The SFA-LSSVM as a Decision Support System for Mitigating Liquefaction Disasters

Julian Pratama Putra Thedja, Jui-Sheng Chou, Tri Joko Wahyu Adi


Advanced data mining techniques are potential tools for solving civil engineering problems. This study proposes a novel classification system that integrates smart firefly algorithm (SFA) with least squares support vector machine (LSSVM). SFA is an optimization algorithm which combines firefly algorithm (FA) with smart components, namely chaotic logistic map, chaotic gauss/mouse map, adaptive inertia weight and Lévy flight to enhance optimization solutions. The least squares support vector machine (LSSVM) was adopted in this study for its superior performance of solving real-world problems. Based on the provided engineering data, the analytical results confirm that the SFA-LSSVM has 95.18% prediction accuracy


Data mining; optimization; firefly algorithm; support vector machines; liquefaction

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