The Kriging surrogate model in complex simulation problems uses as few expensive objectives as possible to establish a global or local approximate interpolation. However, due to the inversion of the covariance correlation matrix and the solving of Kriging-related parameters, the Kriging approximation process for high-dimensional problems is time consuming and even impossible to construct. For this reason, a high-dimensional Kriging modeling method through principal component dimension reduction (HDKM-PCDR) is proposed by considering the correlation parameters and the design variables of a Kriging model. It uses PCDR to transform a high-dimensional correlation parameter vector in Kriging into low-dimensional one, which is used to reconstruct a new correlation function. In this way, time consumption of correlation parameter optimization and correlation function matrix construction in the Kriging modeling process is greatly reduced. Compared with the original Kriging method and the high-dimensional Kriging modeling method based on partial least squares, the proposed method can achieve faster modeling efficiency under the premise of meeting certain accuracy requirements.

Rice has long served as the staple food in Asia, and the cultivation of high-yield rice crops draws increasing attention from academic researchers. The prediction of rice growth condition by image features realizes nondestructive prediction and it has great implications for smart agriculture. We found a special image parameter called the fractal dimension that can improve the effect of the prediction model. As an important geometric feature, the fractal dimension could be calculated from the image, but it is rarely used in the field of rice growth prediction. In this paper, we attempt to combine the fractal dimension with traditional rice image features to improve the effect of the model. The thresholding method is used to transform the cropped rice image into binary image, and the box-counting method is used to calculate the fractal dimension of the image. The correlation coefficients are calculated to select the characteristics with a strong correlation with biomass. The prediction models of dry weight, fresh weight and plant height of rice are established by using random forest, support vector regression and linear regression. By evaluating the prediction effect of the model, it can be concluded that the fractal dimension can improve the prediction effect of the model. Among the models obtained by the three methods, the multiple linear regression model has the best comprehensive effect, with the dry weight prediction model $R^2$ reaching 0.8697, the fresh weight prediction model $R^2$ reaching 0.8631 and the plant height prediction model $R^2$ reaching 0.9196. The model established in this paper has a fine effect and has a certain guiding significance in rice research.

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

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.

Efficient treatment of aquaculture effluent is a crucial measure for ensuring the green and sustainable development of fisheries and alleviating pressure on aquatic ecosystems. However, traditional treatment technologies face bottlenecks of low efficiency and poor adaptability, making it difficult to meet the pollution control demands of large-scale aquaculture development. This is a systematic review focusing on artificial intelligence (AI) applications in aquaculture effluent treatment, aiming to clarify the technical framework, core application scenarios, industry trends, challenges, and future directions of AI-driven aquaculture effluent treatment. It first outlines core machine learning technologies, compares model adaptability, and analyzes AI synergies with IoT and digital twins. It then details AI implementation pathways across four core scenarios: precision feeding for pollution reduction, water quality monitoring and prediction, development of denitrifying and phosphorus-removing engineering bacteria, and system module control. Finally, it validates technical effectiveness through case studies, identifies industry trends toward integrated models and predictive monitoring, highlights existing challenges, such as data quality bottlenecks, system coupling complexity, and insufficient implementation economics, and proposes future research directions. This study provides theoretical foundations and practical references for the intelligent upgrading of aquaculture effluent treatment and the high-quality development of the fisheries industry.

Kriging-based modeling has been widely used in computationally intensive simulations. However, the Kriging modeling of high-dimensional problems not only takes more time, but also leads to the failure of model construction. To this end, a Kriging modeling method based on multidimensional scaling (KMDS) is presented to avoid the “dimensional disaster”. Under the condition of keeping the distance between the sample points before and after the dimensionality reduction unchanged, the KMDS method, which mainly calculates each element in the inner product matrix due to the mapping relationship between the distance matrix and the inner product matrix, completes the conversion of design data from high dimensional to low dimensional. For three benchmark functions with different dimensions and the aviation field problem of aircraft longitudinal flight control, the proposed method is compared with other dimensionality reduction methods. The KMDS method has better modeling efficiency while meeting certain accuracy requirements.