AN INTENSE SURVEY OF WEATHER FORECASTING BASED ON MACHINE LEARNING AND DEEP NEURAL NETWORK

Deepika Awadhiya; Omprakash Chandrakar; Bakhtawer Shameem

doi:10.29121/shodhkosh.v5.i5.2024.2837

Authors

Deepika Awadhiya Research Scholar, MSIT, MATS University, Raipur, India
Dr. Omprakash Chandrakar Professor, MSIT, MATS University, Raipur, India
Dr. Bakhtawer Shameem Assistant Professor, Govt Bilasa Girls P. G. COllage, Bilaspur, India

DOI:

https://doi.org/10.29121/shodhkosh.v5.i5.2024.2837

Keywords:

ANN, Weather Forecasting, Back Propagation Algorithms, Deep Neural Network, Data Mining

Abstract [English]

The weather forecast determines the future state of the weather. Weather forecasting is very important because both agriculture and business sector rely on weather forecasts. We use artificial neural network and data mining technique to predict weather conditions. Weather is a dynamic process, on-liner artificial neural network (ANN) can operate as process. Our research also pointed out that deep neural network is best methods as compared to traditional algorithms. Main focus is ANN or back propagation methods. Back propagation method is best approach for variation of data as well as long- range weather forecasting. This system will take the parameter and predict weather after analyzing the input information with the information in database. Consequently, two basic functions to be specific classification (training) and prediction (testing) will be performed. The outcomes demonstrated that these data mining procedures can be sufficient for weather forecasting.

References

G. Bala Sai Tarun, J.V. Sriram, K. Sairam, K. Teja Sreenivas, M.V.B.T. Santhi,” Rainfall prediction using Machine Learning Techniques”,International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-7, May, 2019.

Lemuel Clark P. Velasco, Ruth P. Serquiña, Mohammad Shahin A. Abdul Zamad, Bryan F. Juanico, Junneil C. Lomocso,” Performance Analysis of Multilayer Perceptron Neural Network Models in Week-Ahead Rainfall Forecasting”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 3, 20

Kaushik Dutta, Gouthaman. P,” Rainfall Prediction using Machine Learning and Neural Network”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878 (Online), Volume-9 Issue-1, May 2020. DOI: https://doi.org/10.35940/ijrte.A2747.059120

Gyanesh Shrivastava, Sanjeev Karmakar, Manoj Kumar Kowar, Pulak Guhathakurta,” Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review”, International Journal of Computer Applications (0975 – 8887) Volume 51– No.18, August 2012. DOI: https://doi.org/10.5120/8142-1867

Munmun Biswas, Tanni Dhoom, Sayantanu Barua,” Weather Forecast Prediction: An Integrated Approach for Analyzing and Measuring Weather Data”, International Journal of Computer Applications (0975 – 8887) Volume 182 – No. 34, December 2018. DOI: https://doi.org/10.5120/ijca2018918265

Y.Md.Riyazuddin, Dr.S.Mahaboob Basha, Dr.K.Krishna Reddy,” An Approach for Prediction of Weather System by Using Back propagation Neural Networ”, International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org ISSN: 2455-2631 © July 2017 IJSDR | Volume 2, Issue7.

B.S.Panda, P.V.Lasyasri , D.Maneesha , P.Goutham , K.Suresh , Ch.Pranavsankar,” A Novel Approach for Weather Forecasting using Machine Learning Techniques”, International Journal of Computer Science & Communication (ISSN: 0973-7391) Volume 11• Issue 2 pp. 25-30 April 2020 -Sept 2020 www.csjournals.com

Priyanka Mahajan, Chhaya Nawale, Siddheshwar Kini, Prof. Krishnanjali Shinde,” Weather Forecasting using Neural Network”, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Published by, www.ijert.org ICIATE - 2017 Conference Proceedings

R. Samya, R. Rathipriya. “Predictive Analysis for Weather Prediction using Data Mining with ANN: A Study”, in International Journal of Computational Intelligence and Informatics, Vol. 6: No. 2, ISSN: 2349-6363 `149-153, September 2016.

