Flood forecasting by using Machine Learning

Authors

  • Dr. C. Murugamani  Associate Professor, Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • K Lakshmi Prasanna  Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • R. Mamatha  Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India

Keywords:

Machine Learning, Artificial Intelligence, Apache SystemML, Python

Abstract

Floods have always been one of the worst disasters in the world. It not only affects living creatures but also impact the surrounding. The prediction of floods well before its arrival can provide a larger safety measures and can help to protect the habitat. The collection of large data for prediction and analyzing it approximately is always being the point of concern for the increasing technology. Various Artificial Intelligence model and machine learning algorithm software’s are developed to predict the flood. The previous developed system still needs modification. Our proposed work is based on Apache System ML machine learning software. Since this platform supports Python programming, the optimization and collection of large number of flood related data can be analyzed. The algorithm codes or programming codes are writable, reduced in error and readable. The proposed system will be more efficient and scalable and is expected to give better results and predictions.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-10-18

Issue

Volume 8, Issue 5, September-October-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. C. Murugamani, K Lakshmi Prasanna, R. Mamatha, " Flood forecasting by using Machine Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 5, pp.329-333, September-October-2022.