| Eutrophication occurred in the drinking water source areas (surface water) often results in the water quality deterioration. Recent studies showed that algal blooms are the process of algae physical migration by floating and aggregating to the surface of water body, which are not caused by the short-time outbreak of algae biomass. The algal concentration has far exceeded the limits of water quality standards even before the occurrence of algal bloom. It shows more practical for drinking water security to make the short-scale continuous prediction of algal concentration, rather than to predict such’rare events’.In the thesis, a method of short-term prediction was proposed to make continuous prediction of chlorophyll a concentrations in river. The prediction theory is based on the consistency between the similarity of the algal short-term growth and the assumptions of the method of Case-based Reasoning (CBR). According to’four-REs’in the method of CBR, the similar conditions including similar factors, similarity coefficient, similarity criteria, similarity error, and multi-criterion synthetic similitude index, were established for the element of fluid meshed as the unit of algal growth according to Lagrangian method. The historical period in which the algal growth is most similar to the current algal growth, was firstly determined by those similar conditions, then the values of chlorophyll a in the backward extension of the historical period were selected as the predictive values. Additionally, a growth model based on the recent historical data was built to predict chlorophyll a concentration on12o’clock next day according to Lagrangian method,. The optimal combination of calibrated parameters and the optimal period of calibration data were both determined by the L-M method. The results from the model with the optimal calibration parameters input in the forecast period were uses as the predicted values. Finally, the chaos of the time series of hourly chlorophyll a observations was analized, the main chaotic charateristeics, which included embedding dimension, time delay, correlation dimension, and the largest Lyapunov exponent, were estimated. The classical methods estimating such characteristics included the C-C method for embedding dimension and time delay, the G-P algorithm for correlation dimension, and the Rosenstein method for the largest Lyapunov exponent. The maximum prediction time was finally estimated by the largest Lyapunov exponent.The series of the chlorophyll a concentrations, which were observed between May1st and August9th on2000and2001in Elbe River, were made the next24-72hours prediction according to the similar prediction principle. After the prediction accuracy was improved by data preprocessing and weight setting, hourly variation of trophic level for the next three days were predicted by multi-threshold sectional evaluations of chlorophyll a concentration. The forecasting accuracy was up to85%. Only the prediction of daily mean values of chlorophyll a concentrations according to a single threshold (often called’algae bloom’prediction) were focused in recent studies, which were able to give a precision of83%and2.5-3days of predictable time. The results indicated that the predict model proposed in the thesis have advantage in the prediction accuracy of trophic level and the real-time performance of prediction.The mechanic model of algae growth based on the variable parameters was verified by predicting the noontime data for the next day observed between May and August in2000in Elbe River, which gave more accurate description of algal growth heterogeneity in space and time than only piror paprameters used. The results obtained by selecting the reasonable parameters combinations (five parameters) and the period of calibration data(seven days), showed much better than those obtained on the condition of the prior parameters, even had more days with the high prediction precision (the relative error less than±10%) compared to the results by CBR at the same predictive time. The method provides a new solution for shore-term algal prediction in river when using mechanic model.Highly nonlinear growth of algae and sprase sampling of algae data in make the real-time preiditon of alage more difficult. The estimation of predictablity has few reports at present. The theory of chaos was used to analyze the observed sequence of chlorophyll a from March to September between1997and2001in Elbe River. The results showed that the time series of chlorophyll a had low-dimensional chaos with low correlation dimension (D=2.75-4.02). It also confirmed that, the runoff sequence of Elbe River in the same period was chaotic judged by the largest Lyapunov exponent larger than zero (λ1=0.0125). The results were similar to those from domestic research about the chaos of river runoff. However, there are no relevant reports about chaotic characteristics of chlorophyll a sequence in river currently. In the thesis, the maximum predictive times for the sequence of hourly chlorophyll a concentration in each year were estimated respectively, which changed from8.01to18.94days. The average value was13.98days (about two weeks) just close to the current biggest day-to-day weather forecast time. A much larger value for the runoff series in the same period was estimated to be80days. The results indicates that, compared with the weather factor greatly influencing the chaotic characteristics of chlorophyll a, the runoff factor is clearly weaker. The results of analysis are expected to provide a new thought for the researches of predictablity of algae in river.In conclusion, the achievements of the thesis are expected to help broaden the research method of prediction theory, improve the prediction accuracy of algae, and reveal the real growth rhythm of algae. The jobs in the thesis show the important practical significance for improving the level of water quality management of portable water source district. |
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