An ensemble of neural networks for weather forecasting

Authors:
Imran Maqsood;Riaz Khan;Ajith Abraham
Affiliations:
University of Regina, Faculty of Engineering, Regina, Canada;AMEC Technologies Training and Development Services, Faculty of Engineering, 400-111 Dunsmuir Street, SK S4S 0A2, Vancouver, Canada;Oklahoma State University, Computer Science Department, 400-111 Dunsmuir Street, BC V6B 5W3, Vancouver, USA
Venue:
Neural Computing and Applications
Year:
2004

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Abstract

This study presents the applicability of an ensemble of artificial neural networks (ANNs) and learning paradigms for weather forecasting in southern Saskatchewan, Canada. The proposed ensemble method for weather forecasting has advantages over other techniques like linear combination. Generally, the output of an ensemble is a weighted sum, which are weight-fixed, with the weights being determined from the training or validation data. In the proposed approach, weights are determined dynamically from the respective certainties of the network outputs. The more certain a network seems to be of its decision, the higher the weight. The proposed ensemble model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN), radial basis function network (RBFN), Hopfield model (HFM) predictive models and regression techniques. The data of temperature, wind speed and relative humidity are used to train and test the different models. With each model, 24-h-ahead forecasts are made for the winter, spring, summer and fall seasons. Moreover, the performance and reliability of the seven models are then evaluated by a number of statistical measures. Among the direct approaches employed, empirical results indicate that HFM is relatively less accurate and RBFN is relatively more reliable for the weather forecasting problem. In comparison, the ensemble of neural networks produced the most accurate forecasts.