Abstract
Earthquakes could be early predicted as demonstrated by animal’s behavior that are able to detect the leading wave part of the seismic wave (the P-wave). P-waves travel faster than S-wave wave (the shaking wave), so they reach the seismic sensors early (tens of seconds to minutes in advance) compared to the P-wave.
A bioinspired framework could be implemented mimicking the animal’s behaviour related to the event of an incoming earthquake.
Training a Fuzzy Neural Network to recognize the P-waves, early prediction of earthquakes is feasible and an adequate recovery strategy could be implemented. A technological motivation is the availability of OTS (off-the-shelf) vibration sensors and the fast development of IoT (Internet of Things) toward the new paradigm IoE (Internet of Everything).
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Acknowledgements
Thanks are due to Dr. Paolo Valisa (Centro Geofisico Prealpino – Varese –Italy) that enabled us to collect seismographic data at the Centro Geofisico Prealpino – Varese - Italy.
A special acknowledgment is due to Prof. Nikola Kasabov, Auckland University of Technology, Director KEDRI – Knowledge Engineering and Discovery Research Institute, for his invaluable suggestions on how to get the most from the EFuNN’s evolving capabilities.
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Malcangi, M., Malcangi, M. (2019). Bioinspired Early Prediction of Earthquakes Inferred by an Evolving Fuzzy Neural Network Paradigm. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_30
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DOI: https://doi.org/10.1007/978-3-030-20257-6_30
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