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Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep

Muhammad Aamir1, Tariq Ali1, Muhammad Irfan2

  • 1Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces a novel multilayered deep convolutional neural network for detecting and classifying natural disasters from images. The proposed model achieves high accuracy, offering a robust solution for disaster management and ecological preservation.

Keywords:
convolutional neural networkdeep learningnatural disasters intensity and classification

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Natural disasters significantly disrupt ecological systems, human infrastructure, and ecosystems.
  • Existing deep learning methods for disaster detection face challenges with complex and imbalanced image data.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate natural disaster detection and classification.
  • To address the limitations of current techniques in handling complex and imbalanced image datasets.

Main Methods:

  • A multilayered deep convolutional neural network (CNN) was proposed, featuring two blocks: Block-I CNN for detection and Block-II CNN for intensity classification.
  • The model utilizes distinct filters and parameters within each block to enhance performance.

Main Results:

  • The model was evaluated on 4428 natural disaster images.
  • Achieved high performance metrics: 97.54% sensitivity, 98.22% specificity, 99.92% accuracy rate, 97.79% precision, and 97.97% F1-score.
  • Overall accuracy reached 99.92%, demonstrating competitive performance against state-of-the-art algorithms.

Conclusions:

  • The proposed multilayered deep CNN effectively detects and classifies natural disasters with high accuracy.
  • This model offers a promising advancement for mitigating losses caused by natural disasters and preserving ecosystems.