A hybrid neural–genetic algorithm for reservoir water quality management

Kuo, J.B.; Wang, Yingyi; Lung, Wu‐Seng

doi:10.1016/j.watres.2006.01.046

Cited by 97 publications

(40 citation statements)

References 13 publications

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“…GA is a classical heuristic search algorithm which mimics the thought of natural selection and genetic evolution in Darwin's theory. By the power of evolution, GA can provide an efficient and robust search capability for the optimization problems associated with numerous complex constraints [34,35]. In GA, each potentially feasible or infeasible solution to the problem is encoded as a string of chromosomes.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…In order to obtain better performance, researchers have been constantly developing new technologies and methods for the hydrological prediction. In recent years, many hybrid approaches take advantage of more than one forecasting method to carry out the research work and engineering practice related to the reservoir inflow [34][35][36][37][38][39]. Application results indicate that the hybrid methods have higher forecasting precision than a single forecasting method.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

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Abstract:Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. OPEN ACCESSWater 2015, 7 4478

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

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“…As for the detail of the backpropagation MLP, interested readers can refer to any literatures addressing neural network theory for more information (Freeman and Skapura 1991;Jang et al 1997;Kuo et al 2006). There are several optimization methods to improve the convergence speed and the performance of network training.…”

Section: Back-propagation Neural Network and Leaning Algorithmmentioning

confidence: 99%

“…They are capable of providing a neuron computing approach to solve complex problems. In the last decade, ANNs have been widely successfully applied to various water resources problems, such as hydrological processes (Nayak et al 2004;Sahoo et al 2005;Dastorani et al 2010;Guo et al 2011;Wu and Chau 2011;Senkal et al 2012), water resources management (Kralisch et al 2003;Sreekanth and Datta 2010), groundwater problems (Daliakopoulos et al 2005;Dixon 2005;Garcia and Shigidi 2006;Nayak et al 2006;Ghose et al 2010;Banerjee et al 2011), and water quality (Ha and Stenstrom 2003;Kuo et al 2006;Anctil et al 2009;da Costa et al 2009;Dogan et al 2009;Chang et al 2010;He et al 2011). ANNs also have been used for modeling and forecasting DO (Kuo et al 2007;Singh et al 2009;Ranković et al 2010;Najah et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…ANNs also have been used for modeling and forecasting DO (Kuo et al 2007;Singh et al 2009;Ranković et al 2010;Najah et al 2011). Furthermore, some intelligence algorithm such as genetic algorithm was used with ANN model for the management of the watershed water quality problem (Kuo et al 2006). ANNs, as effective tool for the computation of water quality, can be regarded as a powerful predictive alternative to traditional modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Wen

Fang

Diao

et al. 2012

Environ Monit Assess

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Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl . Cl − was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.

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Auto‐configuring radial basis function networks for chaotic time series and flood forecasting

Chang

Wang

2009

Hydrological Processes

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Abstract:The learning strategy of the radial basis function network (RBFN) commonly uses a hybrid learning process to identify the structure and then proceed to search the model parameters, which is a time-consuming procedure. We proposed an evolutionary way to automatically configure the structure of RBFN and search the optimal parameters of the network. The strategy can effectively identify an appropriate structure of the network by the orthogonal least squares algorithm and then systematically search the optimal locations of centres and the widths of their corresponding kernel function by the genetic algorithm. The proposed strategy of auto-configuring RBFN is first testified in predicting the future values of the chaotic Mackey-Glass time series. The results demonstrate the superiority, on both effectiveness and efficiency, of the proposed strategy in predicting the chaotic time series. We then further investigate the model's suitability and reliability in flood forecast. The Lan-Young River in north-east Taiwan is used as a case study, where the hourly river flow of 23 flood events caused by typhoons or storms is used to train and validate the neural networks. The back propagation neural network (BPNN) is also performed for the purpose of comparison. The results demonstrate that the proposed RBFN has much better performance than the BPNN. The RBFN not only provides an efficient way to model the rainfall-runoff process but also gives reliable and precise one-hour and two-hour ahead flood forecasts.

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A hybrid neural–genetic algorithm for reservoir water quality management

Cited by 97 publications

References 13 publications

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Auto‐configuring radial basis function networks for chaotic time series and flood forecasting

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