CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
HYBRID ANN AND FIREWORKS ALGORITHM FOR REAL-TIME SOFTWARE RELIABILITY PREDICTION

Ivy Botchway Felix Larbi Aryeh Boniface Kayode Alese
Random Articles
Reseach Article

Rainfall Prediction using Neural Net based Frequency Analysis Approach

by Seema Mahajan, Himanshu Mazumdar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 9
Year of Publication: 2013
Authors: Seema Mahajan, Himanshu Mazumdar
10.5120/14602-2851

Seema Mahajan, Himanshu Mazumdar . Rainfall Prediction using Neural Net based Frequency Analysis Approach. International Journal of Computer Applications. 84, 9 ( December 2013), 7-11. DOI=10.5120/14602-2851

@article{ 10.5120/14602-2851,
author = { Seema Mahajan, Himanshu Mazumdar },
title = { Rainfall Prediction using Neural Net based Frequency Analysis Approach },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 9 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number9/14602-2851/ },
doi = { 10.5120/14602-2851 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:27.294889+05:30
%A Seema Mahajan
%A Himanshu Mazumdar
%T Rainfall Prediction using Neural Net based Frequency Analysis Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 9
%P 7-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rainfall prediction is very complex hydrologic process and is important as it holds the key to any countries' economy. Proposed model presents a new approach for yearly rainfall prediction of 30 Indian subdivisions. Yearly rainfall data of the Indian subdivision is available from IITM, Pune. The combination of Fast Fourier Transform (FFT) and Feed Forward Neural Network (FFNN) is applied for next one year rainfall prediction. Fast Fourier transform with filtering is performed on interpolated rainfall data to separate periodic components. These periodic components and delayed periodic components are given as input and desired output respectively to an FFNN for training. While testing the output of FFNN, inverse FFT gives the predicted rainfall value by amount of training input-output delay. This model is tested with 140 year's Indian subdivisions rainfall data. The experimental results of 30 subdivisions show that next one year rainfall prediction accuracy is above 92%.

References
  1. A. K. Sahai, M. K. Soman and V. satyan, All India summer monsoon rainfall prediction using an artificial neural network, Climate Dynamics (2000)
  2. A. El-shafie, M. Mukhlisin, Ali A. Najah and M. R. Taha, Performance of artificial neural network and regression techniques for rainfall-runoff prediction, International Journal of the Physical Sciences vol. 6(8)
  3. 8P. Guhathakurta, Long range monsoon rainfall prediction of 2005 for the districts and sub-division Kerala with artificial neural network, Scientific Correspondance
  4. V. K. Somvanshi, O. P. Pandey, P. K. Agrawal, N. V. kalanker, M. Ravi Prakash and Ramesh Chand, Modelling and prediction of rainfall using artificial neural network and ARIMA techniques, Indian Geophysical Journal (April 2006).
  5. Sarah Afshin, Hedayat Fahmi, Amin Alizadeh, Hussein Sedghi and Fereidoon Kaveh, Long term rainfall forecasting by integrated artificial neural network-fuzzy logic-wavelet model in Karoon basin, Scientific Research and Essays Vol. 6(6)
  6. Turgay Partal and Özgür Ki?i , Wavelet and neuro-fuzzy conjunction model for precipitation forecasting, Journal of Hydrology, Volume 342, Issues 1–2
  7. Lingzhi wang and Jiansheg Wu, Application of Hybrid RBF Neural Network Ensemble Model Based on Wavelet Support Vector Machine Regression in Rainfall Time Series Forecasting, Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference
  8. Khaing Win Mar, Thinn Thu Naing, Optimum Neural Network Architecture for Precipitation Prediction of Myanmar, World Academy of Science, Engineering & Technology, 48 2008
  9. D. Nagesh Kumar, M. Janga Reddy and Rajib Maity, Regional Rainfall Forecasting using Large Scale Climate Teleconnections and Artificial Intelligence Techniques, Journal of Intelligent Systems, vol 16, No 4,2007
  10. Iyengar, R. N. and Raghukanth, S. T. G. , Intrinsic mode functions and a strategy for forecasting Indian monsoon rainfall. Meteorol. Atmos. Phys. , 2004
  11. S. Karmakar, M. K. Kowar, P. Guhathakurta, Long-Range Monsoon Rainfall Pattern Recognition and Prediction for the Subdivision 'EPMB' Chhattisgarh Using Deterministic and Probabilistic Neural Network, 7th International conference on Advance in Pattern Recognition
  12. Sulochana Gadgil*, M. Rajeevan and Ravi Nanjundiah, Monsoon prediction – Why yet another failure?, Current Science, Vol. 88, NO. 9, 10 May 2005.
  13. Surajit Chattopadhyay, Goutami Chattopadhyay, Comparative study among different neural net learning algorithms applied to rainfall time series, Meteorological Applications Volume 15, Issue 2, pages 273–280, 2008
  14. Venkatraman Prasanna, Juvy Subere, Dwijendra K. Das, Srinivasan Govindarajan and Tetsuzo Yasunari, Development of daily gridded rainfall dataset over the Ganga, Brahmaputra and Meghna river basins, Meteorological Applications, Royal Meteorological Society.
  15. http://www. tropmet. res. in/ - Indian Institute of Tropical Meteorology, Pune
Index Terms

Computer Science
Information Sciences

Keywords

Rainfall prediction Fast Fourier Transform Feed Forward Neural Network

Powered by PhDFocusTM