Evolutionary modeling for streamflow forecasting with minimal datasets: A case study in the West Malian River, China

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Ni2010377,

• author = "Qingwei Ni and Li Wang2 and Renzhen Ye and Fenglin Yang and Muttucumaru Sivakumar",
• title = "Evolutionary modeling for streamflow forecasting with minimal datasets: A case study in the West Malian River, China",
• journal = "Environmental Engineering Science",
• year = "2010",
• volume = "27",
• number = "5",
• pages = "377--385",
• month = may # " 7",
• keywords = "genetic algorithms, genetic programming, Annual streamflow, Automatic selection, Climatic data, Data sets, Degree of accuracy, GP algorithm, Gray theories, Hydrological process, Large datasets, Measured data, Model relationships, Multi layer perceptron, Multiple linear regression models, Potential impacts, Precipitation, Remote areas, Streamflow forecasting, Training and testing, Water resource management, Algorithms, Climate change, Data flow analysis, Developing countries, Electric loads, Evaporation, Forecasting, Genetic programming, Linear regression, Multi-layers, Rivers, Statistics, Stream flow, Water management, Climate models, accuracy, article, back propagation, China, climate change, controlled study, data analysis, developing country, evolutionary algorithm, forecasting, hydrology, multiple regression, perceptron, prediction, stream (river), water flow, water management, Malia, GP algorithm, Statistical methods, West Malian River",
• ISSN = "10928758",
• URL = "http://online.liebertpub.com/doi/abs/10.1089/ees.2009.0082",
• DOI = "doi:10.1089/ees.2009.0082",
• size = "11 page",
• abstract = "A large dataset is generally needed when modelling 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 2003-2006 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.",
• affiliation = "School of Environmental and Biological Science and Technology, Dalian University of Technology, Dalian, China; College of Environmental and Biological Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China; Department of Mathematics, Agricultural University of Huazhong, Wuhan, China; School of Civil, Mining and Environmental Engineering, University of Wollongong, Wollongang, NSW, Australia",
• correspondence_address1 = "Wang, L.; College of Environmental and Biological Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China; email: wanglijohn@hotmail.com",
• language = "English",
• document_type = "Article",

}

Genetic Programming entries for Qingwei Ni Li Wang2 Renzhen Ye Fenglin Yang Muttucumaru Sivakumar

Citations