Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems

This study proposes a novel classification system integrating swarm and metaheuristic intelligence, i.e., a smart firefly algorithm (SFA), with a least squares support vector machine (LSSVM). Benchmark functions were used to validate the optimization performance of the SFA. The experimental results showed that the SFA obtained 100% success rate in searching the optimum for most benchmark functions. The SFA was then integrated with the LSSVM to create a metaheuristic optimized classification model. A graphical user interface was developed for the proposed classification system to assist engineers and researchers in executing advanced machine learning tasks. The system was applied to several geotechnical engineering problems that involved measuring the groutability of sandy silt soil, monitoring seismic hazards in coal mines, predicting postearthquake soil liquefaction, and determining the propensity of slope collapse. The prediction problems in these studies were complex because they were dependent on various physical factors, and such factors exhibited highly nonlinear relations. The analytical results revealed that the metaheuristic optimization within machine learning-based classification system exhibited a groutability prediction accuracy of 95.42%, seismic prediction accuracy of 93.96%, soil liquefaction prediction accuracy of 95.18%, and soil collapse prediction accuracy of 95.45%. Hence, the proposed system is a promising tool to provide decision-makers with timely warnings of geotechnical hazards.