Open AccessModels of Hydraulic Factors Analysis Based on Genetic Programming for South-to-North Water Diversion Middle Route Project
Author(s) -
Xiaonan Chen,
Haitao Chen,
Yanguo Jin,
Feng Xiao-bo,
Yan Ma,
Fang Guo
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/304/2/022030
Subject(s) - water diversion , crossover , genetic algorithm , genetic programming , flexibility (engineering) , relation (database) , function (biology) , computer science , environmental science , mathematical optimization , statistics , mathematics , data mining , water resource management , artificial intelligence , evolutionary biology , biology
The South-to-North Water Diversion Middle Route Project has long water transmission line, complicated operation conditions for water conservancy dispatch, and high water diversion demands. It is important to calculate and analyse the correlation among hydraulic factors for water diversion. At present, the traditional hydraulic empirical formulas are used to evaluate relation of hydraulic elements in Middle Route Project, the parameters need to be manually corrected by measured data during operation, and the flexibility is poor. Model based on genetic programming is suggested for data mining of water conveyance dispatch. Correlativity function can be established automatically by genetic operations including selection, crossover and mutation. The model is applied into the discharge calculation and analysis of water surface curve for typical gate station and canal pool in Middle Route Project. In discharge calculation, the relation function between head difference, gate opening and flow coefficient can be found automatically with genetic programming. Similarly, the relation between downstream water depth, flow and upstream water depth can be achieved by the suggested model. It is shown that the proposed model based on genetic programing would be effective in nonlinear regression for hydraulic factors.