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Data Mining Techniques Used for Rainfall Prediction – A Review Survey

Neetu Sharma, Dhiraj Kumar

Abstract


Rainfall is considered as one of the real segments of the hydrological procedure; it takes critical part in assessing dry season and flooding occasions. Thusly, it is vital to have a precise model for rainfall prediction. As of late, a few data driven demonstrating approaches have been explored to perform such forecasting as multilayer perceptron neural systems (MLP-NN). Actually, the rainfall time series modeling (SARIMA) includes essential temporal measurements. Keeping in mind the end goal to assess the earnings of both models, factual parameters were utilized to make the examination between the two models. These parameters incorporate the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient Of Correlation (CC) and BIAS. Two-Third of the information was utilized for preparing the model and 33% for testing.

Keywords: big data mining, Hadoop, map reduce, security

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References


Le Duff F., Muntean C., Cuggiaa M., et al. Predicting Survival Causes After Out of Hospital Cardiac Arrest using Data Mining Method. Fieschi M., et al. (Eds.), MEDINFO 2004, Amsterdam: IOS Press; 2004.

Pappa G.L., Baines A.J., Freitas A.A. Predicting post-synaptic activity in proteins with data mining, Bioinformatics. 21; ii19–25p.

Kumar L.S., Lee y.H., Yeo j.X., et al. tropical rain classification and estimation of rain from z-r (reflectivity-rain rate) relationships, Prog Electromag Res B. 2011; 32: 107{127, 2011}p.

El-Shafie A., Noureldin A., Taha M.R., et al. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia Hydrol. Earth Syst Sci Discuss. 2011; 8: 6489–532p.

Wu C.L., Chau K.W. Prediction of rainfall time series using modular soft computing methods, Eng Appl Artif Intell. 2012 Elsevier Ltd. All rights. http://dx.doi.org/10.1016/j.engappai.2012.05.023

Alvisi S., Mascellani G., Franchini M., et al. Water Level Forecasting Through Fuzzy Logic and Artificial Neural Network Approaches. Copernicus GmbH; 2006.

Yilmaz A.G., Imteaz M.A. Development of a hydrologic model using artificial intelligence for Upper Euphrates Basin in Turkey.

Singh P., Borah B. Indian Summer Monsoon Rainfall Prediction Using Artificial Neural Network. Springer; 2013.

Kumar A., Kumar A., Ranjan R., et al. A rainfall prediction model using artificial neural network, Control Syst Grad Res Colloquium (ICSGRC). 2012.

Lee S.y., Cho S., Wong P.M. Rainfall prediction using artificial neural networks, J Geogr Inform Decision Anal. 1998; 2(2): 233–42p.

Bhuwana V., Rainfall Runoff Modeling by Using Adaptive-Network-Based Fuzzy Inference System (ANFIS) – Case Study Ciliwung River.

Edvinaldrian, Yudhasetiawandjamil. Application of multivariate anfis for daily rainfall prediction: influences of training data size, makara, sains, 2008; 12(1).

Faulina R., Suhartono. Hybrid ARIMA-ANFIS for rainfall prediction in Indonesia, Int J Sci Res (IJSR). 2013; 2(2).

Guhathakurta P. Long-range monsoon rainfall prediction of 2005 for the districts and sub-division Kerala with artificial neural network, Curr Sci. 2005; 90: 773–9p.

Rajeevan M. Prediction of Indian summer monsoon: statusproblems and prospects, Curr Sci. 2001




DOI: https://doi.org/10.37591/jscrs.v2i1.288

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