The Study On Low Flow Of Karstic Catchment Using ANN

Artificial Neural Networks (ANNS) is a kind of cross-science, which is developing veryrapidly now. It has been used in many fields such as machinery industry, electronic industry, power industry, hydraulic engineering, chemical industry, agriculture, environment, economy and so on.This paper focuses on application of ANNS in the study of the karst basin and its low flow.There are many factors that work on the low flow complexly in karst basins. The factors include antecedent precipitation of low flow period and status of karst basins (such as lithology, landform, drainage area, drainage density, length and ratio of demotion of main river and so on.).Based on the former studies, integrated the conventional statistic methods, the technique of ANNS is applied in this paper for researching the law of the low flow of karst basins in Guizhou altiplano.In view of the factors of inflection of the low flow in karst basins, sixteen factors about antcedent precipitation and characteristics of the chosen karst basins, are quantified. And the correlation of low flow and the factors is worked out. The statistics show that the correlation of antecedent precipitation, lithology, landform, drainage density, drainage area, length and ratio of demotion of main river and four characteristics of low flow is very complex. In view of this, to maintain the information of the original data, the PGA (Principal Component Anylysis) method is applied to preprocess the input mode, which improves the generaziation performance of BP (Back Propagation) network by turning multi-dimension variable into four-dimension variable, and four principal components are take out containing 85.345% of the information of the original data. Based on the four principal components, the 18 chosen karst basins are classified into three types by the technique of SOM (Self-Organizing Map).The laws of correlation of unclassified karst basins and classified karst basins are compared. As a result, the grades of the correlation between factors and low flow are higher than those of the formers. In view of this, the forecasting models of BP are based on the four principal components.In the course of studying of the forecasting models of BP, the characteristics of five models are analysized carefully by contrast. It shows that the BP models based on four main factors may improve the performance and the capability of the network's forecasting, meanwhile, it is very effective to set up forecasting model of BP based on four principal components of classified basins.