Study On Water Quality Time Series Data Mining And Application Integration

As the water level of Three Gorges Reservoir rising, it has brought great influence to the reservoir water quality. To further grasp the reservoir water quality conditions to ensure the water environment safety of the reservoir, The environmental protection departments has gradually established more and more water quality monitoring systems for the Three Gorges Reservoir, and got a lot of water quality time series data. We needed a way to find variation and distribution of water quality from these data, due to the characteristics of time series data itself such as the high dimensional, complex, dynamic, high noise, and easy to achieve large-scale.Based on the theory of the Time Series Data Mining (TSDM) and supported by project of the ChongQing Science&Technology Committee: "Research on Water Environment Security Early Warning Platform and Key Technology of Science Decision in Three Gorge Region "(CSTC2006AA7024), this research, based on the time series data in water quality monitoring of environmental and focusing on the method and theory of the TSDM, was carried out from the following four perspectives: the time series representation, similarity measures of Multivariate Time Series (MTS), time series forecast and application integration.â‘ This paper analyzed the representation of time series and focus on piecewise representation. The author present a piecewise polynomial continuous representation, through analysing the basic theory of piecewise linear and piecewise polynomial, overall the advantages of global continuity of the piecewise linear and local shape retain of piecewise polynomial. As the simulation results indicated that the continuous piecewise polynomial representation of time series retained the advantages of piecewise polynomials in the fitting, owned the advantages of continuity with piecewise linear, and was compatible with the piecewise linear, retained most representative of the time series characteristics. Form maintaining of time series in this algorithm enables it to be used to the trends extract and noise filter for the time series and applied to pre-process time series data in the field of water safety.â‘¡The author introduced the similarity of space path into MTS similarity measure based on the analyzing of the similarity measure between the distance of STS and the DTW distance, and propose a clustering method for the MTS based on the path similarity through measure the similarity of the space path to determine the similarity of MTS. The author compared the clustering result based on the path similarity of DTW with the clustering result based on the similarity of Euclidean distance and MTS clustering which is STS similar. The result of comparing shows that the clustering method based on DWT similarity can cluster data in the multidimensional time series completely correct, and the method based on the similarity of path Euclidean distance exist error just in the MTS which their difference is smaller, the two methods of clustering are all better than method based on theSTS. The better effect of practical applications was got when the method of clustering based on the DTW similar path or multivariate time series used in the river clustering of water environment.â‘¢In view of the forecast problem in neural networks for time series, the author research the basic principles of RBF and neural network integration, combine PCA technique and sample cluster center, proposed a RBF neural network integration method. The idea of the RBF neural network integration is using the dimension of the sample which is split from time series with the technology of PCA as the input dimension of Individual RBF network, orderly select the cluster center and radius of the sample as the individual RBF parameters, and introduce the priori knowledge into the average ensemble. Through the experiment, the result shows that the forecast accuracy of the RBF neural network integration method is higher than any individual in the forecast accuracy of RBF and the method get better application effect when using the method in the water quality forecast of water environment.â‘£The author design an application structure based on the service request mechanisms and an open TSDM calculation server architecture through combining the service request mechanism and TSDM method, and define the service request protocols for the client and the compute service, the protocols contain module querying, module contents, compute request and compute result, which are all based on the XML.The water quality time series data mining researched in this paper can extract effectively trends and filter noise, classify the rivers and forecast water quality. The open computing servers open integration structures, water quality model not only can support the expansion of services, while can also meet other system or platform, the demand for these models, and verified through the practical application of the method is feasible.