Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), School of Information Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia.
Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), School of Information Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia; Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209, Neungdong-ro Gwangin-gu, Seoul 05006, Republic of Korea; Center of Excellence for Climate Change Research, Department of Meteorology, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, P. O. Box 80234, Jeddah 21589, Saudi Arabia.
Sci Total Environ. 2020 Dec 20;749:141582. doi: 10.1016/j.scitotenv.2020.141582. Epub 2020 Aug 11.
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.
2018 年 9 月 28 日，印度尼西亚苏拉威西岛的帕卢市发生了一次震级非常高的地震 Mw7.5 级。本研究的主要目的是使用衍生参数之间的互相关、轮廓聚类 (SC)、基于层次聚类分析 (HCA) 的纯位置聚类 (PLC)、卷积神经网络 (CNN) 和层次分析过程 (AHP) 技术，根据概率和危害评估帕卢地区的地震风险。由于风险的定义随时间和空间而变化，因此没有特定或简单的方法来识别风险。本研究的主要目的是：i) 进行聚类分析以识别地震多发区，ii) 开发用于概率估计的 CNN 模型，以及 iii) 使用两个计算方程（风险 A 和风险 B）来估计和比较风险。由于其高预测能力，CNN 模型评估了概率，而 SC 和 PLC 则用于理解空间聚类、集群之间的欧几里得距离、空间关系和互相关以及估计的 Mw、PGA 和烈度（包括事件深度）。最后，AHP 用于脆弱性评估。为此，地震概率评估 (EPA)、地震放大敏感性 (SSA) 和地震脆弱性评估 (EVA) 结果用于生成风险 A，而地震危害评估 (EHA)、SSA 和 EVA 用于生成风险 B。对风险图进行了比较，并得出了结果的差异。本研究得出结论，在地震风险评估 (ERA) 中，风险 B 的结果优于风险 A。本研究实现了 EPA 89.47%的准确率，而 EVA 的一致性比率为 0.07。这些结果对未来的大规模风险评估、土地利用规划和灾害缓解具有重要意义。
