RainFall Prediction System for Mumbai
Keywords:
Accuracy, Forecasting, Machine Learning Algorithms, Rainfall, random forest classifierAbstract
These days, climate change is accelerating due to global warming, which has a major influence on humanity. Sea levels are rising, the atmosphere and ocean are warming, and there are more floods and droughts as a result. Uneven rainfall or precipitation is one of the main effects of it. Today, most of the important global authorities are taking into consideration the laborious problem of precipitation forecasting. One climatic factor that has an impact on the many human activities is precipitation. like manufacturing, production, and tourism in the agricultural sector. Rainfall becomes extremely problematic as a result, necessitating more accurate forecasts. Accurate rainfall forecasting is crucial for all of these reasons. There are several ways to forecast it, but the one that is chosen for the objective of this assignment is to analyze and compile rainfall data from the past 12 months, gathered over a period of 5 years. The goal is to utilize this data to forecast rainfall for the following day. To achieve this, the project aims to optimize the results by employing a random forest classifier as a machine learning model for predicting rainfall.
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References
L. Houthuys, Z. Karevan and J.A. Suykens, “Multi-view LSSVM regression for black-box temperature prediction in weather forecasting”, International Joint Conference on Neural Networks, pp. 1102-1108, May 2017, IEEE
Ali Haidar and Brijesh Verma. "Monthly rainfall forecasting using a one-dimensional deep convolutional neural network." IEEE Access 6, pp. 69053-69063, Nov 2018
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.
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
S. Dev, F.M. Savoy, Y.H. Lee, and S. Winkler,” Design of lowcost, compact and weather-proof whole sky imagers for HighDynamic-Range captures”, IEEE International Geoscience and Remote Sensing Symposium, pp. 5359-5362, Jul 2015. IEEE.
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
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.
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.
A.D Dubey, "Artificial neural network models for rainfall prediction in Pondicherry", International Journal of Computer Applications, vol 120, no. 3, Jan 2015.
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.
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.
