PREDICTION OF CLIMATOLOGY USING NEURO-FUZZY TECHNIQUES - Namdeo, Ratnesh Kumar; Chandrakar, Omprakash (ShodhKosh: Journal of Visual and Performing Arts, 2023)
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.