Abstract
Weather forecasting is a widely researched area in time series forecasting due to the necessity of accurate weather forecasts in various human activities. Out of numerous weather forecasting techniques Artificial Neural Networks (ANN) methodology is one of the most widely used techniques. In this study the application of Neural Network Ensembles in Rainfall Forecasting is investigated by using various types of Ensemble Neural Networks (ENN) to forecast the rainfall in Colombo, Sri Lanka. Ensembles are generated by changing the network architecture, changing initial weights of the ANN and changing the ANN type. Two ensembles one consisting of a collection of networks of various architectures of Multi Layer Feed Forward Network with Back Propagation Algorithm (BPN), and the other consisting of a combination of BPN, Radial Basis Function Network (RBFN) and General Regression Neural Network (GRNN). The performance of ensembles are compared with the performance of BPN, RBFN and GRNN. The ANNs are trained, validated and tested using daily observed weather data for 41 years. The results of our experiment show that the performance of the ensemble models are better than the performance of the other models for this application and that changing the network type gives better results than changing the architecture of the ANN.DOI: http://dx.doi.org/10.4038/icter.v6i2.7151International Journal on Advances in ICT for Emerging Regions (ICTer), 2013;v.6(2)
Highlights
Weather forecasting is predicting the state of the atmosphere for a certain location for a certain time period
Each network was trained several times with different initial weights and it was noted that the Root Mean Square Error (RMSE) value for testing decreases when the number of nodes are increased and it starts to increase again
ENN1 was created with varying the network architecture and initial weights and ENN2 was created with varying the network type and initial weights
Summary
Weather forecasting is predicting the state of the atmosphere for a certain location for a certain time period. Using mathematical models of the atmosphere to predict future weather based on current weather conditions is called numerical weather prediction. This needs full knowledge of atmospheric dynamics and involves calculations with a large number of variables and huge datasets. This process requires a lot of computational resources due to the advancement of modern computer hardware there have been many improvements in numerical weather prediction [1]. Still there are difficulties in short term weather predictions because of sudden atmospheric changes
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