NDLI: GCA-CG Based Groundwater Level Prediction with Uncertainty in Lower Reaches of Tarim River

Content Provider	IEEE Xplore Digital Library
Author	Yue Chen Yuhong Li
Copyright Year	2009
Abstract	It is well known that no uniform prediction approaches were obtained regarding ground water level, though the neural network and some other so-called artificial intelligence methods consistently provide the smallest uncertainty and different medians warranting further research on their abilities. In the present paper, the lower reaches of Tarim River is taken as the study area, a grey correlation analysis and cloud generator (GCA-CG) based groundwater level prediction model is proposed. The most important characteristic feature of the novel model is that the observation data with uncertainty is taken into consideration. First of all, based on the GCA theory, the most important influencing indicator of groundwater level is selected. And then, the CG of knowledge reasoning is applied to predict the groundwater level. Finally, a numerical experiment based on the historical observation data is performed to verify the presented ground water level prediction model, which shows us that the fitting precision is 91.09% before water transportation and 87.84% after the water transportation. From the theoretic foundation and experiment results, we can see that the model could be widely used in other systems with uncertainty.
Starting Page	589
Ending Page	592
File Size	288407
Page Count	4
File Format	PDF
ISBN	9780769538198
DOI	10.1109/ICEET.2009.380
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Chemical analysis Clouds Grey correlation analysis (GCA) Transportation Predictive models Groundwater level prediction Rivers Character generation Cloud generator (CG) Soil Tarim river Water resources Water salinity Power engineering and energy
Content Type	Text
Resource Type	Article

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