Promotion of residential water conservation measures in South Africa: the role of water-saving equipment

Environmental Economics and Policy Studies - In many urban settings around the world the severity of water scarcity has induced changes in household behavior, leading to reduction in the volume of...     

Elimination of pharmaceutical pollutants by solar photoelectro-Fenton process in a pilot plant

Environmental Science and Pollution Research - In this study, the simultaneous degradation of antibiotics (ampicillin, sulfamethazine, and tetracycline; and non-steroidal anti-inflammatories...     

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.