Abstract:
Earthquakes are one of the devastating natural disasters which cause significant damage
to property due to their destructive nature. Seismic stations around the globe record data
continuously to make it available for research and information purpose. An enormous amount of
research has been done in this regard in the past as well but generally, the research is done on the
seismic regions only. This identifies that there is limited work done on the data analysis for
country-wise seismic data. This thesis specifically analyzes and evaluates collective countrywise seismic data through machine learning algorithms. From a geological perspective, Pakistan is
located on three tectonic plates. The historic seismic activity of Pakistan along with its neighboring
countries including China and Afghanistan is considered for an efficient evaluation. For an
unbiased comparative analysis, two evaluation techniques are considered that include threshold based binary seismic classification and magnitude categorization based on the Mercalli intensity
scale for determining magnitude destructive nature. Decision tree, Random
forest, XGBoost, Adaboost, and KNN are implemented on three country-wise seismic datasets.
Among the five applied algorithms, two algorithms including Random forest and XGB performed
exceptionally well in the selected evaluation methods.
The proposed evaluation methods can be applied to other natural hazardous data as well to evaluate
the performance of applied algorithms on selected evaluation criteria.
All the algorithms are compared on the basis of selected comparative metrics that provides an
insight into the quality of the algorithm performance on country-wise seismic historic activity.
