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Open AccessArticle

Physical Hybrid Neural Network Model to Forecast Typhoon Floods

1
Construction and Disaster Prevention Research Center, Feng Chia University, Taichung 407, Taiwan
2
Department of Water Resources Engineering and Conservation, Feng Chia University, Taichung 407, Taiwan
*
Author to whom correspondence should be addressed.
Water 2018, 10(5), 632; https://doi.org/10.3390/w10050632
Received: 10 April 2018 / Revised: 4 May 2018 / Accepted: 10 May 2018 / Published: 13 May 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
This study proposed a hybrid neural network model that combines a self-organizing map (SOM) and back-propagation neural networks (BPNNs) to model the rainfall-runoff process in a physically interpretable manner and to accurately forecast typhoon floods. The SOM and a two-stage clustering scheme were applied to group hydrologic data into four clusters, each of which represented a meaningful hydrologic component of the rainfall-runoff process. BPNNs were constructed for each cluster to achieve high forecasting capability. The physical hybrid neural network model was used to forecast typhoon flood discharges in Wu River in Taiwan by using two types of rainfall data. The clustering results demonstrated that the rainfall-runoff process was favorably described by the sequence of derived clusters. The flood forecasting results indicated that the proposed hybrid neural network model has good forecasting capability, and the performance of the models using the two types of rainfall data is similar. In addition, the derived lagged inputs are hydrologically meaningful, and the number and activation function of the hidden nodes can be rationally interpreted. This study also developed a traditional, single BPNN model trained using the whole calibration data for comparison with the hybrid neural network model. The proposed physical hybrid neural network model outperformed the traditional neural network model in forecasting the peak discharges and low flows. View Full-Text
Keywords: hybrid neural network; flood forecasting; self-organizing map hybrid neural network; flood forecasting; self-organizing map
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MDPI and ACS Style

Jhong, Y.-D.; Chen, C.-S.; Lin, H.-P.; Chen, S.-T. Physical Hybrid Neural Network Model to Forecast Typhoon Floods. Water 2018, 10, 632.

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