AI-Based Intensity Analysis and Categorization of Natural Disasters

ABSTRACT

Natural disaster intensity analysis and classification is a vital application in the domain of artificial intelligence and environmental monitoring. The goal is to enable machines to accurately assess and categorize the severity of natural disasters such as floods, earthquakes, cyclones, and wildfires based on real-time data inputs like satellite imagery, seismic readings, and meteorological parameters. Traditional methods that rely on threshold-based rules or early machine learning models like Decision Trees and Naive Bayes often fail to provide accurate predictions due to the dynamic and unpredictable nature of disasters. This project proposes a robust AI-based solution leveraging Deep Learning techniques, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to analyze spatial and temporal data. The proposed system automatically learns complex patterns from raw datasets, improving its performance across diverse scenarios. Tested on publicly available datasets such as NASA's disaster database and NOAA weather data, the system achieves an accuracy exceeding 95% in classifying disaster intensity levels.

Keywords: Natural Disasters, Deep Learning, Disaster Classification, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Real-Time Analysis, Accuracy, Adaptability.