Research On Dynamic Water Quality Earlv-warning Correction Method For Sudden Pollution Accidents In River

With the rapid development of economy in China,pressure of environmental protection is larger than before.In addition,sudden river pollution accidents occur frequently,which often cause serious environmental,economic and social problems.Therefore accurately prediction of pollutant concentration at the early warning point is of great significance to effectively control and reduce the impact of sudden river pollution accidents.Based on this situation,this thesis focuses on the study of water quality early warning after sudden river pollution accidents upstream.The model parameters and the early warning results can be corrected dynamically by using the measured data.Then,experiments based on different facilities are conducted to simulate the dispersion of pollutants and to analyze algorithms.Finally,the results of corresponding researches are applied to the developed water quality early warning system.The main work and innovations of this thesis are as follows:(1)At present,the research on water quality early warning is mainly based on static model,which is hard to be corrected and optimized continuously by new measured data.In order to improve the accuracy of early warning model,this thesis proposes a methodology framework of dynamic correction for the water quality after sudden river pollution accidents according to the measured data in every river section.Based on one-dimensional water quality model,this thesis uses the measured data to correct the results dynamically and segmentally for early warning model in the process of prediction.Meanwhile,the 'simulated pollution source' is constructed by using the data from upstream section of the early warning points to correct and optimize the parameters in early warning model and the prediction results.At the same time,the tributary correction is carried out by considering the influence of tributaries on pollutant diffusion.(2)In order to improve the efficiency of correction algorithm and to avoid it getting into the local optimal solution,a new method basing on the improved genetic algorithm is proposed in this thesis.This algorithm is based on standard genetic algorithm,perfecting the way of encoding and production of initial population,improving the operators of crossover and mutation,and adding the immigration operator to prevent algorithm premature.(3)In order to reduce the influence of model structure error and measurement error and to improve the accuracy of early warning results,a dynamic pollutant concentration correction method based on Kalman filter is proposed.The state equation is established basing on water quality model,and the dynamic pollutant concentration correction is performed by estimating system noise covariance matrix Q and measurement noise covariance matrix R,thus the prediction accuracy is improved on the basis of parameter correction.On the basis of theoretical research,experiments in the wave flume and other experimental sites were conducted for simulating the dispersions of pollutants to verify the effectiveness of dynamic water quality early warning correction method.Further,the results had been used in a complete water quality early warning system for sudden river pollution accidents,which was compatible with the requests of monitoring and early warning in a real river.Moreover,the real system with the correction function of the model parameters and early warning results had been deployed and exhibited.