Techniques Based on Metaheuristics Combined with an Adaptive Neurofuzzy System and Seismic Sensors for the Prediction of Earthquakes

Abstract

This research looked into the viability of using metaheuristic algorithms in conjunction with an adaptive neurofuzzy system to predict seismicity and earthquakes. Different metaheuristic algorithms have been combined with an artificial intelligence (AI) algorithm. Subjected to seismicity is a promising factor. The new sensors have many advantages over the older, more impressive-looking ones, including (a) a generally linear relationship between the measured values and real ground motion (described above), (b) the ability to measure three orthogonal components of ground movement in a single unit, (c) sensitivity to a very broad range of frequencies, and (d) high dynamic range, which allows for the detection of both very small and fairly large tremors. To accept the acquired results as a hybrid model of an adaptive neurofuzzy inference system with particle swarm optimization (PSO), genetic algorithm (GA), and extreme machine learning (ELM) (ANFIS-PSO-GA-ELM) implemented. According to the dataset, all approaches produce excellent and realistic predictions of seismic loads; however, the method ANFIS-PSO produces better results. All the strategies demonstrated a high level of predictability. Finally, this research urges researchers to investigate the performance of triple hybrid MT algorithms using a variety of hybrid metaheuristic methodologies, rather than the existing double hybrid MT algorithms.