Scholarlink

<p>Today, many natural disasters occur in the world. Damage caused during disaster assessment has been a vital process. Traditional methods of assessing the cause of damage are neither fast nor efficient. In disaster error estimation, progress has been made in recent years. Internet resources serve as a dynamic facilitator to obtain data for the model and produce the desired output. The proposed hybrid CNN helps in detecting and identifying the natural disaster damage and also precisely assesses the damages with higher accuracy. Hybrid CNN performance metrics, such as accuracy, precision, recall, and F1 score, are compared with logistic regression, support vector machine, gradient boosting, and random forest algorithms in the processing of imagery data with natural disaster regions and provide the cost estimation with any objects precisely over the higher accuracy rate of greater than 96%.</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 enhanced discovery of multiple natural disasters using machine learning model

  • 期刊论文

  • 发表日期：

    2025年3月12日

查看全文
  • Thirukrishna J T

摘要

Today, many natural disasters occur in the world. Damage caused during disaster assessment has been a vital process. Traditional methods of assessing the cause of damage are neither fast nor efficient. In disaster error estimation, progress has been made in recent years. Internet resources serve as a dynamic facilitator to obtain data for the model and produce the desired output. The proposed hybrid CNN helps in detecting and identifying the natural disaster damage and also precisely assesses the damages with higher accuracy. Hybrid CNN performance metrics, such as accuracy, precision, recall, and F1 score, are compared with logistic regression, support vector machine, gradient boosting, and random forest algorithms in the processing of imagery data with natural disaster regions and provide the cost estimation with any objects precisely over the higher accuracy rate of greater than 96%.