Research On Processing Of Multiresouce Mornitoring Data And Synthetic Forecasting Method For Red Tide

Red Tide is one of major ecological disasters of china coast. The monitoring and prediction of red tide is an open issue which has attract high attention. The related mechanism of red tide dynamic process is unclear for the complexity of red tide process, so machine learning and fuzzy sets theory is widely used in the current red tide monitoring systems. On other hand, in order to monitor red tide process more comprehensively, multi-method and stereoscopic monitoring system should be constructed, therefore handling and fusion multi-source monitoring information should be considered.The research work in this thesis is supported by"863"of high technology research and development program"monitoring and early-warning system for red tide key monitoring sea area"important specific subject. A great deal of data is obtained from the monitoring system, so combined with the theory of machine learning, fuzzy mathematics and data fusion, our research is emphasis on the handling and analysis the monitoring data. The major research work and contribution are as follows:First of all, the implementation situation of the subject and its five subsystems on which our research work based is introduced and the means to acquire and transmission of the data are described. The content for preprocessing of data comprise from two parts. In first part the buoy sensors are online accessed in which available related standards are comprehensive utilized, and the uncertainty evaluation is carried out for the monitoring parameters accordance with the linear analysis standard. In second part, in order to impute the missing data in monitoring time series, a new imputation method is proposed that can achieve better imputation result than common method.Being accessed to the buoy sensors, it is found that some on-line monitoring parameters accordance with the linear analysis standard, while some ones not. For the first kind, accurate value can be used to represent the data in temperature and salinity time series. A new time series multi-step prediction method combining singular spectrum analysis and radial basis function neural networks is proposed, and an exact result is achieved. For the second kind, considering the uncertainty of the data, fuzzy value can be used to represent the data in chlorophyll time series. Taking advantage of the superiority of two-factor time-variant fuzzy relation method and two-factor high-order fuzzy inference method, a better result is achieved comparing with other method.For the purpose of the early-warning of red tide, utilizing the multi-source monitoring chlorophyll information from this monitoring system, combined with the data fusion theory, a data fusion model which applied to the early-warning of red tide is tried. The major content of evidence theory is introduced, and the main problems and the existing solutions met in evidence theory application is discussed. According to the specific requirements, a new practical model for BBA generation and conflicts handling is constructed, which could realize the real-time judgment of red tide occurrence.The biological density and species distribution of the red tide are the important data derived from the red tide monitoring process. The SOM neural network is used to cluster the red tide population information and visualize the results which could facilitate the expert to analysis. The structure of fuzzy-ARTMAP neural network is talked about. The hybrid model of the fuzzy-ARTMAP neural network and SOM neural network is applied to aided prediction analysis the algae population fluctuation that have obtained good results.For the case that red tide experts are distributed in different cities with deferent opinion, extended fuzzy number is implied to express the linguistic information of experts, and multi-expert information fusion model is given. According to the defects of several existing fuzzy number similarity measurement, a new similarity measurement is put forward which has been applied in the multi-experts information fusion model, a numerical example is used to illustrate the efficiency of the proposed method.