PREDICTION OF CLIMATOLOGY USING NEURO-FUZZY TECHNIQUES

Ratnesh Kumar Namdeo; Omprakash Chandrakar

doi:10.29121/shodhkosh.v4.i2.2023.1961

Authors

Ratnesh Kumar Namdeo Research Scholar, MSIT, MATS University Raipur
Dr. Omprakash Chandrakar Professor, MSIT, MATS University Raipur

DOI:

https://doi.org/10.29121/shodhkosh.v4.i2.2023.1961

Keywords:

Artificial Neural Networks, AI Weather Forecast, Machine Learning, Weather Forecasting, Weather Prediction

Abstract [English]

For social and economic activists, weather forecasting is the most talked about topic right now. Due to its applicability in a number of public and private industries, such as forestry, air traffic, agriculture, and maritime, it is also garnering widespread interest. Recent events have caused alterations in the climate. Occurat a rapid pace, rendering traditional weather forecasting techniques less accurate, more time-consuming, and erratic. To solve these, more advanced and effective weather forecast techniques are required. Challenges. Through actual evidence, we show that artificial neural networks generate significantly less deviations than GDAS assessment. Thus, almost exact weather forecast results are predicted. This article investigates the forecasting performance of neural networks in comparison to linear and polynomial approximations for a time series produced by the chaotic Mackey-Glass differential delay equation. There is one step ahead in the predicting horizon. A basic neural network with two neurons is used in a series of regressions with polynomial approximators, and the numerous correlation coefficients are compared. A nearly perfect forecast is produced by the neural network, a very basic neural network that outperforms polynomial expansions. Lastly, over a broad range of realizations, the neural network outperforms the other techniques in terms of precision.

References

Weather Research & Forecasting Model (WRF) | Mesoscale & Microscale Meteorology Laboratory. (n.d.). NCAR.

Kumar, Manish, Deepak Kumar Gupta, and Samayveer Singh. "Extreme event forecasting using machine learning models." Advances in Communication and Computational Technology: Select Proceedings of ICACCT 2019. Springer Singapore, (2021).

Wong, Ken CL, et al. "Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion." arXiv preprint arXiv:2112.05254 (2021).

Waldvogel, Ann-Marie, et al. "Evolutionary genomics can improve prediction of species’ responses to climate change." Evolution Letters 4.1 (2020): 4-18. DOI: https://doi.org/10.1002/evl3.154

Kumar, Manish, Deepak Kumar Gupta, and Samayveer Singh. "Extreme event forecasting using machine learning models." Advances in Communication and Computational Technology: Select Proceedings of ICACCT 2019. Springer Singapore, (2021). DOI: https://doi.org/10.1007/978-981-15-5341-7_115

Wong, Ken CL, et al. "Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion." arXiv preprint arXiv:2112.05254 (2021).

Ardabili, Sina, et al. "Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review." Engineering for Sustainable Future: Selected

papers of the 18th International Conference on Global Research and Education Inter-Academia–2019 18. Springer International Publishing, 2020

Kashinath, Karthik, et al. "Physics-informed machine learning: case studies for weather and climate modelling." Philosophical Transactions of the Royal Society A 379.2194 (2021): 20200093. DOI: https://doi.org/10.1098/rsta.2020.0093

Held, Isaac M., and Brian J. Soden. "Water vapor feedback and global warming." Annual review of energy and the environment 25.1 (2000): 441-475. DOI: https://doi.org/10.1146/annurev.energy.25.1.441

Saima, H., Jaafar, J., Belhaouari, S., & Jillani, T. A. (2011). Intelligent methods for weather forecasting: A review. In 2011 National Postgraduate Conference (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/NatPC.2011.6136289

Baboo R., Shereef I., “An Efficient Weather Forecasting System using Artificial Neural Network,” International Journal of Environmental Science and Development, Vol. 1, No. 4, October 2010 ISSN: 2010-0264. DOI: https://doi.org/10.7763/IJESD.2010.V1.63

Biswas S., Marbaniang L., Purkayastha B., Chakraborty M., Singh H., Bordoloi M., “Rainfall forecasting by relevant attributes using artificial neural networks – a comparative study,” Int. J. Big Data Intelligence, Vol. 3, No. 2, 2016. DOI: https://doi.org/10.1504/IJBDI.2016.077362

Ali S., Shahbaz M., “Streamflow forecasting by modelling the rainfall–streamflow relationship using artificial neural networks,” Modelling Earth Systems and Environment, Springer Nature Switzerland AG 2020. DOI: https://doi.org/10.1007/s40808-020-00780-3

Darji M., “Rainfall Forecasting Using Neural Networks,” Rainfall ForecastingUsing Neural Networks,” https://www.researchget.net/publication/34315449 Conference Paper on June2019.

Haviluddinb M., Hardwinartoc S., Aipassae M., “Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan – Indonesia,” International Conference on Computer Science and Computational Intelligence (ICCSCI 2015).