中水回用对社区景观水体叶绿素a变化的影响

由于中水的营养盐含量较高,以中水为补水水源的社区景观水体存在较大的水华暴发风险.基于中水回用于景观水体的换水控制实验,建立了中水景观水体的富营养化模型,模拟了连续换水时和终止换水后叶绿素a的变化过程,分析了中水水质和换水周期对水体藻类生长的影响.结果表明,提高中水水质可以降低水体的叶绿素a峰值;在连续换水期,随着换水周期的缩短, 水体的叶绿素a峰值降低,并且从开始换水至达到峰值的时间增长;在连续换水期后终止换水,叶绿素a会重新达到峰值,并且原来的换水周期越短,水体的叶绿素a峰值反而越高,达到峰值所需时间越长.模型应用可为中水回用于社区景观水体的水量水质设计与维护提供依据.     

GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING1

ABSTRACT: Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real‐time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall‐runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real‐time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly.     

GENETIC PROGRAMMING: A NEW PARADIGM IN RAINFALL RUNOFF MODELING1

ABSTRACT: Genetic Programming (GP) is a domain‐independent evolutionary programming technique that evolves computer programs to solve, or approximately solve, problems. To verify GP's capability, a simple example with known relation in the area of symbolic regression, is considered first. GP is then utilized as a flow forecasting tool. A catchment in Singapore with a drainage area of about 6 km² is considered in this study. Six storms of different intensities and durations are used to train GP and then verify the trained GP. Analysis of the GP induced rainfall and runoff relationship shows that the cause and effect relationship between rainfall and runoff is consistent with the hydrologic process. The result shows that the runoff prediction accuracy of symbolic regression based models, measured in terms of root mean square error and correlation coefficient, is reasonably high. Thus, GP induced rainfall runoff relationships can be a viable alternative to traditional rainfall runoff models.