Using Deep Learning Technique to Predict the Weather Forecasting

Dr. A. C. Subhajini

Zenodo (CERN European Organization for Nuclear Research) · 10 April 2021

In one sentence. Compares backpropagation, RBF, GRNN and deep networks on ten years of Kanyakumari district data, finding deep learning most accurate.

Abstract

Weather Forecasting is the task of determining future state of the atmosphere. Accurate weather forecasting is very important because agricultural and industrial sector largely depend on it. Weather forecasting has become an important field of research in the last few decades. In most of the cases the researcher had attempted to establish a linear relationship between the input weather data and the corresponding target data. The Neural Networks package supports different types of training or learning algorithms. The purpose of this paper is to forecast the weather using machine learning techniques. In any machine learning technique, the most important thing for any model is data. With proper and clean data we can use many models to accurately predict the weather. In this paper, the application of neural networks to study the design of neural network technique for Kanyakumary District,Tamil Nadu, India. A total of ten years of data collected for training the net work. The network is trained using the Back propagation Algorithm, Radial Basis Function, Regression Neural Network, Optical Neural Network, Fuzzy ARTMAP and Deep learningNeural Network and Deep learning network. The deep learning network can give the best overall results in terms of accuracy and training time. It is better correlated compared to the BPN,RBFN,GRNN,ONN and Fuzzy ARTMAP networks. The proposed deep learning neural network can also overcome several limitations such as a highly non-linear weight update and the slow convergence rate.

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