Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : Hettal M Tokle | Jignesh A Joshiunnamed

Volume 03 Issue 12 2016

ISSN no:  2348-3121

Page no: 91-96

Abstract – Precipitation forecasting is used for multiple reasons in multiple areas like agriculture, energy supply, transportations, etc. Accuracy of precipitation conditions shown in forecast reports is very necessary. In this paper, the review was conducted to investigate a better approach for forecasting which compares many techniques such as Artificial Neural Network, Ensemble Neural Network, Back propagation Network, Radial Basis Function Network, General Regression Neural Network, Genetic Algorithm, Multilayer Perception, Fuzzy clustering, etc. which were used for different types of forecasting. Among which neural network with the back propagation algorithm performs prediction with minimal error, the model derived is run on that basis. Neural network is a complex network which is self-adaptive in nature. It learns by itself using the training data and generates some intelligent patterns which are useful for forecasting. This paper reviews various techniques and focuses mainly on neural network with back propagation technique for precipitation-forecasting. 

Keywords— Neural Network, Back propagation Algorithm, ANN, Precipitation Prediction, Multilayer Neural Network, Quantitative Forecast, rainfall forcast


  1. Ch.Jyosthna Devi, B.Syam Prasad Reddy, K.Vagdhan Kumar, B.Musala Reddy, N.RajaNayak, “ANN Approach for precipitation Prediction using Back Propagation,” International Journal of Engineering Trends and Technology- Volume3Issue1- 2012.
  2. Harshani R. K. Nagahamulla, Uditha R. Ratnayake, AsangaRatnaweera,” An Ensemble of Artificial Neural Networks in Rainfall Forecasting,” The International Conference on Advances in ICT for Emerging Regions – ICTer 2012: 176-181
  3. M. Nasseri, K. Asghari, M.J. Abedini, “Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network,” Elsevier, ScienceDirect, Expert Systems with Applications 35 (2008) 1415–1421
  4. R Lee, J Liu, “JADE Weather MAN: A precipitation Forecasting System Using Intelligent Multiagent-Based Fuzzy Neuro Network”, IEEE 181 Transactions on Systems, Man and Cybernetics – Part C: Applications and Reviews, vol 34, no 3, 369 – 377, August 2004.
  5. Mohsen hayati and Zahra mohebi, “Temperature Forecasting based on Neural Network Approach”, World Applied Sciences Journal 2(6): 613-620, 2007, ISSN 1818-4952, IDOSI Publications, 2007.
  6. Kumar Abhishek, Abhay Kumar, Rajeev Ranjan, Sarthak Kumar, “A Rainfall Prediction Model using Artificial Neural Network”, IEEE Control and System Graduate Research Colloquium (ICSGRC 2012), pp 82-87.
  7. Yamin Wang, Shouxiang Wang, Na Zhang, “A Novel Wind Speed Forecasting Method Based on Ensemble Empirical Mode Decomposition and GA-BP Neural Network”, 978-1-4799-1303-9/13/2013 IEEE.
  8. Saima H., J. Jaafar, S. Belhaouari, T.A. Jillani,“Intelligent Methods for precipitation Forecasting: A Review”, 978-1-4577-1884-7/11/2011 IEEE.
  9. Tony Hall, Harold E. Brooks, Charles A. Doswell, ” Precipitation Forecasting Using a Neural Network”, Weather and Forecasting, Volume 14, June 1999, pp 338-345.
  10. Saurabh Karsoliya, “Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture”, International Journal of Engineering Trends and Technology- Volume3Issue6- 2012, pp 714-717.
  11. http://en.wikipedia.org/wiki/Weather forecasting
  12. Hansoo Lee, Jungwon Yu, YeongsangJeong, Sungshin Kim, “Genetic based feed-forward neural network training for chaff cluster detection,” International conference of fuzzy theory and applications, Taichung, Taiwan, Nov.16-18, 2012.
  13. R. Sallehuddin, et al., “Forecasting Time Series data using Hybrid Grey Relational Artificail Neural Network and Auto Regressive Integrated Moving Average,” Journal of Applied Artificial Intelligence, vol. 23.
  14. J. N. K. Liu and K. Y. Sin, “Fuzzy neural networks for machine maintenance in mass transit railway system,” IEEE Trans. Neural Networks, vol. 8, pp. 932–941, July 1997.
  15. Carlos Gershenson, “Artificial Neural Network for Beginners”, [email protected], university of sussex.