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Data Mining based Neural Network Model for Rainfall Forecasting

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
P. Arumugam, R. Ezhilarasi

P Arumugam and R Ezhilarasi. Data Mining based Neural Network Model for Rainfall Forecasting. International Journal of Computer Applications 170(4):30-33, July 2017. BibTeX

	author = {P. Arumugam and R. Ezhilarasi},
	title = {Data Mining based Neural Network Model for Rainfall Forecasting},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {4},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {30-33},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017914831},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


India is basically an agricultural country and the success or failure of the harvest and water scarcity in any year is always considered with the greatest concern. The average annual or seasonal rainfall at a place does not give sufficient information regarding its capacity to support crop production. Rainfall distribution pattern is the most important. The rainfall forecasting is scientifically and technologically challenging problem around the world in the last century. In this paper Neural Network model was developed for the rainfall forecast performance and the results were compared with Seasonal Auto regressive integrated moving average (SARIMA) model. The performance by (ANN) model and statistical time series model for prediction were examined using visualization technique and statistical test.


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Data mining, Neural networks, Time Series, SARIMA, BIC and RMSE.