A hybrid neural network model for typhoon-rainfall forecasting
Journal
Journal of Hydrology
Journal Volume
375
Journal Issue
3-4
Pages
450-458
Date Issued
2009
Author(s)
Wu, M.-C.
Abstract
A hybrid neural network model is proposed in this paper to forecast the typhoon rainfall. Two different types of artificial neural networks, the self-organizing map (SOM) and the multilayer perceptron network (MLPN), are combined to develop the proposed model. In the proposed model, a data analysis technique is developed based on the SOM, which can perform cluster analysis and discrimination analysis in one step. The MLPN is used as the nonlinear regression technique to construct the relationship between the input and output data. First, the input data are analyzed using a SOM-based data analysis technique. Through the SOM-based data analysis technique, input data with different properties are first divided into distinct clusters, which can help the multivariate nonlinear regression of each cluster. Additionally, the topological relationships among data are discovered from which more insight into the typhoon-rainfall process can be revealed. Then, for each cluster, the individual relationship between the input and output data is constructed by a specific MLPN. For evaluating the forecasting performance of the proposed model, an application is conducted. The proposed model is applied to the Tanshui River Basin to forecast the typhoon rainfall. The results show that the proposed model can forecast more precisely than the model developed by the conventional neural network approach. © 2009 Elsevier B.V. All rights reserved.
Subjects
Hybrid neural network; Multilayer perceptron network; Self-organizing map; Typhoon-rainfall forecasting
SDGs
Other Subjects
Artificial Neural Network; Data analysis techniques; Discrimination analysis; Forecasting performance; Hybrid neural network; Hybrid neural networks; Input and outputs; Input datas; Multi-layer perceptron networks; Multilayer perceptron network; Multivariate non-linear regression; Nonlinear regression technique; One step; Rainfall forecasting; Self-organizing map; Tanshui river basin; Topological relationships; Typhoon rainfall; Typhoon-rainfall forecasting; Cluster analysis; Conformal mapping; Electric loads; Forecasting; Hurricanes; Input output programs; Multilayers; Strength of materials; Multilayer neural networks; artificial neural network; climate modeling; cluster analysis; discriminant analysis; multivariate analysis; nonlinearity; rainfall; regression analysis; river basin; typhoon; weather forecasting; Asia; Eurasia; Far East; Taiwan; Tanshui River
Type
journal article