Abstract
Recent advances in neural networks algorithm solve many complex problems in real world. Neural networks were applied to many non-linear prediction domains such as speech recognition, computer vision, machine learning and other areas. One of such challenging phenomena is applying neural networks to weather predictions and climate modelling. Because of its large size and complex data, it is difficult to predict. In this research work, climate factors were taken to make predictions using novel tollgate neural networks (TNN) algorithm. TNN algorithm is projected with quick training methodology rather than conventional approach. The advantage of this versatile model is that it can successfully incorporate the useful features by pruning the unnecessary data during training. Therefore it is intriguing algorithm to approach climatic patterns to predict certain notable variations in the area under study. Experimental results proclaim that this framework of the algorithm is efficient, adaptable and outperforms popular methods when tested on different climatic patterns. It also helps to form an proficient learning and optimisation platform for other large scale complex problems.
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Acknowledgements
In this research work, 100 years rainfall data of Chennai district data is collected form district wise monthly annual rainfall database of India water portal website link: http://www.indiawaterportal.org/articles/meteorological-datasets-download-entire-datasets-various-meteorological-indicators-1901.
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Raj, A.S., Viswanath, J., Oliver, D.H. et al. Tollgate neural networks (TNN) model with time bound learning methodology for futuristic approach in climatic data analysis. Model. Earth Syst. Environ. 4, 1331–1339 (2018). https://doi.org/10.1007/s40808-018-0495-0
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DOI: https://doi.org/10.1007/s40808-018-0495-0