Study Of Analysis And Forecast For Red Tide Based On Knowledge Discovery Techniques

It is well known that the 21^st century is called "marine century". Marine and high-tech-driven marine economy is increasingly becoming major field of international competition.Red tide has brought about many negative environmental and economic consequences around,along the coastal areas of China in recent years.The annual direct economic loss caused by red tide has amounted to much as 10 billion Yuan average. Therefore,research on key technology of analysis and forecast for red tide,and development of red tide early warning application systems has important practical and strategic significance on promoting sustainable development of marine economy in China.Red tide is an anomalous ecological phenomenon caused by various complex factors and is characterized by random,fuzziness,abruptness and nonlinearity,so it is difficult to build up red tide prediction model. All the features of red tide make the research on the red tide prediction one of the most challenging subjects in the frontier field of marine science and technology.To overcome the shortcomings of traditional ways,new theories and methods must be continuously introduced.At the same time,different methods must be integrated to research red tide systematically and comprehensively in a macro view.This thesis is written against the background of the project named Marine Environment Observing and Calamity Intelligent Early Warning System,which is supported by Science and Technology Development Plan (Key Project) of Shandong Province(Grant No.2004GG2205108).On the basis of previous achievements,by taking theories and methods of knowledge discovery as a framework,and cross-subject integrative research in analysis and prediction of red tide,this thesis applies knowledge discovery techniques(such as wavelet transform,principal component analysis,fuzzy clustering and neutral network) to data analysis and processing of red tide.At the same time,a red tide knowledge discovery platform is designed and implemented,thanks to numerous research achievements of our team.Hopefully,through construction of composition models based on the platform,researchers can fully and correctly understand the nature and internal mechanism of red tide from multi points of view,and then fruit certain achievements on the prediction of red tide.The thesis consists of the major novel technical contributions as follows:Firstly,grounded on the analysis of the traditional threshold methods,a de-noising method of wavelet exponential threshold is put forward,based on translation invariance.It proves that this method has effectively overcome the shortcomings of hard shrinkage and soft shrinkage function, and greatly improved the performance of de-noising.In response to the fact that the measurement data of red tide may be inevitably affected by noise and therein,the proposed method is applied to de-noise the data in order to achieve high-quality ones.Thus,the nature of red tide is more objectively and truly reflected through the de-noised measurement data, which provides a reliable and valid data source for the subsequent analysis and prediction of red tide.Secondly,this thesis studies fuzzy clustering theory and algorithms.In conventional FCM algorithm,every sample has the same influence on data set classification,but it is not often correct in practical classification process.This sometimes causes the fact that the ascertainment of cluster centers is strongly influenced by a few anomalous samples in data set.In order to solve the problem,a new fuzzy weighting clustering algorithm based on the fuzzy C-means algorithm and similar relation is proposed. When the fuzzy weighting coefficient is applied to the cluster centers and Euclidean distance,each sample has various influences on data set classification in FCM algorithm.The convenient prediction algorithm for red tide is not able to make reasonable distinction between the red tide evolutionary phases including its origin,occurrence,maintenance and dying.Therefore,the FWFCM algorithm is applied to find out the reliable division rules for different phases of evolutionary process of red tide. More accurate and authentic division is achieved,which reflects the fuzziness and transitivity of red tide intuitively and truly,and retains original information of the data sets.Thus,the proposed strategy provides a valid way to explore inherent reason of division and explain correlation degree between samples.Thirdly,a red tide combined prediction model is proposed,which is the integration of principle component analysis,fuzzy clustering algorithm, wavelet transform and neural network.The model reflects the inner mechanism of red tide to a certain extent,and explains the predictive result in a reasonable and clear manner.What's more,this combined model efficiency increases the model efficiency and predictive accuracy.Fourthly,the inner mechanism of knowledge discovery system and software architecture model is studied.To solve the common problems (such as simplified model,coupling of data and algorithm,lack of expandability and reusability,etc.) of traditional red tide prediction software,a solution of red tide knowledge discovery platform is put forward,based on component technology and service oriented architecture.Through the convenient environment provided by the proposed solution,users could build various combined models,test models,and execute in actual business.In short,the thesis takes theories and methods of knowledge discovery as a framework,and integrates cross-subject research in analysis and prediction of red tide.Lots of case studies show that the research has enriched and deeply developed the theory and method of the analysis and prediction of red tide with its practicability and effectiveness.It provides a new approach to the research on red tide prediction.