Application Research of Tsunami Monitoring and Forecasting Using Data Mining Techniques

Abstract eng:
85 percent of the tsunami triggered by earthquakes, it’s difficult to detect and of great danger. Since the earthquake-triggered tsunami in the open sea off spreads quickly, it is difficult to carry out effective monitoring and forecasting efficiently and timely. However, the path and the impact of the tsunami indeed follow certain rules which are not evident. Thanks to the use of data mining techniques, it is possible to analyze the factors which can cause the tsunami, or to evaluate the impact of the factors, and also to find the relation between these factors. This paper focuses on using appropriate data mining algorithms such as artificial neural networks, principal component analysis and cluster analysis to analyze related data to find the generating mechanism of earthquake tsunami and then to create the earthquake generating model, and also to analyze to create quick impact evaluating model of tsunami to coastal areas, utilizing the spatial distribution data of Chinese inshore seabed structure, topography and faulting, the historical data and dynamic data of the seismic source depth, seismic source location, seismic waves, and earthquake grades, the data of satellite based sea shock wave monitoring, data buoys, coastal tide level real-time monitoring, and the geo-data of coastal zone terrain and tied-protection facilities.

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