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The surrogate model is an effective way to connect the simulation and optimization models in groundwater flow numerical modeling; it could overcome the limitations of embedding and calling simulation models in the optimization model by conventional methods, which greatly reduces the computational load caused by directly calling the simulation model in the solving process of the optimization model. In this paper, the dual-response surface method and radial basis function artificial neural network method were applied to establish the surrogate model of groundwater flow numerical simulation in Jinquan Industrial Park, Inner Mongolia, China. The Latin hypercube sampling method was used to determine random pumping load of the five pumping wells, which were taken as the input data groundwater flow numerical simulation model for calculating 10 observation wells drawdown data sets (output data sets). Based on the input and output data sets, the dual-response surface method and radial basis function artificial neural network method were used to establish the surrogate model of groundwater simulation model, and the validity of surrogate models were comparatively tested. The results showed that both the results of two surrogate models fit well with the results of the simulation model, which indicates that two surrogate models were capable of approaching the groundwater flow numerical simulation model; compared with the dual response surface model, the RBF neural network model had more advantages in terms of sample size requirements, fitting the accuracy of simulation results.
Flower colour polymorphism is attributed to pollinator or non-pollinator mediated selection. Geranium nepalense has common white morph and very rare pink morph. We compared pollinator visitation frequencies, temperature and soil moisture between two morphs in the mixed morph population. We also compared morph ratio and reproductive success between white and pink flower individuals. Our results indicated that no visitor groups were different between two colour morphs. But visitor groups differed in visits between two years. Halictidae preferred pink morph in the year of 2012 but showed no discrimination in 2014, whereas Syrphidae preferred white morph in 2014 but no discrimination in 2012. Overall, pink morph produced more seeds than white morph, but exhibited variation between two years. However, visitor discrimination was not the main cause of the difference in female fitness. Soil moisture was not different between two colour morphs. Temperature of white morph was lower than pink in evening but not different in morning and noon. The results indicated that non-pollinator factors may exert the selective pressure to maintain the flower colour polymorphism in this species. Although pollinators did not exert selection on the flower colour polymorphism, we suggest that they provided potential pollination environment of fluctuating selection to drive flower colour evolution if visitors were limited.
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