Using Additive and Deep Learning Algorithms for Weather Forecasting
Vivek Kakarla
Student, Independence High School, Ashburn, Virginia
18-25
Vol: 14, Issue: 1, 2024
Receiving Date:
2023-11-29
Acceptance Date:
2024-01-29
Publication Date:
2024-02-09
Download PDF
http://doi.org/10.37648/ijrst.v14i01.003
Abstract
One significant element that directly impacts agricultural activities is the weather. The type of crop that should be grown depends significantly on the temperature and humidity of the area. Therefore, farmers need to be aware of upcoming weather trends to plan their farming operations. Although there are several traditional weather prediction services, they are always run by governments and rely on intricate modelling. In this scenario, time series forecasting becomes an effective method of predicting these temperature patterns because it needs historical weather data and very little computer power to provide results. In this work, we examine the steps involved in putting both additive and regression-based models into practice and evaluate their respective performances to determine which strategy is most effective for weather prediction.
Keywords:
Weather Forecasting; Deep Learning Algorithms; Autoregressive Integrated Moving Average (ARIMA)
References
- F. V. Davenport and N. S. Diffenbaugh Using machine learning to analyze physical causes of climate change: A case study of U.S. Midwest extreme precipitation, Geophys. Res. Lett., vol. 48, no. 15, Aug. 2021 Art. no. e2021GL093787
- Krouma, M., Yiou, P., Déandreis, C., and Thao, S.: Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation, Geosci. Model Dev., 15, 4941–4958, https://doi.org/10.5194/gmd-15-4941-2022, 2022.
- Z. Liu, Z. Zhu, J. Gao and C. Xu, 'Forecast Methods for Time Series Data: A Survey,' in IEEE Access, vol. 9, pp. 91896-91912, 2021, doi:10.1109/ACCESS.2021.3091162
- A. Srivastava and A. S, 'Weather Prediction Using LSTM Neural Networks,' 2022 IEEE 7th International conference for Convergence in Technology (I2CT), Mumbai, India, 2022, pp. 1-4, doi: 10.1109/I2CT54291.2022.9824268.
- J. Pant, R. K. Sharma, A. Juyal, D. Singh, H. Pant and P. Pant, 'A Machine-Learning Approach to Time Series Forecasting of Temperature,' 2022 6th International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, 2022, pp. 1125-1129, doi: 10.1109/ICECA55336.2022.10009165.
- Taylor SJ, Letham B. 2017. Forecasting at scale. PeerJ Preprints 5:e3190v2. doi: https://doi.org/10.7287/peerj.preprints.3190v2
- Sepp Hochreiter, Jürgen Schmidhuber; Long Short-Term Memory. Neural Comput 1997; 9 (8): 1735–1780. doi: https://doi.org/10.1162/neco.1997.9.8.1735
- Z. Li, H. Zou and B. Qi, 'Application of ARIMA and LSTM in Relative Humidity Prediction,' 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, 2019, pp. 1544-1549, doi: 10.1109/ICCT46805.2019.8947142.
- Kumar Prakhar, Sountharrajan S, Suganya E, Karthiga M Effective Stock Price Prediction using Time Series Forecasting IEEE 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI)
Back
