Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis
Computers & Geosciences
Multiresponse sparse regression with application to multidimensional scaling
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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In this paper, we propose a novel hybrid multi-model approach for rainfall forecasting. In this multi-model system we have incorporated an efficient input selection technique, a set of distinct predictive models with carefully selected parameter settings, a variable selection method to rank (weight) the models before combining their outputs and a simple weighted average to combine the forecasts of all the models. The input selection technique is based on auto correlation and partial autocorrelation function, the predictive models are stepwise linear regression, partial least square regression, multivariate adaptive regression spline, radial basis kernel gaussian process and multi layer perceptron with quasi Newton optimization. The model ranking technique is based multi response sparse regression, which rank the variables (here models) according to their predictive performance (here forecasting). We have utilized this rank to use it as the wegiht in the weighted average of the forecast combination of the models. We have applied this novel multi model approach in forecasting daily rainfall of rainy season of Fukuoka city of Japan. We have used several performance metrics to quantify the predictive quality of the hybrid model. The results suggest that the novel hybrid multi-model approach can make efficient and persistent short term rainfall forecast.