A Rainfall Prediction Model Using Articial Neural Network
Keywords:
feed forward Network, artificial neural network, back propagation algorithm, multilayer artificial neural network componentAbstract
Back propagation algorithm is most commonly used in neural network projects because it works faster than earlier approaches to learning and for its accuracy. Back propagation is a workhorse of learning in neural network. In back-propagation algorithm, there are two facets in its learning cycle, one to generate input pattern and another one to adjust the output by changing the weights of the network. There are many applications of feed forward neural network such as weather and financial predictions, face and signature detections etc. Thispaper describes the training, testing of data sets and finding the number of hidden neurons using back propagation algorithm for better performance. In the research, rainfall prediction in the region of Mumbai has been analyzed using feed forward network. In formulating artificial neuralnetwork based predictive models three layered network has beenconstructed.
References
M. Tektaş, “Weather forecasting using ANFIS and ARIMA models”, Environmental Research, Engineering and Management, Vol.51, Issue.1, pp.5-10, 2010.
SC. Michaelides, CC. Neocleous, CN. Schizas, ”Artificial Neural networks and multiple linear regression in estimating missing rainfall data”, In Proceedings of the DSP95 International Conference on Digital Signal Processing, Limassol Cyprus, pp.668-673, 1995.
SA. Kalogiru, C. Neocleous, CN. Constantinos, SC. Michaelides, NC. Schizas, “A time series construction of precipitation records using artificial neural networks, In Proceedings of EUFIT, Germany, pp. 2409-2413. 1997.
S. Lee, S. Cho, PM. Wong, “Rainfall prediction using artificial neural network”, Journal of geographic information and Decision Analysis, VOl.2, Issue.2, pp.233-42, 1998.
WK. Wong, PM. Wong, TD. Gedeon, CC. Fung, “RainfallPrediction Using Neural Fuzzy Technique”, pp.213-21, 1999
E. Toth, A. Brath, A. Montanari,” Comparison of short-term rainfall prediction models for real-time flood forecasting”, Journal of Hydrology, Vol.239, 132–147, 2000.
K. Koizumi, “An objective method to modify numerical model forecastswith newly given weather data using an artificial neural network”, Forecast, Vol.14, pp.109–118, 1999.
NQ. Hung, MS. Babel, S. Weesakul, NK. Tripathi, “An Artificial Neural network Model for rainfall Forer casting in Bangkok, Thailand”, Hydrology and Earth System Sciences, Vol.13, Issue.8, pp.1413-25, 2009.
KK. Htike, OO. Khalifa, “Research paper on ANNmodel using focused time delay learning”, International Conference on Computer and Communication Engineering, Kuala Lumpur, pp.23-41, 2010.
A. Abraham, D. Steinberg, NS. Philip, “Rainfall Forecasting Using Soft Computing Models and Multivariate Adaptive Regression Splines”, IEEE SMC Transactions, Special Issue on Fusion of Soft Computing and Hard Computing in Industrial Applications, NY, pp.1-6 2001.
RD. Stern, RA. Coe, "model fitting analysis of daily rainfall data", Journal of the Royal Statistical Society: Vol. 147, No. 1 (1984), pp. 1-34Series A (General), Vol.147, No.1, pp.1-34, 1984.
K. Gurney, “An introduction to neural networks”, CRC Press, USA, pp.1-234, 1997.
SS. Baboo, IK. Shareef, “An efficient WeatherModel using Artificial Neural Network”, International Journal of Environmental Science and Development, Vol.1, No.4, pp.321-326, 2010.
E. Vamsidhar, “Prediction of rainfall Using BackpropagationNeural Network Model”, International Journal on ComputerScience and Engineering Vol. 02, No. 04, pp.1119-1121, 2010.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
