ANN Approach for Weather Prediction using Back Propagation
B. Syam Prasad Reddy, Keshav Kumar, B. Musala Reddy, N. Sri Madhava Raja · 2012
Temperature forecasting is important because they are used to protect life and property. Temperature forecasting i s the application of science and technology to predict the state of the temperature for a future time at a given location. Temperature forecasts are made by collecting quantitative data about the current state of the atmosphere. A neural network can learn complex mappings from inputs to outputs, based solely on samples and require limited understanding from trainer, who can be guided by heuristics. In this paper, a neural network-based algorithm for predicting the temperature is presented .The Neural Networks package supports different types of training or learning algorithms .One such algorithm is 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. This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The proposed idea is tested using the real time dataset. The results are compared with practical working of meteorological department and these results confirm that our model have the potential for successful application to temperature forecasting.