@article{ref1,
title="Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia",
journal="Science of the total environment",
year="2020",
author="Jena, Ratiranjan and Pradhan, Biswajeet and Beydoun, Ghassan and Alamri, Abdullah M. and Ardiansyah,  and Nizamuddin,  and Sofyan, Hizir",
volume="749",
number="",
pages="e141582-e141582",
abstract="On 28th September 2018, a very high magnitude of earthquake Mw 7.5 struck the Palu city in the Island of Sulawesi, Indonesia. The main objective of this research is to  estimate the earthquake risk based on probability and hazard in Palu region using  cross-correlation among the derived parameters, Silhouette clustering (SC), pure  locational clustering (PLC) based on hierarchical clustering analysis (HCA),  convolutional neural network (CNN) and analytical hierarchy process (AHP)  techniques. There is no specific or simple way of identifying risks as the  definition of risk varies with time and space. The main aim of this study is: i) to  conduct the clustering analysis to identify the earthquake-prone areas, ii) to  develop a CNN model for probability estimation, and iii) to estimate and compare the  risk using two calculation equations (Risk A and B). Owing to its high prediction  ability, the CNN model assessed the probability while SC and PLC were implemented to  understand the spatial clustering, Euclidean distance among clusters, spatial  relationship and cross-correlation among the estimated Mw, PGA and intensity  including events depth. Finally, AHP was implemented for the vulnerability  assessment. To this end, earthquake probability assessment (EPA), susceptibility to  seismic amplification (SSA) and earthquake vulnerability assessment (EVA) results  were employed to generate risk A, while earthquake hazard assessment (EHA), SSA and  EVA were used to generate risk B. The risk maps were compared and the differences in  results were obtained. This research concludes that in the case of earthquake risk  assessment (ERA), results obtained in Risk B are better than the risk A. This study  achieved 89.47% accuracy for EPA while for EVA a consistency ratio of 0.07. These  results have important implications for future large-scale risk assessment, land use  planning and hazard mitigation.<p /> <p>Language: en</p>",
language="en",
issn="0048-9697",
doi="10.1016/j.scitotenv.2020.141582",
url="http://dx.doi.org/10.1016/j.scitotenv.2020.141582"
}