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Neural network prediction of tsunami parameters in the aegean and Marmara Seas

Erdurmaz, Muammer Sercan
Tsunamis are characterized as shallow water waves, with long periods and wavelengths. They occur by a sudden water volume displacement. Earthquake is one of the main reasons of a tsunami development. Historical data for an observation period of 3500 years starting from 1500 B.C. indicates that approximately 100 tsunamis occurred in the seas neighboring Turkey. Historical earthquake and tsunami data were collected and used to develop two artificial neural network models to forecast tsunami characteristics for future occurrences and to estimate the tsunami return period. Artificial Neural Network (ANN) is a system simulating the human brain learning and thinking behavior by experiencing measured or observed data. A set of artificial neural network is used to estimate the future earthquakes that may create a tsunami and their magnitudes. A second set is designed for the estimation of tsunami inundation with relation with the tsunami intensity, the earthquake depth and the earthquake magnitude that are predicted by the first set of neural networks. In the case study, Marmara and Aegean regions are taken into consideration for the estimation process. Return periods including the last occurred earthquake in the Turkish seas, which was the Izmit (Kocaeli) Earthquake in 1999, were utilized together with the average earthquake depths calculated for Marmara and Aegean regions for the prediction of the earthquake magnitude that may create a tsunami in the stated regions for various return periods of 1-100 years starting from the year of 2004. The obtained earthquake magnitudes were used together with tsunami intensities and earthquake depth to forecast tsunami wave height at the coast. It is concluded that, Neural Networks predictions were a satisfactory first step to implement earthquake parameters such as depth and magnitude, for the average tsunami height on the shore