Vol. 2 No. 5 (2020): Volume 2, Issue 5, Year 2020
Articles

A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence

Tabassum Farhana Ullah
Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India.
Gnana Prakasi O.S.
Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India
Kanmani P
Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India
Published September 23, 2020
Keywords
  • Precipitation,
  • Multilayer Perception Layer (MLP) architecture,
  • Recurrent Neural Network (RNN),
  • Long Short-Term Memory (LSTM)network,
  • Gated Recurrent Units (GRU)
How to Cite
Ullah, T. F., O.S., G. P., & P, K. (2020). A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence. International Research Journal of Multidisciplinary Technovation, 2(5), 8-14. https://doi.org/10.34256/irjmt2052

Plum Analytics

Abstract

Flood occurs as often as possible happens due to many environmental changes in our planet in the present years. The occurrence and damages caused by flood is very high. Major cause of flood is due to heavy rainfall which in turn increases the water level of the rivers and other water bodies. The various factors that play a major role in the occurrence of rainfall are rise in temperature, humidity level, dew point, pressure in and around the area of concern, wind speed, etc. In order to reduce the number of victims due to flood it is necessary to have a system to predict flood occurrence. In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results. In addition, a design model has been proposed to predict the flood by training the Deep Neural Network with the above-mentioned factors.

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References

  1. R. C. Gonzalez, R. E. Woods, (1987) Digital Image Processing 2nd edition, Prentice Hall, 1.
  2. K. Hiroi, and N. Kawaguchi, (2016) Flood Eye- Real-time flash flood prediction system for urban complex water flow, IEEE Sensors, IEEE, 1-3.
  3. J. Liang, P. Jacobs, S. Parthasarathy, (2016) Human-guided Flood Mapping on Satellite Images, KDD 2016 Workshop on Interactive Data Exploration and Analytics, 76-85.
  4. S A. Krishnan, S. Ganesh, S. S. Satheendran, A. Varghese, Flood Mapping and Damage Assessment of Kaziranga National Park, Assam using Multi-Temporal Sentinel-1 Synthetic Aperture Radar Images, Indian Journal of Scientific Research, 18 (2018) 1-8,
  5. H. Karl, A. Willig, (2007) Protocols and Architectures for Wireless Sensor Networks, 1st edition, John Wiley & Sons, 1-507.
  6. I. R. Widiasari, L. E. Nugroho, W. Widyawan, Deep Learning Multilayer Perceptron (MLP) for Flood Prediction Model Using Wireless Sensor Network Based Hydrology Time Series Data Mining, In 2017 International Conference on Innovative and Creative Information Technology (ICITech) IEEE, 1-5.
  7. N. H. B. Harun, W. L. Woo, S. S. Dlay, (2010) Performance of Keystroke Biometrics Authentication System Using Multilayer Perceptron Neural Network (MLP NN), In International Conference on Computer and Communication Engineering (ICCCE'10), IEEE, 1-6.
  8. S. M. Faradonbeh, F. Safi Esfahani (2019) A review on Neural Turing Machine, arXiv.
  9. S. Mahmoud, A. Lotfi, C. Langensiepen, Behavioural Pattern Identification and Prediction in Intelligent Environments, Applied Soft Computing Journal, 13 (2013) 1813-1822.
  10. E. Diaconescu, The Use of NARX Neural Networks to Predict Chaotic Time Series, WSEAS Transactions on Computer Research, 3 (2008) 182-191.
  11. F. A. Ruslan, Z. M. Zain, R. Adnan, Flood Water Level Modeling and Prediction Using Narx Neural Network: Case Study at Kelang River, In 2014 IEEE 10th International Colloquium on Signal Processing and its Applications, IEEE, 204-207.
  12. M. Huang, J. Gallic hand, Z. Wang, M. Goulet, A Modification to the Soil Conservation Service Curve Number Method for Steep Slopes in The Loess Plateau of China, Hydrological Processes, 20 (2006) 579-589.
  13. Y. Wu, Y. Tao, F. Liu, Flood Zoning Calculation in Zhejiang Province Based on Regional Regression Analytical Method, Journal of Hohai University, Nature Sciences, pp. 39-43, 2015.
  14. P. K. Singh, P. K. Bhunya, S. K. Mishra, U. C. Chaube, A Sediment Graph Model Based on SCS-CN Method, Journal of Hydrology, 349 (2008) 244-255.
  15. Z. Shaozhong, Y. Juqin, (2010) Flux and Level Prediction based on A Wavelet Neural Network Flood Model, In 2010 Third International Symposium on Knowledge Acquisition and Modeling, IEEE. 67-70.
  16. B. Tavus, S. Kocaman, C. Gokceoglu, H. A. Nefeslioglu, Considerations on the use of Sentinel-1 Data in Flood Mapping in Urban Areas: Ankara (Turkey) 2018 Floods, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII–5 (2018) 575-581.
  17. Y-J Kwak, R. Pelich, J. Park, W. Takeuchi, (2018) Improved Flood Mapping Based on the Fusion of Multiple Satellite Data Sources and In-Situ Data, In 2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 3521-3523.
  18. T. Mikolov, G. Zweig, (2012) Context Dependent Recurrent Neural Network Language Model, In 2012 IEEE Spoken Language Technology Workshop (SLT), IEEE, 234-239.
  19. H. Sak, A. Senior, F. Beaufays, (2014) Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling, Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 338-342.