NDLI: Prediction of salt water intrusion using BP-RAGA coupled neural network model

Content Provider	IEEE Xplore Digital Library
Author	Xiaolei Dong Tao Tao Suiqing Liu Yu Xia Yang Yu
Copyright Year	2010
Description	Author affiliation: College of Environmental Science and Engineering Tongji University Shanghai, China (Xiaolei Dong; Tao Tao; Suiqing Liu; Yu Xia; Yang Yu)
Abstract	Tidal river is often intruded by salt water in dry season. The deterioration of water quality will lower the reliability of drinking water. Raw water systems, which include reservoirs, pump stations and pipelines, were constructed in costal cities. In order to guarantee the safety of drinking water, precise plans of raw water system must be drawn up. So it is necessary to predict salt water intrusion in water sources precisely. The period that water source can not provide raw water is selected as indicator of salt water intrusion. Artificial neural network is applied to develop a salt water intrusion prediction model. Real coding based accelerating genetic algorithm (RAGA) and Back propagation (BP) algorithm are applied to optimize the weights of neural network. Tidal range of water resource, the period that water source can not supply raw water and observed flows in upstream hydrological station in last day are selected as influencing factors. So a BP-RAGA prediction model is developed in this paper. And the model was applied to predict salt water intrusion in PingGang water source of Zhuhai city in this paper. The BP-RAGA coupled neural network model proved to be superior to BP neural network model in precision.
Starting Page	4244
Ending Page	4248
File Size	1190471
Page Count	5
File Format	PDF
ISBN	9781424465132
e-ISBN	9781424465163
DOI	10.1109/CISP.2010.5646891
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	artificial neural network back propagation algorithm Computational modeling Neurons Artificial neural networks Predictive models real coding based accelerating genetic algorithm Prediction algorithms Rivers Water resources prediction of salt water intrusion component
Content Type	Text
Resource Type	Article

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