Flood Susceptibilty Prediction using LSTM Algorithm

Authors

  • Lakshmi Satya Rayasam
  • Divya R.B
  • Gajapriya L
  • R Valarmathi
  • R. Uma

Keywords:

Flood Prediction, LSTM, Machine Learning, Tensor flow etc.

Abstract

This Floods are one of the most frequent and severe natural disasters. Especially in India, where about 60% of the flood damage occurs due to river floods and 40% due to heavy rainfalls and cyclones. The main reasons for these intense flooding's are severe deforestation, development, and infrastructure in flood-prone areas, impermeable surfaces, and heavy rainfall. The impact of flooding includes loss of human life, damage to property, destruction of crops, loss of livestock, and deterioration of health conditions owing to waterborne diseases. This project helps in predicting the susceptibility of flooding of a particular area based on not only on the rainfall level given by the meteorological department but also based on the data such as previous rainfall levels, River flow. The data obtained are trained by using special recurrent network algorithm called LSTM (Long Short-Term Memory networks).

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References

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Published

2021-06-30

How to Cite

Rayasam, L. S. ., R.B , D. ., L, G. ., Valarmathi, R. ., & Uma, R. (2021). Flood Susceptibilty Prediction using LSTM Algorithm. The Journal of Contemporary Issues in Business and Government, 27(3), 1957–1963. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/1802

Issue

Vol. 27 No. 3 (2021): The journal of contemporary issues in business and government

Section

Articles

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