Mr. Sunil Navadia, Mr. Jobin Thomas, Mr. Pintukumar Yadav, Ms. Shakila Shaikh, “Weather Prediction: A novel approach for measuring and analyzing weather data”, International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), (I-SMAC 2017), IEEE, pp 414-417. DOI: https://doi.org/10.1109/I-SMAC.2017.8058382

Hornik, K.; Stinchcombe, M.; White, H. Universal approximation of an unknown mapping and its derivativesusing multilayer feedforward networks. Neural Netw. 1990, 3, 551–560. [CrossRef] DOI: https://doi.org/10.1016/0893-6080(90)90005-6

LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [CrossRef] [PubMed] DOI: https://doi.org/10.1038/nature14539

Lawrence, S.; Giles, C.L.; Tsoi, A.C.; Back, A.D. Face recognition: A convolutional neural- network approach.IEEE Trans. Neural Netw. 1997, 8, 98–113. [CrossRef] [PubMed] DOI: https://doi.org/10.1109/72.554195

Lipton, Z.C.; Berkowitz, J.; Elkan, C. A critical review of recurrent neural networks for sequence learning.arXiv 2015, arXiv:1506.00019

(PDF) Deep Learning-Based Weather Prediction: A Survey. Available from: https://www.researchgate.net/publication/347684364_Deep_LearningBased_Weather_Prediction_A_Survey [accessed Jun 12 2024].

Shi, X.; Chen, Z.; Wang, H.; Yeung, D.Y.; Wong, W.K.; Woo, W.C. Convolutional LSTM network: A machinelearning approach for precipitation nowcasting. In Proceedings of the 28th International Conference on NeuralInformation Processing Systems; MIT Press: Montreal, QC, Canada, 2015; pp. 802–810

Shi, X.; Gao, Z.; Lausen, L.; Wang, H.; Yeung, D.Y.; Wong, W.K.; Woo, W.C. Deep learning for precipitationnowcasting: A benchmark and a new model. In Proceedings of the 31st International Conference on NeuralInformation Processing Systems; Curran Associates Inc.: Long Beach, CA, USA, 2017; pp. 5622–5632.

Zhang, P.; Jia, Y.; Gao, J.; Song, W.; Leung, H.K. Short-term rainfall forecasting using multi- layer perceptron.IEEE Trans. Big Data 2018, 6, 93–106. [CrossRef DOI: https://doi.org/10.1109/TBDATA.2018.2871151

Zhu, X.; Li, J.; Zhu, M.; Jiang, Z.; Li, Y. An evaporation duct height prediction method based on deeplearning. IEEE Geosci. Remote Sens. Lett. 2018, 15, 1307–1311. [CrossRef] DOI: https://doi.org/10.1109/LGRS.2018.2842235

Huang, D.; Du, Y.; He, Q.; Song, W.; Liotta, A. Deepeddy: A simple deep architecture for mesoscale oceaniceddy detection in sar images. In Proceedings of the 2017 IEEE 14th International Conference on Networking,Sensing and Control (ICNSC), Calabria, Italy, 16–18 May 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 673–678. DOI: https://doi.org/10.1109/ICNSC.2017.8000171

Lguensat, R.; Sun, M.; Fablet, R.; Tandeo, P.; Mason, E.; Chen, G. EddyNet: A deep neural network forpixel-wise classification of oceanic eddies. In IGARSS 2018—2018 IEEE International Geoscience and RemoteSensing Symposium; IEEE: Piscataway, NJ, USA, 2018; pp. 1764–1767. DOI: https://doi.org/10.1109/IGARSS.2018.8518411

Du, Y.; Song, W.; He, Q.; Huang, D.; Liotta, A.; Su, C. Deep learning with multi-scale feature fusion in remotesensing for automatic oceanic eddy detection. Inf. Fusion 2019, 49, 89–99. [CrossRef] DOI: https://doi.org/10.1016/j.inffus.2018.09.006

Alpaydin, E. Introduction to Machine Learning; MIT Press: Cambridge, UK, 2020.

Zhou, Z. Machine Learning; Tsinghua University Press: Beijing, China, 2016.

Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: Cambridge, UK, 2016.35. Montgomery, M.T.; Sang, N.V.; Smith, R.K.; Persing, J. Do tropical cyclones intensify by WISHE? Q. J.R.Meteorol. Soc. 2009,135,1697–1714. [CrossRef] DOI: https://doi.org/10.1002/qj.459

(PDF) Deep LearningBased Weather Prediction: A Survey. Available from: https://www.researchgate.net/publication/347684364_Deep_Learning- Based_Weather_Prediction_A_Survey [accessed Jun 12 2024].