Abstract
Problem statement: Accurate weather forecasting plays a vital role fo r planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. Approach: A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological expert s. This study evaluates the performance of Radial Basis Function (RBF) with Back Propagation (BPN) neural network. The back propagation neural network and radial basis function neural network we re used to test the performance in order to investigate effective forecasting technique. Results: The prediction accuracy of RBF was 88.49%. Conclusion: The results indicate that proposed radial basis fu nction neural network is better than back propagation neural network.
Highlights
Weather refer to the condition of air on earth at a given place and time
Weather forecasts are made by collecting quantitative data about the current state of the atmosphere and using scientific understanding of atmospheric processes to project how the atmosphere will evolve
The chaotic nature of the atmosphere implies the need of massive computational power required to solve the equations that describe the atmospheric conditions. This is resulted from incomplete understanding of atmospheric processes which mean that forecasts become less accurate as the difference in time between the present moment and the time for which the forecast is being made increases
Summary
Weather refer to the condition of air on earth at a given place and time. The application of science and technology are to predict the state of the atmosphere in future time for a given location is so important due to its effectiveness in human life (Cheng et al, 2010). The ANN has capability to extract the relationship between the inputs and outputs of a process, without the physics being explicitly provided (Veisi and Jamzad, 2009) These properties of ANN are well suited to the problem of weather forecasting (Abd, 2009). The main purpose is to develop the most suitable ANN architecture and its associated training technique for weather prediction This development will be based on using two different neural network architecture to demonstrate the suitable one for this application. Data assimilation: During the data assimilation process, information gained from the observations is used in conjunction with a numerical model most recent forecast for the time that observations were made to produce the meteorological analysis This is the best estimate of the current state of the atmosphere. A forecaster examines how the features predicted by the computer will interact to produce the day’s weather
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