A gray system model for studying the response to climatic change: The Liulin karst springs, China

Hao, Yonghong; Yeh, Tian Chyi Jim; Zong-qiang, Gao; Wang, Yanrong; Zhao, Ying

doi:10.1016/j.jhydrol.2006.01.022

Cited by 67 publications

(46 citation statements)

References 12 publications

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“…Hence, based on the results of the GP method during the training phase and testing phase the overfitting problem was able to be determined. Figure 6 indicates that the GM (1, 2) models did not perform as desirable as other case studies have reported (Hao et al, 2006). Despite the fact that they showed high correlation (r ¼ À0.98) during the testing phase, the MAPE values of the GM (1, 2) models were the largest compared with other methods (Table 3).…”

Section: Evolutionary Modeling With Minimal Datasetsmentioning

confidence: 76%

“…Gray system theory (Deng, 1989) provides a way to investigate the relationship of inputoutput process with relatively little data (as low as four), and it has been successfully applied in the fields of finance, engineering, economics, and hydrology (Chen and Lin 1996;Chiao et al, 1997;Hao et al, 2006). In this article, the GM (1, 2) model was used as another comparative tool to forecast the annual streamflow of the West Malian River.…”

Section: Evolutionary Modeling With Minimal Datasetsmentioning

confidence: 99%

“…In this article, the GM (1, 2) model was used as another comparative tool to forecast the annual streamflow of the West Malian River. [Detailed construction process of the gray model is given by Hao et al (2006).] For the GM (1, 2) model, 1 represents the data that need to be predicted (streamflow), and 2 represents the data series that was mainly impacted by 1.…”

Section: Evolutionary Modeling With Minimal Datasetsmentioning

confidence: 99%

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Evolutionary Modeling for Streamflow Forecasting with Minimal Datasets: A Case Study in the West Malian River, China

Wang

et al. 2010

Environmental Engineering Science

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A large dataset is generally needed when modeling hydrological processes. However, for developing countries such as China, datasets are often unavailable in remote areas. An attempt to apply a novel genetic programming (GP) technique was made to model the relationship between streamflow of the West Malian River and the impact of climate change in the northeastern part of China. Available annual streamflow and climatic data were used for training and testing of the GP model. Data from the years between 1982 and 2002 were used for automatic selection of the model relationship. Prediction of the model was undertaken for the period [2003][2004][2005][2006] and the results were compared with measured data. Predicted annual streamflow of the West Malian River agreed with measured data to an acceptable degree of accuracy even with a small amount of dataset. For comparison, a multilayer perceptron method with back propagation algorithm, a gray theory model, and a multiple linear regression model were selected to conduct the prediction with the same dataset. Results showed that the performance of GP method was generally better than other statistical methods such as multilayer perceptron, gray theory model, and multiple linear regression model. Further, the results also showed that the GP method is a useful tool for water resource management, especially in developing countries, to evaluate the potential impacts of climate change on the streamflow when large datasets are unavailable.

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Section: Evolutionary Modeling With Minimal Datasetsmentioning

confidence: 76%

Section: Evolutionary Modeling With Minimal Datasetsmentioning

confidence: 99%

Section: Evolutionary Modeling With Minimal Datasetsmentioning

confidence: 99%

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Evolutionary Modeling for Streamflow Forecasting with Minimal Datasets: A Case Study in the West Malian River, China

Wang

et al. 2010

Environmental Engineering Science

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“…One is the MLP method, one of the most popular ANN architectures (Castellano-Méndez et al, 2004) and the other is the GM method (Deng, 1989), which has been applied successfully in hydrological modeling (Chiao et al, 1997;Trivedi and Singh, 2003;Hao et al, 2006).…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…A small data set usually led to poor performance. Grey theory model (GM) as a potential tool for modeling with a small data set (as few as four) has been successfully used in hydrological process modeling (Trivedi and Singh, 2003;Hao et al, 2006). But the data quality often influenced the performance of the method.…”

mentioning

confidence: 99%

Evolutionary Algorithm for Water Storage Forecasting Response to Climate Change with Small Data Sets: The Wolonghu Wetland, China

Wang

Zheng

et al. 2012

Environmental Engineering Science

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A novel genetic programming (GP) technique, a new method of evolutionary algorithms, was applied to a small data set to predict the water storage of Wolonghu wetland in response to the climate change in the northeastern part of China. Fourteen years (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) of annual water storage and climatic data of the wetland were used for model training and testing. Results of simulations and predictions illustrate a good fit between calculated water storage and observed values (mean absolute percent error = 9.47, r = 0.99). By comparison, a multilayer perceptron method (a popular artificial neural network model) and Grey theory model with the same data set were applied for performance estimation. It was found that GP technique had better performance than the other two methods, in both the simulation step and the predicting phase. The case study confirms that GP method is a promising way for wetland managers to make a quick estimation of fluctuations of water storage in some wetlands under the limitation of a small data set.

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Gray Relational Analysis

2021

A Handbook on Multi‐Attribute Decision‐Making Methods

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A gray system model for studying the response to climatic change: The Liulin karst springs, China

Cited by 67 publications

References 12 publications

Evolutionary Modeling for Streamflow Forecasting with Minimal Datasets: A Case Study in the West Malian River, China

Evolutionary Modeling for Streamflow Forecasting with Minimal Datasets: A Case Study in the West Malian River, China

Evolutionary Algorithm for Water Storage Forecasting Response to Climate Change with Small Data Sets: The Wolonghu Wetland, China

Gray Relational Analysis

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