Abstract
The paper demonstrates the use of clustering to find different sensitive seismic zones and time series for the earthquake hazard prediction. Anticipating seismic activities using previous history data is obtained by applying hierarchical, k-means and density based clustering. Data is collected first and then clustered. Finally, the clustered data is used to obtain the different seismic zones on map. On the top of that data is used in linear regression to build a predictive model for forecasting upcoming earthquakes’ magnitudes for different regions in and nearby areas of Bangladesh.
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Md Tanzim, S. et al. (2019). Analysis of Spatial Data and Time Series for Predicting Magnitude of Seismic Zones in Bangladesh. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_36
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DOI: https://doi.org/10.1007/978-3-319-91189-2_36
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