Genetic Projection Pursuit Interpolation Model for Social Renewability Assessment of Water Resources

Abstract

In order to resolve the non-uniformity problem of evaluation results of social renewability of water resources in cities, and to raise the evaluation result precision, a genetic projection pursuit interpolation model (GPPIM) is founded for comprehensive assessment of social renewability of water resources, which is by using Gray-encoded accelerating genetic algorithm (GAGA). Taking the 15 cities in the Yellow River basin as examples, the social renewability of water resources is assessed with 11-index system by the GPPIM, the results show that the social renewability of water resources in Xining, Baoji, Xianyang and Luoyang are weaker grade, and it is medium grade in the other cities. The levels of them are very low. And it is hard task to improve their social renewability of water resources. In addition, the GPPIM for social renewability of water resources is effective and precise, which can be used for other cluster analyses of high-dimensional data sets. Then, Grayencoded accelerating genetic algorithm is applied to optimize projection direction of the projection pursuit interpolation model and the steps of the Gray-encoded accelerating genetic algorithm are introduced in detail.