Thursday, December 12, 2019

Geographical Information System

Question: Discuss about theGeographical Information System. Answer: Introduction Landslides are known to be severely destructive in nature with regards to human life and the general economy across the globe. Assessment and reduction of risks in landslide hazard can be achieved through the provision of easily accessible, consistent, and accurate data regarding the occurrence of landslides (Shahabi Hashim, 2015). Therefore, a susceptibility mapping that's accurate can be primary information for a broad range of users from both the public and the private sectors, from the global community of scientists and governmental departments on both the local level and the international arena. Consequently, this paper discusses the landslide susceptibility of a region with annual rainfall greater than 1600 nm, slope steeper than 15 degrees, and evidence of clay, silt, sand or basaltic feature. Geographical Information System The Digital Cadastral Database allows authorities to view the lot number, plan number and property area for any property in the local area. In this particular field with annual rainfall greater than 1600 nm, slope steeper than 15 degrees, and evidence of clay, silt, sand or basaltic feature, a GIS spatial data will be used. There are two main types of GIS spatial data namely, vector data and raster data (Ahmed, 2015). In this research, raster data will be used because the analysis of data is usually easy and quick to perform. According to Shahabi and Hashim (2015), typically, the best source of data in a GIS landslide susceptibility analysis is the landslide inventory map showing locations and landslide features that have shifted in the past albeit showing the trigger mechanism. As such, inventory maps are a source of significant information about the spatial distribution of regions with existing landslides and those with potential for future landslides. The figure below shows the flow chart of the spatial analysis tool. Source: (Andi Gusti, 2013) Predicted probability is the basis for landslide susceptibility visualization of the occurrence of a landslide in a grid cell computed to form a predicted logit (Petschko, Brenning, Bell, Goetz, Glade, 2014). As a result of sampling variability and model, the predictions are subject uncertainty expressed by the standard error of model predictions. Further, this error provides a means to determining the approximate lower and upper confidence limits for final statistical predictions. These confidence limits define the interval for the actual probability of sites with values of the explanatory variables located with the confidence limit, for instance, 95% (Petschko, et al., 2014). Synonymously, the assumption is that the actual probability of the occurrence of a landslide at a given locality is within the confidence interval; however, the actual probability may not fall within any narrower range of values within the confidence interval. Nonetheless, the results of this study will be an alyzed and reported through statistical tables, maps, and graphs. The region, in this case, has several potential risk factors in mapping the landslide susceptibility. These are precipitation and slope which are the most critical, surface curvature, land cover proximity to fault and coastline as well as elevation. On the other hand, the accuracy of the model employed in this study will be accessed statistically through regression coefficient. References Ahmed, B. (2015). Landslide susceptibility modeling applying user-defined weighting and data-driven statistical techniques in Cox's Bazar Municipality, Bangladesh. Nat Hazards, 79(3), 1707-1737. Andi Gusti, T. (2013). The Dynamic of Embankment Width Change at the Coastal Area of Pangkep District, South Sulawesi Province, Indonesia. Ijms. Petschko, H., Brenning, A., Bell, R., Goetz, J., Glade, T. (2014). Assessing the quality of landslide susceptibility maps case study Lower Austria. Natural Hazards And Earth System Science, 14(1), 95-118. Shahabi, H. Hashim, M. (2015). Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci. Rep., 5, 9899.

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