Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model

Ju, Qin; Yu, Zhongbo; Hao, Zhenchun; Ou, Gengxin; Zhao, Jianshi; Liu, Dedong

doi:10.1016/j.neucom.2008.12.032

Cited by 111 publications

(53 citation statements)

References 49 publications

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“…The discrete values, 0 and 1, are selected as the input sampling signals. By comparing the network output signals and the expected output signals to generate the error signals, the weight coefficients of the learning system can be rectified based through iterative adjustments to minimize the errors until reaching an acceptable range [28]. In this process, the expected output signals are regarded as the teacher signals, which are compared with the actual output, and the errors produced are applied to rectify the weight coefficients.…”

Section: The Description Of the Annmentioning

confidence: 99%

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Chen

Cheng

Wang

et al. 2017

Entropy

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Event-based runoff-pollutant relationships have been the key for water quality management, but the scarcity of measured data results in poor model performance, especially for multiple rainfall events. In this study, a new framework was proposed for event-based non-point source (NPS) prediction and evaluation. The artificial neural network (ANN) was used to extend the runoff-pollutant relationship from complete data events to other data-scarce events. The interpolation method was then used to solve the problem of tail deviation in the simulated pollutographs. In addition, the entropy method was utilized to train the ANN for comprehensive evaluations. A case study was performed in the Three Gorges Reservoir Region, China. Results showed that the ANN performed well in the NPS simulation, especially for light rainfall events, and the phosphorus predictions were always more accurate than the nitrogen predictions under scarce data conditions. In addition, peak pollutant data scarcity had a significant impact on the model performance. Furthermore, these traditional indicators would lead to certain information loss during the model evaluation, but the entropy weighting method could provide a more accurate model evaluation. These results would be valuable for monitoring schemes and the quantitation of event-based NPS pollution, especially in data-poor catchments.

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Section: The Description Of the Annmentioning

confidence: 99%

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Chen

Cheng

Wang

et al. 2017

Entropy

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“…model BP neural network adopts the multi-layer forward feedback network and the back-propagation algorithm, with a strong nonlinear mapping processing capacity (Ju et al 2009), which constantly adjusts the network weights through forward propagation of learning information and back propagation of error information, so that the output value is close to the measured data as much as possible (Abdi et al 1996). Three-layer BP neural network can map or approach the vast majority of rational functions (El-Din and Smith, 2002).…”

Section: Establishment Of Neural Network Predictionmentioning

confidence: 99%

Application of Grey Prediction and BP Neural Network in Hydrologic Prediction

Huang

Jin

Xiang

et al. 2015

MATEC Web of Conferences

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Based on the data of annual runoff at Boluo Hydrologic Station in the Dongjiang River of Guangdong Province from 1954 to 2010, this paper establishes a prediction model of an annual runoff through three methods, that is, the regression analysis, the grey theory and the neural network. The prediction model which is established by the regression analysis method has passed F-statistical test (P=0.05), but the relative error of predicted value at 30% of the data point is over ±30%, and its prediction precision is general. The precision of the residual prediction model of an annual runoff GM (1, 1) that established based on the grey theory is obviously better than that of the former one; the relative error of predicted value at only 10% of the data point is over ±30%; the Nash statistical coefficient (NS) of predicted value and measured value is 0.627, and the correlation coefficient (R 2 ) is 0.774. For the prediction model of BP neural network, the relative error at about 5% of the data point is over ±30%, and NS=0.66, R 2 =0.853. In general, the precision and the reliability of the neural network prediction model of an annual runoff are the best.

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“…The Xianjiang model is a conceptual watershed model, which was originally developed for simulating streamflow at the daily scale or flood-event scale (Zhao, 1992;Ju et al, 2009). The key concept of the model is that runoff is only generated where the field capacities are reached.…”

Section: Theory and Data Preparation Of The Xinanjiang Modelmentioning

confidence: 99%

Impact of Global Climate Change on Regional Water Resources: A Case Study in the Huai River Basin

Ju¹,

Hao²,

Geng-xin³

et al. 2012

Climate Models

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Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model

Cited by 111 publications

References 49 publications

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Application of Grey Prediction and BP Neural Network in Hydrologic Prediction

Impact of Global Climate Change on Regional Water Resources: A Case Study in the Huai River Basin

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