Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Neural network-based mean-variance-skewness model for portfolio selection
Computers and Operations Research
Expert Systems with Applications: An International Journal
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Modeling meteorological prediction using particle swarm optimization and neural network ensemble
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
An application of artificial neural networks for rainfall forecasting
Mathematical and Computer Modelling: An International Journal
Parallel consensual neural networks
IEEE Transactions on Neural Networks
Prediction of rainfall time series using modular RBF neural network model coupled with SSA and PLS
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Hybrid PSO and GA for neural network evolutionary in monthly rainfall forecasting
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Hybird evolutionary algorithms for artificial neural network training in rainfall forecasting
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.