Simulation and Prediction of Saltwater intrusion Based on Artificial Neural Network
This paper regard the eastern coast plain of Laizhou bay as study area. Considering the factors that affect the saltwater intrusion, the improved Neural Network Model has been applied to construct the random model that can analog the dynamic change of saltwater intrusion. The model parameters are corrected by the data from 1998 to 2003.On this base, according to the exploitation status of the groundwater and the using plan of the water in Laizhou city, two different exploited schemes are designed. After calculation, the dynamic trend of saltwater intrusion is predicted. This research provides the scientific support for the prevention of saltwater intrusion.Saltwater intrusion is a familiar geologic disaster in coast area. 100 years ago, Holland scholar Badon-Cyben and German Scholar Herzberg pointed out Gyben-Herzberg salt-fresh water interface formula. The study of saltwater went through static phase, seep dynamics phase and seep-dispersion dynamics phase. Now great achievement has been gained about the forming mechanism, prevention measure and die transport numerical model of salt-fresh water interface of saltwater intrusion.The city of Laizhou is one of the earliest areas in our country where found the saltwater intrusion. Then people began to takes the relevant measure to prevent the phenomena from curing in dial area. Since the seventies of the 20th century, we have begun to carry on a series of research work. But from 1998, the new situation has appeared in this city, namely the developing state of rollback appears for the first time in some areas. After amounts of simulating and research, FuLin Li proposed: The occurrence of saltwater intrusion rollback was mainly owing to exploitation of a large amount of underground saltwater of sea floor caused by cultivating Dalingping fish in the littoral zone 8. As everyone knows, the developing state of the saltwater intrusion is influenced by many kinds of factors synthetically, and there are complicated non-linear relations between every factors. So obviously, we cant use the linear parameter method to estimate the relation between the exploitation of the underground salt water in the sea floor and development of the saltwater intrusion. Artificial neural network method uses die random nonlinear function of finite subsets by the complex function of neuron function, it is effective to estimate non-linear parameter and choose the structure that routine mathematics statistical method is difficulty to solve. For the purpose of finding inherent relation between the exploitation of the underground salt water in the sea floor and the development of saltwater intrusion, this paper uses the model of improved BP artificial neural network to imitate the phenomenon of saltwater intrusion happening in the east bank of Laizhou bay and cany on macro-forecast to its dynamic change.
Xuequn Chen Fulin Li Yuchao Zhang Lu Chen Mingyuan Fan Panping Wang
Water Conservancy Research Institute of Shandong Province, Jinan 250013, Shandong Water Resources Bureau of Linyi, Linyi 276002, Shandong School of Water Resource and Hydropower, Wuhan University, Wuhan430074, Hubei Water Resources Bureau of Laizhou, Laizhou261400, Shandong
国际会议
The 12th Conference of the International Association for Mathematical Geology(第12届国际数学地质大会)
北京
英文
203-206
2007-08-26（万方平台首次上网日期，不代表论文的发表时间）