EFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS: EXAMPLE OF KAZAKHSTAN
DOI:
https://doi.org/10.37943/21KUXZ6354Keywords:
earthquake prediction, machine learning, Kazakhstan, Seismic Risk Assessment, predictive modeling, disaster management, Geographical Heatmap, algorithm performanceAbstract
This study investigates the effectiveness of machine learning methods in identifying earthquake-prone areas in Kazakhstan and its neighboring regions. By leveraging a comprehensive dataset encompassing significant earthquake data from 1900 to 2023, various machine learning algorithms were employed, including RandomForest, GradientBoosting, Logistic Regression, Support Vector Classification (SVC), K-Nearest Neighbors (KNeighbors), Decision Tree, XGBoost, LightGBM, AdaBoost, and MLPClassifier. The primary objective was to analyze and compare the performance of these models in predicting earthquake magnitudes and frequencies. The results reveal that certain algorithms significantly outperformed others in terms of accuracy, underscoring the potential of machine learning techniques to enhance earthquake prediction capabilities. Notably, XGBoost and RandomForest demonstrated the highest predictive accuracy, suggesting their suitability for application in seismic risk assessment. These findings offer valuable insights for governmental agencies engaged in disaster management and prevention planning, highlighting the practical implications of integrating advanced analytical techniques in their strategies. In addition to model performance analysis, a visual heatmap was generated to illustrate the geographical distribution of earthquake occurrences across the studied regions. This visual representation effectively identifies high-risk areas, serving as a crucial tool for local authorities and researchers in making informed decisions regarding safety measures and emergency preparedness. This research contributes to the expanding body of knowledge on earthquake prediction utilizing machine learning, emphasizing the necessity for continuous improvement in predictive models by incorporating additional environmental and geological factors. The implications of these findings extend beyond academic discourse, holding significant potential for enhancing public safety in regions vulnerable to seismic activity. As such, this study advocates for the integration of machine learning methodologies in disaster management frameworks to mitigate risks and enhance preparedness in earthquake-prone regions.
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