Study And Application Of Digital Simulation On River Water Quality Management By Computational Intelligence

Improving river water quality in management aspect by digital simulation modelsnot only can save resources const, but also effectively achieve the task goal. Tosolve the main technical problems of river water quality management, models foridentifying pollution sources, forecasting water quality and allocating the reductionof outlet loads, were studied based on computational intelligence (CI) methods,through investigating the current research situation at home and abroad, with theSonghua River-Harbin section as the study area.The model for identification pollution sources to river water was establishedbased on expert system. Through the normal water quality monitoring matrix, aninterpreting mechanism was built by preliminary screening and multivariate datamining technique, by which major pollution factors and their loading water qualityindices could be obtained.Then a decision tree model was constructed based on the22classes of produced rules by experts. According to the53national industrialwater pollution discharge standards, a similarity matching model for identifying thepotential point sources of industrial pollution was established, which can calculatethe contributing rate of a certain industry on target area. The model was applied tothe Songhua River Harbin section and for outside city group, the main point sourcesordinally were medical and pharmacy、iron ores dressing. For high pollution group,the major five were pottery and porcelain production, chemical coking, traditionalChinese medicine pharmacy and pulp and paper industry depending on thecontributing rate. The validation results were reasonable. For mainstream groupafter Dadingzishan Dam Project (DDP), the main five were pharmacy, sewage plant,iron ores dressing and electroplating industries. Compared to that before DDP, thecontribution rates of point sources were decreased and the number of non-pointsources factors increases, suggesting the DDP strengthened the self cleaningcapacity of river water body and the environmental policy was also effective.For predicting monthly water quality series and assessing the total uncertainty,a Bootstrap wavelet neural network model (BWNN) was developed based on re-Bootstrap method. The Morlet wavelet basis function (WBF) was employed asnonlinear activation function and the time-lag orders were set as the input variables. Performances of BWNN models were satisfactory and were better than ARIMA andother ANN models for NH4+-N and DO in Zhushuntun station, with respect NSE0.9678and0.8556. The uncertainty from data noise was smaller than that frommodel structure for seasonal NH4+-N series; conversely, the uncertainty from datanoise was larger for unseasonal DO series. Besides, total uncertainties in the low-flow period were10-20%bigger than other flow periods.The optimal data missing-refilling scheme was studied for the above BWNN.Performances of BWNN were still satisfactory when the missing percentage was8.3%. Temporal method was satisfactory for filling seasonal series, whereas spatialimputation was fit for unseasonal series and the local average infilling could be aneffective alternative when the spatial data were unavailable. The case study ofDadingzishan station showed that the predictions of COD, NH4+-N and TP couldmeet the national water quality standard IV, but the upper band of predictionintervals violated the standard in some winter months.Loads reducing allocation model of regional outlet discharge was establishedbased on water environment capacity control and the CI method. A linkage modelwas developed by bootstrap and ANN, which only needed water quality monitoringdata and discharge loads data. However, obtained prediction intervals couldguarantee the margin of safety in environmental management. Considering thecharacters of big and small rivers, reduction allocation models for single waterfunctional area (RMS) and multi-functional area (RMM) were developed,respectively. Then two intelligent optimizing algorithms were created for solvingeach model. The RMS could inversely simulate and optimize the allocation ofreduction rate of discharge loads and it had good applicability in watershed scale;the RMM could generate many noninferior solutions that could meet the balancebetween water environment capacity and reduction cost, then some solutions couldbe chosen as feasible ones for the practice. The results of NH4+-N case studyshowed that the two models could provide effective scientific supports forwatershed water quality management and would strengthen its systematicness.