Scholarlink

<p>Natural disasters such as earthquakes, floods, Thunderstorms, cyclones, and rainfall pose significant risks to human life and infrastructure. Traditional prediction methods often rely on physical sensors, meteorological data, and historical patterns, which may not always provide timely or accurate warnings. To investigate the feasibility of using sentiment analysis on GIS-based data in social media posts like Twitter, Facebook, Google News and other textual data to predict natural disasters in tropical regions. By analyzing the sentiment of communications related to weather conditions, emergency alerts, and public reactions, the study seeks to identify patterns and correlations that may serve as early indicators of impending disasters. The ultimate goal is to enhance early warning systems and improve disaster preparedness by integrating sentiment analysis with traditional prediction models. Sentiment keyword graph filtering Technique (SKG) is used to remove neural text keyword-based filtering for disaster-related terms and graph-based filtering for central node. A deep neural network (DNN) is used to classify and analyze sentiment from social media posts. Adam optimization algorithm (AOA) is used to optimise model parameters to minimize the loss function, improving prediction accuracy. The evaluation is conducted on Matlab or Python based on parameters such as Adam accelerates convergence, reducing training time and computational resources and for natural disaster prediction are disaster occurrence risk factors, disaster probability, event timelines, geospatial alerts, early warning alerts, and confidence intervals. The result shows that the Proposed Model demonstrates superior performance with consistently high accuracy rates, peaking at 98.8, implemented using Python software. The future scope of this research is vast and promising, with potential expansions including integration with IoT devices, multilingual support, real-time processing, and more, to enhance the system’s capabilities in predicting and alerting tropical region natural disasters through GIS-based sentiment analysis of social media data.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An efficient predictive alert system for natural disasters in tropical region using GIS and social media data using sentiment analysis

  • 期刊论文

  • 发表日期：

    2025年6月10日

查看全文
  • S. Mohanarangan,
  • L. Suganthi,
  • G. Shoba,
  • D. Karthika

摘要

Natural disasters such as earthquakes, floods, Thunderstorms, cyclones, and rainfall pose significant risks to human life and infrastructure. Traditional prediction methods often rely on physical sensors, meteorological data, and historical patterns, which may not always provide timely or accurate warnings. To investigate the feasibility of using sentiment analysis on GIS-based data in social media posts like Twitter, Facebook, Google News and other textual data to predict natural disasters in tropical regions. By analyzing the sentiment of communications related to weather conditions, emergency alerts, and public reactions, the study seeks to identify patterns and correlations that may serve as early indicators of impending disasters. The ultimate goal is to enhance early warning systems and improve disaster preparedness by integrating sentiment analysis with traditional prediction models. Sentiment keyword graph filtering Technique (SKG) is used to remove neural text keyword-based filtering for disaster-related terms and graph-based filtering for central node. A deep neural network (DNN) is used to classify and analyze sentiment from social media posts. Adam optimization algorithm (AOA) is used to optimise model parameters to minimize the loss function, improving prediction accuracy. The evaluation is conducted on Matlab or Python based on parameters such as Adam accelerates convergence, reducing training time and computational resources and for natural disaster prediction are disaster occurrence risk factors, disaster probability, event timelines, geospatial alerts, early warning alerts, and confidence intervals. The result shows that the Proposed Model demonstrates superior performance with consistently high accuracy rates, peaking at 98.8, implemented using Python software. The future scope of this research is vast and promising, with potential expansions including integration with IoT devices, multilingual support, real-time processing, and more, to enhance the system’s capabilities in predicting and alerting tropical region natural disasters through GIS-based sentiment analysis of social media data.