Predictive Analysis of Machine Learning Algorithms Applicable for Natural Disaster Management

Abstract

The escalating impact and frequency of natural disasters necessitate the development of robust predictive frameworks to proactively manage and mitigate their devastating consequences. ML techniques are used for the accurate forecasting of various natural disasters, such as earthquakes, floods, wildfires, hurricanes, and landslides, and these are thoroughly examined in this study. By harnessing historical data, environmental variables, and cutting-edge ML algorithms, this study meticulously assesses the efficacy of diverse techniques in forecasting and classifying these cataclysmic events. Through a comprehensive survey that scrutinizes the nuances of ML methods, random forest methods, support vector machines (SVM), neural networks, k-means clustering, Naive Bayes, reinforcement learning, and time series analysis models, the authors dissect their strengths and limitations in predicting specific types of natural disasters. Examining algorithms against actual real-world datasets offers valuable insights into the capabilities of each algorithm, shedding light on their capabilities to fortify early detection and warning systems. The research underscores the multifaceted challenges inherent in predicting natural disasters, emphasizing the paramount significance of high-quality, real-time data acquisition. This foundational aspect drives the iterative refinement of models, ensuring their adaptability to the dynamic and evolving environmental conditions that influence disaster occurrences. Furthermore, it emphasizes the pivotal role of interdisciplinary collaboration, emphasizing the fusion of domain expertise and technological advancements to bolster the resilience of predictive models. Ultimately, the culmination of these efforts aims to improve the precision and timeliness of disaster predictions, thereby fortifying comprehensive disaster preparedness and response strategies. To address the challenges associated with predicting and managing natural disasters, this article advocates for an all-encompassing strategy that integrates advanced machine learning techniques with ongoing data collection and expert perspectives.