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Earthquake Risk Assessment Using Xtreme Learning Machine and Remote Sensing
Malaysian peninsula is one of the seismically stable countries in the world which is situated at a distance of 350 km from the seismic zone of Sumatra fault. However, the Penang is one of the islands of Malaysia situated close to the Sumatran seismic zone experiencing moderate to high seismic hazard in comparison to the mainland. The purpose of this study is to analyze and estimate the earthquake potential zone, vulnerability, and risk with respect to several demographic, environmental, and physical criteria. An earthquake risk assessment (ERA) map was created by using an Xtreme learning machine and remote sensing generating potential zone map coupled with a multi criteria decision making model (MCDM) model producing five vulnerability classes. The hybrid combination of the XLM-MCDM model with a geographic information system (GIS) enabled to generate the risk map. The model was applied to Penang Island in Malaysia and the validation shows that the proposed model can produce potential zone, vulnerability, and risk map with an overall accuracy of 95%. A comparison of the results obtained by model shows that the hybrid XLM-MCDM model is reliable for ERA mapping. The XLM-MCDM model accurately derived the vulnerability conditions in the highly populated and densely occupied building areas. The findings of this research are useful for land use planners, decision makers and researchers as a scientific basis to create risk management strategies.
Earthquake Risk Assessment Using Xtreme Learning Machine and Remote Sensing
Malaysian peninsula is one of the seismically stable countries in the world which is situated at a distance of 350 km from the seismic zone of Sumatra fault. However, the Penang is one of the islands of Malaysia situated close to the Sumatran seismic zone experiencing moderate to high seismic hazard in comparison to the mainland. The purpose of this study is to analyze and estimate the earthquake potential zone, vulnerability, and risk with respect to several demographic, environmental, and physical criteria. An earthquake risk assessment (ERA) map was created by using an Xtreme learning machine and remote sensing generating potential zone map coupled with a multi criteria decision making model (MCDM) model producing five vulnerability classes. The hybrid combination of the XLM-MCDM model with a geographic information system (GIS) enabled to generate the risk map. The model was applied to Penang Island in Malaysia and the validation shows that the proposed model can produce potential zone, vulnerability, and risk map with an overall accuracy of 95%. A comparison of the results obtained by model shows that the hybrid XLM-MCDM model is reliable for ERA mapping. The XLM-MCDM model accurately derived the vulnerability conditions in the highly populated and densely occupied building areas. The findings of this research are useful for land use planners, decision makers and researchers as a scientific basis to create risk management strategies.
Earthquake Risk Assessment Using Xtreme Learning Machine and Remote Sensing
Jena, Ratiranjan (author) / Shanableh, Abdallah (author) / Al-Ruzouq, Rami (author) / Gibril, Mohammed Barakat A. (author)
2023-02-20
906591 byte
Conference paper
Electronic Resource
English
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