IoT-based Earthquake Prediction Using Fog and Cloud Computing
Earthquakes are severe, unexpected, life-threatening catastrophes that affect all kind of living beings. It frequently results in the loss of life and property. Predicting earthquake is the most important aspect of this field. With the golden age of the Internet of Things (IoT), an innovative new idea is to couple IoT technology with cloud and fog computing to improve the potency and accuracy of earthquake monitoring and forecasting. The embedded IoT-Fog-Cloud layered structure is adopted in this research to predict earthquakes using seismic signal data. This model transfers sensed seismic signals to fog for analysis of seismic data. At fog, Fast Walsh Hadamard transform is used to extract time and frequency domain features and PCA is employed to reduce the dimensionality of feature sets. Random Forest algorithm has been used to classify seismic signals into two different events, viz., earthquake and non-earthquake accompanied by the real-time warnings. When compared to other classification models, implementation findings indicate that the Random Forest classifier achieves high values of specificity, sensitivity, precision, and accuracy with average values of 88.50%, 90.25%, 89.50%, and 92.66%. Hence make this framework more real-time compliant for earthquake prediction.
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