The employment is also one of the most influencing of deforestation. The field of education itself does not give a significant effect. The spatial model of deforestation based on social factors in South Sulawesi shows that in the same profile different influences were found. In West Sulawesi, social involvement tends to be the same for each profile. The influence of population, productive age and availability of regions has a significant influence on the incidence of deforestation.