Research On The Key Techniques Of Dynamic Data Driven Early Warning System For Sudden River Pollution Accidents

Water pollution accidents usually take place unexpectedly and may cause severe variations of raw water quality. To deal with this situation, the early warning information of water security is urgently needed by the departments of water supply. Especially, sudden water pollution accidents often occur abruptly with many uncertain factors. Thus, quick and precise prediction of the water variation trend is always a difficult and important issue in the research field of water environment. With the support of the National Science and Technology Major Project and the NSFC, this thesis mainly focuses on the methods and the system implementation techniques of early warning for sudden river pollution accidents.The main work and achievement of this thesis are as follows:(1) On the basis of Dynamic Data Driven Application System(DDDAS), a new framework of the early warning system for sudden river pollution accidents is proposed. The working principle of the newly framework is analyzed, and the differences compared with traditional ones in the application process are concluded. There are some attractive features and advantages in the new framework based on the DDDAS, such as multi-model integration, symbiosis feedback, dynamic nature, self learning function, which can be helpful to the water quality prediction and the decision making.(2) In order to solve the problems in the dynamic adaptability and convenience of the water quality prediction models, the dynamic early warning method of sudden river pollution accidents is studied under the proposed framework based on DDDAS. With the historical and dynamic monitoring data, the initial parameters of the prediction model are firstly adjusted using Case Based Reasoning (CBR) method and the initial early warning is realized. Then, in the case of more accurate prediction with inadequate hydrological and boundary data, using the improved grid search optimization method, a technique for dynamic optimization of parameters in the prediction model is implemented with the observed water quality data. As for the structural errors of the prediction model caused by the generalization process and other factors, the time series algorithm is used to realize the on-line correction of results of the prediction model. With the actual river pollution accidents as the application cases, the dynamic early warning method is demonstrated and analyzed. The application results show that the presented method is able to effectively supply early warning function for sudden river pollution accidents. Due to the dynamic correction mechanism, the deviation between the predicted results and the measured results can be reduced.(3) The current layout of emergency monitoring stations is usually lack of theory guidance, and the emergency departments are difficult to determine the location of the emergency monitoring stations fast and legitimately. To solve this problem, under the framework proposed based on the DDDAS, the dynamic method concerning the selection, evaluation and optimization of the emergency monitoring stations is studied. Using the Fuzzy Analytical Hierarchy Process (FAHP) algorithm, the technical solutions of the layout of emergent monitoring stations are evaluated dynamically. This method can supply the technical support for quantitative evaluation of the sensitivity and importance of emergent monitoring stations in different stages of sudden river pollution accidents. Thus, the limited emergency monitoring resources can be allocated more reasonably and timely. This technique is freshly researched in domestic and overseas.Based on the above research, the technique platform of dynamic data driven early warning system for sudden river pollution accidents is developed, which has been integrated into the urban drinking water quality early warning system developed by the research group. The early warning for sudden river pollution accidents and the dynamic layout of emergency monitoring stations based on DDDAS have been achieved. The urban drinking water quality early warning system has been deployed in one province and three cities. The systems are running well and have achieved continuous operation and have been deemed as information and technical supports for urban drinking water quality security.