Development and application of a decision group Back-Propagation Neural Network for flood forecasting

Chen, Chang-Shian; Chen, Boris Po-Tsang; Chou, Frederick Nai-Fang; Yang, Chao-Chung

doi:10.1016/j.jhydrol.2010.02.019

Cited by 52 publications

(27 citation statements)

References 26 publications

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“…An advantage of using the LTF is that the lagged variables can be objectively determined by the statistical significance test. The process of using the LTF and the statistical significance test to determine the lags of input variables can be found in Chen et al [38]. The time step for the analysis of lags and the following flood forecasting is one hour in this study.…”

Section: Determining the Input Variablesmentioning

confidence: 99%

Physical Hybrid Neural Network Model to Forecast Typhoon Floods

Jhong

Chen

Lin

et al. 2018

Water

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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.

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Section: Determining the Input Variablesmentioning

confidence: 99%

Physical Hybrid Neural Network Model to Forecast Typhoon Floods

Jhong

Chen

Lin

et al. 2018

Water

Self Cite

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“…RBFN-BP is three-layered feed-forward neural network where the functioning of back propagation examines through the learning (training), testing, and validation. It is applied in various disciplines such as prediction of drill flank wear (Panda et al 2008;Xu et al 2014), determination kinetic spectrophotometric of nitroaniline isomers (Hasani and Emami 2008), estimation of furnace exit gas temperature (Chandok et al 2008), estimation of suspended sediment load (Alp and Cigizoglu 2007), and hydrometeorological parameters (Sedki et al 2009;Ghose et al 2010;Chen et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

Petković

Gocić

Shamshirband

et al. 2015

Theor Appl Climatol

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Accurate estimation of the reference evapotranspiration (ET 0 ) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman-Monteith equation was used as reference equation to estimate ET 0 for Serbia during the period of 1980-2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R 2 ). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET 0 estimation.

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“…For example, to improve flood forecasting accuracy, Chen (2010) used the decision group backpropagation network (DGBPN), while Yang and Chen (2009) integrated the linear transfer function (LTF) and self-organizing map (SOM) to efficiently determine the intervals of weights and biases. In addition, Odan and Reis (2012) used the multilayer perceptron with the back-propagation algorithm (MLP-BP) for water demand forecasting, while Anctil and Tape (2004) used an artificial neural network and wavelet hybrid model for rainfall-runoff forecasting.…”

Section: Discussionmentioning

confidence: 99%

Improvement of mid- to long-term runoff forecasting based on physical causes: application in Nenjiang basin, China

Tian

et al. 2013

Hydrological Sciences Journal

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An artificial neural network, mid-to long-term runoff forecasting model of the Nenjiang basin was established by deciding predictors using the physical analysis method, combined with long-term hydrological and meteorological information. The forecasting model was gradually improved while considering physical factors, such as the main flood season and non-flood season by stage, runoff sources and hydrological processes. The average relative errors in the simulation tests of the prediction model were 0.33 in the main flood season and 0.26 in the non-flood season, indicating that the prediction accuracy during the non-flood season was greater than that in the main flood season. Based on these standards, forecasting accuracy evaluation was conducted by comparing forecasting results with actual conditions: for 2001 to 2003 data, the pass rate of forecasting in the main flood season was 50%, while it was 93% in the non-flood season; for 2001-2010, the respective values were 45% and 72%. The accuracy of prediction was found to decrease as the length of record increases.Key words physical causes; mid-to long-term runoff forecasting; Nenjiang basin; China Amélioration de la prévision à base physique des débits à moyen et long terme: application au bassin de la rivière Nenjiang, en Chine Résumé Nous avons établi un modèle de réseau de neurones artificiels pour la prévision des débits à moyen et long terme sur le bassin de la rivière Nenjiang, en choisissant des prédicteurs par une méthode d'analyse physique, combinés à des informations hydrologiques et météorologiques à long terme. Le modèle de prévision a été progressivement amélioré en tenant compte de facteurs physiques, comme le niveau de l'eau, les sources de ruissellement et les processus hydrologiques pendant la saison des fortes crues et celle des étiages,. Les erreurs relatives moyennes des tests de simulation du modèle ont été de 0,33 pendant la saison des fortes crues, et de 0,26 dans la saison des étiages, ce qui indique que la précision de la prévision durant la saison des étiages est supérieure à celle de la saison des fortes crues. Sur la base de ces critères, l'évaluation de la précision des prévisions a été réalisée en comparant les résultats de prévision avec les conditions réelles : pour les données de 2001 à 2003, le taux de succès des prévisions pour le saison des fortes crues était de 50%, alors qu'il était de 93% pendant la saison des étiages; pour la période 2001-2010, les valeurs étaient respectivement de 45% et 72%. Nous avons trouvé que la précision des prévisions diminuait à mesure que la longueur de l'enregistrement augmentait.

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Development and application of a decision group Back-Propagation Neural Network for flood forecasting

Cited by 52 publications

References 26 publications

Physical Hybrid Neural Network Model to Forecast Typhoon Floods

Physical Hybrid Neural Network Model to Forecast Typhoon Floods

Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

Improvement of mid- to long-term runoff forecasting based on physical causes: application in Nenjiang basin, China

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