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Classification of Flood Disasters Severity Levels by Employing Machine Learning Techniques

EasyChair Preprint no. 9570

7 pagesDate: January 12, 2023


Natural disasters can happen at any time and pose a risk to individuals. Numerous disasters have the potential to affect the economy of the country as well as the people. The most frequent disaster that happens worldwide and severely affects people is flooding. The article provides machine learning classification techniques, like decision trees , Naive Bayes, support vector machines (SVM), and K-Nearest Neighbour (KNN) with severity index,. The severity index was established using economic loss as a starting point. To define the damage, however, a variety of factors were taken into consideration, such as the total number of fatalities, injuries, and flood victims, as well as the costs associated with reconstruction and the overall economic losses. The study reveals that, the Ensemble and decision tree classifiers provide better ideal categorization in terms of accuracy and error

Keyphrases: Decision Tree, Ensemble Classifier, Naive Baye, natural disaster, support vector machines ensemble

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {S Rajeshbabu and G Sakthivel and A Arulkumar and K Kannan},
  title = {Classification of Flood Disasters Severity Levels by Employing Machine Learning Techniques},
  howpublished = {EasyChair Preprint no. 9570},

  year = {EasyChair, 2023}}
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