No abstract

University classroom is the main place for classroom teaching, group discussion, students’ self-study and other activities. With the development of science and technology and the optimization of educational resources, traditional fixed desks and chairs cannot meet the needs of students’ discussion, so more and more colleges and universities adopt the design of movable desks and chairs. To date, most research has focused on schemes that design moving desks and chairs directly according to classroom capacity, but little consideration has been given to adjusting the classroom as long as the schedule remains the same. Combined with the classroom schedule of the first Teaching Building of Huazhong Agricultural University and the maximum capacity of the movable desks and chairs in each classroom, Tableau software is used for visualization analysis, and two ideal schemes are finally designed.

Flood disasters are the major natural disaster that affects the growth of agriculture and forestry crops. Due to rapid growth and strong waterlogging resistance characteristics, many studies have explained the waterlogging resistance mechanism of poplar from different perspectives. However, there is no accurate method to define the evaluation index of waterlogging resistance. In addition, there is also a lack of research on predicting the waterlogging resistance of poplars. Based on the changes of poplar biomass and seedling height, the evaluation index of poplar resistance to waterlogging was well determined, and the characteristics of photosynthesis were used to predict the waterlogging resistance of poplars. First, four methods of hierarchical clustering, lasso, stepwise regression and all-subsets regression were used to extract the photosynthesis characteristics. After that, the support vector regression model of poplar resistance to waterlogging was established by using the characteristic parameters of photosynthesis. Finally, the results show that the SVR model based on Stepwise regression and Lasso method has high precision. On the test set, the coefficient of determination (R2) was 0.8581 and 0.8492, the mean square error (MSE) was 0.0104 and 0.0341, and the mean relative error (MRE) was 9.78% and 9.85%, respectively. Therefore, using the characteristic parameters of photosynthesis to predict the waterlogging resistance of poplars is feasible.

Floods, as one of the most common disasters in the natural environment, have caused huge losses to human life and property. Predicting the flood resistance of poplar can effectively help researchers select seedlings scientifically and resist floods precisely. Using machine learning algorithms, models of poplar’s waterlogging tolerance were established and evaluated. First of all, the evaluation indexes of poplar’s waterlogging tolerance were analyzed and determined. Then, significance testing, correlation analysis, and three feature selection algorithms (Hierarchical clustering, Lasso, and Stepwise regression) were used to screen photosynthesis, chlorophyll fluorescence, and environmental parameters. Based on this, four machine learning methods, BP neural network regression (BPR), extreme learning machine regression (ELMR), support vector regression (SVR), and random forest regression (RFR) were used to predict the flood resistance of poplar. The results show that random forest regression (RFR) and support vector regression (SVR) have high precision. On the test set, the coefficient of determination (R2) is 0.8351 and 0.6864, the root mean square error (RMSE) is 0.2016 and 0.2780, and the mean absolute error (MAE) is 0.1782 and 0.2031, respectively. Therefore, random forest regression (RFR) and support vector regression (SVR) can be given priority to predict poplar flood resistance.

As common surrogate models, Kriging and RBF models have been widely used in various fields. Although the Kriging model and the RBF model have their own advantages, when the problems are complex and diverse, a single Kriging or RBF model usually cannot meet the requirements of global approximation. Luckily, the Kriging and the RBF model have good complementarity in performance. In view of this, an adaptive hybrid modeling method (AHM-CVH) based on cross-validation hypercube of Kriging and RBF is proposed in this paper. The CVH adaptive sampling strategy first generates a hypercube centered on the sample point with the largest cross-validation error, then candidate points are randomly sampled in the hypercube, and finally get a new sample point which is farthest from the center and surrounding samples. Eight benchmark functions ranging from 2 to 6 dimensions and an engineering example are validated, and the results show that the AHM-CVH method is superior to the single Kriging or RBF models in performance, and has the characteristics of high accuracy and strong stability.