Research Article
Geostatistics for Risk Level Mapping: Synthetic Data Example
@INPROCEEDINGS{10.4108/eai.31-3-2022.2320971, author={Waskito Pranowo and Adhitya Ryan Ramadhani}, title={Geostatistics for Risk Level Mapping: Synthetic Data Example}, proceedings={Proceedings of the 1st International Conference on Contemporary Risk Studies, ICONIC-RS 2022, 31 March-1 April 2022, South Jakarta, DKI Jakarta, Indonesia}, publisher={EAI}, proceedings_a={ICONIC-RS}, year={2022}, month={8}, keywords={risk level mapping geostatistics estimation kriging uncertainty}, doi={10.4108/eai.31-3-2022.2320971} }- Waskito Pranowo
Adhitya Ryan Ramadhani
Year: 2022
Geostatistics for Risk Level Mapping: Synthetic Data Example
ICONIC-RS
EAI
DOI: 10.4108/eai.31-3-2022.2320971
Abstract
Risk level mapping is important for mitigation plans and any disaster-related decision. However, the data in certain areas can be sparse for some reasons. The risk levels are not known or analyzed at some positions. In statistics, these positions are called unsampled locations. Geostatistics can play a role in estimating the risk at unsampled locations. The most common geostatistics method that can be used is kriging. Moreover, kriging can calculate the uncertainty of its estimation. This paper aims to investigate the benefit of kriging in risk level mapping. The synthetic data experiment is conducted to explain how kriging works in risk level mapping. Kriging method is able to estimate the risk level at unsampled locations and take into account uncertainties.