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Intelligent System for Weather Prediction

  • Vyom UnadkatEmail author
  • Sneh Gajiwala
  • Prachi Doshi
  • Mitchell D’silva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

The emergence of deep learning techniques in the recent years coupled with the wide accessibility of colossal weather observations has inspired many researchers and technology enthusiasts to identify hierarchical patterns and interdependencies in large datasets for weather forecasting. The advent of technology has enabled us to obtain such forecasts using complex mathematical models, instead of simple analysis and conjecture. In the past few years, intelligent learning models such as neural networks and fuzzy logic have shown much better results as compared to the primitive approaches. Thus, to solve this issue, this paper aims to put forward a hybrid model developed using the concepts of ARIMA and LSTM to predict the weather and assist in the planning of a daily routine.

Keywords

Convolutional neural networks K-nearest neighbour Recurrent Neural Networks 

References

  1. 1.
    Lai, L.L., et al.: Intelligent weather forecast. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826), Shanghai, China, vol. 7, pp. 4216–4221 (2004)Google Scholar
  2. 2.
    Diyankov, O.V., Lykov, V.A., Terekhoff, S.A.: Artificial neural networks in weather forecasting. In: Proceedings of the 1992 RNNS/IEEE Symposium on Neuroinformatics and Neurocomputers, Rostov-on-Don, Russia, vol. 2, pp. 829–835 (1992)Google Scholar
  3. 3.
    Grover, A., Kapoor, A., Horvitz, E.: A deep hybrid model for weather forecasting. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2015)Google Scholar
  4. 4.
    Saima, H., Jaafar, J., Belhaouari, S., Jillani, T.A.: Intelligent methods for weather forecasting: a review. In: 2011 National Postgraduate Conference, Kuala Lumpur, pp. 1–6 (2011)Google Scholar
  5. 5.
    Wibisono, M.N., Ahmad, A.S.: Weather forecasting using Knowledge Growing System (KGS). In: 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, pp. 35–38 (2017)Google Scholar
  6. 6.
    Tensorflow: TensorFlow Guide. https://www.tensorflow.org/guide/. Accessed 22 Sept 2018
  7. 7.
    TensorFlow: Keras. https://www.tensorflow.org/guide/keras. Accessed 22 Sept 2018
  8. 8.
    NOAA: National Centers for Environmental Information. https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00023272/detail. Accessed 18 Sept 2018
  9. 9.
    Singh, G.: 7 methods to perform time series forecasting. https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/. Accessed 18 Sept 2018
  10. 10.
    Morisson, J.: Autoregressive Integrated Moving Average Models (ARIMA). http://www.forecastingsolutions.com/arima.html. Accessed 19 Sept 2018
  11. 11.
    Wikipedia: Autoregressive integrated moving average. https://en.m.wikipedia.org/wiki/Autoregressive_integrated_moving_average. Accessed 20 Sept 2018
  12. 12.
    Godbout, C.: Recurrent neural networks for beginners. https://medium.com/@camrongodbout/recurrent-neural-networks-for-beginners-7aca4e933b82. Accessed 19 Sept 2018
  13. 13.
    Donges, N.: Recurrent neural networks and LSTM. https://towardsdatascience.com/recurrent-neural-networks-and-lstm-4b601dd822a5. Accessed 19 Sept 2018
  14. 14.
    Mahalakshmi, G., Sridevi, S., Rajaram, S.: A survey on forecasting of time series data. In: 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE 2016), Kovilpatti, pp. 1–8 (2016)Google Scholar
  15. 15.
    Geetha, A., Nasira, G.M.: Time series modeling and forecasting: tropical cyclone prediction using ARIMA model. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 3080–3086 (2016)Google Scholar
  16. 16.
    Karim, F., Majumdar, S., Darabi, H., Chen, S.: LSTM fully convolutional networks for time series classification. IEEE Access 6, 1662–1669 (2018)CrossRefGoogle Scholar
  17. 17.
    Abhishek, K., Singh, M.P., Ghosh, S., Anand, A.: Weather forecasting model using artificial neural network. Procedia Technol. 4, 311–318 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Vyom Unadkat
    • 1
    Email author
  • Sneh Gajiwala
    • 1
  • Prachi Doshi
    • 1
  • Mitchell D’silva
    • 1
  1. 1.Dwarkadas J. Sanghvi College of EngineeringMumbaiIndia

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