Ai-Driven Predictive Models for Earthquake Forecasting Using Big Data Analytics

What is it about?

This research explores the improvement of earthquake forecasting with AI predictive models using big data analytics. The primary goal is to develop a predictive framework that integrates comprehensive data sources, counting seismic plus geospatial and ecological data, toward improving model forecast accuracy and central times. The study employs two AI-based methods for machine-learning models, Random Forest and Logistic Regression, which are used for predictive analytics. Data collection is performed with a dataset from Kaggle spanning from 1995 to 2023, with subsequent preprocessing and exploratory data analysis to uncover significant patterns. The Random Forest model established the higher enactment with an accuracy of 0.90 and precision of 0.88 compared to the Logistic Regression model, which achieved an accuracy of 0.89 and precision of 0.87. These results underscore the value of Random Forest in handling complex, then imbalanced data and enhancing predictive capabilities. The research also identifies challenges, such as data quality, model interpretability, and computational constraints. Future work will address these challenges by exploring advanced algorithms and improving real-time predictive systems to provide earlier warnings and better disaster awareness.

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Photo by Clay Banks on Unsplash

Photo by Clay Banks on Unsplash

Why is it important?

This research explores the improvement of earthquake forecasting with AI predictive models using big data analytics. The primary goal is to develop a predictive framework that integrates comprehensive data sources, counting seismic plus geospatial and ecological data, toward improving model forecast accuracy and central times. The study employs two AI-based methods for machine-learning models, Random Forest and Logistic Regression, which are used for predictive analytics. Data collection is performed with a dataset from Kaggle spanning from 1995 to 2023, with subsequent preprocessing and exploratory data analysis to uncover significant patterns. The Random Forest model established the higher enactment with an accuracy of 0.90 and precision of 0.88 compared to the Logistic Regression model, which achieved an accuracy of 0.89 and precision of 0.87. These results underscore the value of Random Forest in handling complex, then imbalanced data and enhancing predictive capabilities. The research also identifies challenges, such as data quality, model interpretability, and computational constraints. Future work will address these challenges by exploring advanced algorithms and improving real-time predictive systems to provide earlier warnings and better disaster awareness.

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