ew 18(18): e2

Research Article

Disaster Impact Mitigation using KDD and Support Vector Machine algorithms

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  • @ARTICLE{10.4108/eai.12-6-2018.154812,
        author={D. John Aravindhar and M. N. Sushmitha and K. Padmaveni},
        title={Disaster Impact Mitigation using KDD and Support Vector Machine algorithms},
        journal={EAI Endorsed Transactions on Energy Web and Information Technologies},
        volume={5},
        number={18},
        publisher={EAI},
        journal_a={EW},
        year={2018},
        month={6},
        keywords={Knowledge Discovery in Databases, Support vector machine, Satellite},
        doi={10.4108/eai.12-6-2018.154812}
    }
    
  • D. John Aravindhar
    M. N. Sushmitha
    K. Padmaveni
    Year: 2018
    Disaster Impact Mitigation using KDD and Support Vector Machine algorithms
    EW
    EAI
    DOI: 10.4108/eai.12-6-2018.154812
D. John Aravindhar1,*, M. N. Sushmitha2, K. Padmaveni2
  • 1: Professor, Hindustan Institute Of Technology and Science, Chennai, India
  • 2: Assistant Professor, Hindustan Institute Of Technology and Science, Chennai, India
*Contact email: jaravindhar@hindustanuniv.ac.in

Abstract

Disasters such as Hurricanes, Typhoons, Floods and earthquakesare not good for the society since it causes serious damage for the society. A natural disaster causes loss in property as well as in life of victims. The victims need immediate help once they are affected by the disaster. The immediate need are rescue, food and communications. The survey says victims of recent disaster were unable to get instant communication regarding the evacuation path and other help from authorities for remedial action. This can be overcome with our proposed idea of having a database of area wise population along with the pre-disaster and post-disaster satellite images of the disaster affected area. Knowledge Discovery in Databases (KDD) is used in data pre-processing and to extract knowledge from the database. Support vector machine(SVM) is used to classify the disaster effect with the pre-disaster and post-disaster satellite images as input. The idea is implemented and tested with sample data and has given impressive results