Weather Prediction Using Artificial Intelligence

Weather forecasting has become an important field of research in the last few decades. Daily Weather forecasting is used for multiple reasons in multiple areas like agriculture, energy supply, transportations, etc. In this project, a neural network- based algorithm for predicting the temperature will be implemented. The Neural Networks package supports different types of training or learning algorithms. One such algorithm is the Back Propagation Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate a large class of functions. The proposed idea is tested using a real- time dataset. Backpropagation is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network. Backpropagation is shorthand for “the backward propagation of errors ” since an error is computed at the output and distributed backward throughout the network’s layers. It is commonly used to train deep neural networks a term referring to neural networks with more than one hidden layer.