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IJSTR >> Volume 9 - Issue 6, June 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Estimating Rainfall Prediction using Machine Learning Techniques on a Dataset

[Full Text]



R Vijayan, V Mareeswari, P Mohankumar, G Gunasekaran K Srikar



Classification, Data Analysis, Decision Tree, Feature Extraction, Machine Learning, Neural Network, Random Forest, Regression, Support Vector Machine.



Machine learning seems to be an artificially intelligent application that demonstrates systems with both the ability to analyze and enhance inherently via experience whilst being specifically programmed. Algorithms rely on software programs that are developed that could also access information and using that to learn for itself. The prediction of rainfall is regarded as very significant in everyday life, from cultivation to event. Previous prediction of rainfall was using the complex combination of mathematical abstractions and it was inadequate to get such a high classification rate Prediction of rainfall is rendered via acquiring quantitative data about the present atmospheric state. Algorithms models could learn complicated mappings, based solely on samples, from inputs to outputs, and require minimal mapping. Due to the dynamic nature of the atmosphere, a precise prediction of weather conditions is a difficult task. To forecast the rainfall state of the future, the variability in situations in earlier years need to be used. The likelihood it will fit throughout the past year's neighboring fortnight is a very high Random forest rainfall prediction algorithm with factors including temperature, humidity, and wind. Therefore this forecast will prove accurate, it will predict rainfall based on previous records.The platform used is anaconda and the language is python which is portable and interactive. The libraries used for implementation are numpy, matlib, seaborn and pandas.



[1] L. Houthuys, Z. Karevan and J.A.Suykens, “Multi-view LS-SVM regression for black-box temperature prediction in weather forecasting”, International Joint Conference on Neural Networks, pp. 1102-1108, May 2017, IEEE
[2] Ali Haidar and Brijesh Verma. "Monthly rainfall forecasting using one-dimensional deep convolutional neural network." IEEE Access 6,pp. 69053-69063,Nov 2018
[3] S. Chatterjee, B. Datta, S. Sen, N. Dey and N.C Debnath,” Rainfall prediction using hybrid neural network approach”,2nd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing, pp. 67-72. IEEE
[4] S. Dev, F.M. Savoy, Y.H. Lee, and S. Winkler,” Design of low-cost, compact and weather-proof whole sky imagers for High-Dynamic-Range captures”, IEEE International Geoscience and Remote Sensing Symposium, pp. 5359-5362, Jul 2015. IEEE.
[5] S. Manandhar, Y.H Lee and S. Dev,” GPS derived PWV for rainfall monitoring”, IEEE International Geoscience and Remote Sensing Symposium, pp. 2170-2173, Jul 2016. IEEE.
[6] M. Fujita and T. Sato,” Observed behaviours of precipitable water vapour and precipitation intensity in response to upper air profiles estimated from surface air temperature”, Scientific Reports, vol. 7, no. 1, pp. 1-6, Jul 2017.
[7] S. Manandhar, Y.H. Lee, Y.S. Meng, and J.T. Ong,” A simplified model for the retrieval of precipitable water vapor from GPS signal”, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp. 6245-6253, Jul 2017.
[8] S. Chatterjee, S. Sarkar, S. Hore, N. Dey, A.S. Ashour and V.E.Balas, “Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings”, Neural Computing and Applications, vol. 28, no. 8, pp. 2005-2016, Aug 2017.
[9] S. Chatterjee, S. Ghosh, S. Dawn, S. Hore, S. and N. Dey, “Forest Type Classification: A hybrid NN-GA model-based approach”, In Information systems design and intelligent applications, pp. 227-236, Springer, New Delhi,2016.
[10] A.D Dubey, "Artificial neural network models for rainfall prediction in Pondicherry", International Journal of Computer Applications, vol 120, no. 3, Jan 2015.
[11] D.R. Nayak, A. Mahapatra, and P. Mishra,” A survey on rainfall prediction using artificial neural network”, International Journal of Computer Applications, vol. 72, no. 16, Jan 2013.