Early Flood Detection and Alarming System Using Machine Learning Techniques

Authors

  • Pooja Mane Student, Department of Computer Science and Engineering, Walchand College of Engineering, Sangli, India
  • Meghana Katti Student, Department of Computer Science and Engineering, Walchand College of Engineering, Sangli, India
  • Preeti Nidgunde Student, Department of Computer Science and Engineering, Walchand College of Engineering, Sangli, India
  • Anil Surve Professor, Department of Computer Science and Engineering, Walchand College of Engineering, Sangli, India

Keywords:

Alarming system, Alerts, Early flood detection, Machine Learning

Abstract

The Krishna river basin covering Sangli, Kolhapur districts under Koyana to Almatti dam region is affected by frequent floods in the rainy season. At present, various robust and efficient flood detection systems are available, but the field of early flood prediction systems is unexplored. Predicting the upcoming floods on the basis of rainfall and current water levels would surely help in forestalling the large scale life and property damages incurred due to the floods. In this work, we have experimented with different ML algorithms like SVM, KNN, Logistic Regression, Naive Bayes, etc., for the available rainfall dataset. Using these ML models, we have developed an end-to-end flood prediction and an alarming system consisting of a website and an android application for alerting the concerned masses and authorities.

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Published

2020-10-13

How to Cite

[1]
P. . Mane, M. . Katti, P. . Nidgunde, and A. . Surve, “Early Flood Detection and Alarming System Using Machine Learning Techniques”, International Journal of Research in Engineering, Science and Management, vol. 3, no. 10, pp. 29-32, Oct. 2020.

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Articles