Search Results (2)

This study employs an improved fractional-order grey multivariable convolution model (FGMC(1,N,2r)) to predict abalone aquaculture output in Fujian, Shandong, and Guangdong. By integrating fractional-order accumulation (r₁, r₂) with a particle-swarm-optimization (PSO) algorithm, the model addresses limitations of handling multivariable interactions and sequence heterogeneity within small-sample regional datasets. Grey relational analysis (GRA) first identified key factors exhibiting the strongest associations with production: abalone production in Fujian and Shandong is predominantly influenced by funding for aquatic-technology extension (GRA degrees of 0.9156 and 0.8357, respectively), while in Guangdong, production was most strongly associated with import volume (GRA degree of 0.9312). Validation confirms that FGMC(1,N,2r) achieves superior predictive accuracy, with mean absolute percentage errors (MAPE) of 0.51% in Fujian, 3.51% in Shandong, and 2.12% in Guangdong, significantly outperforming benchmark models. Prediction of abalone production for 2024–2028 project sustained growth across Fujian, Shandong, and Guangdong. However, risks associated with typhoon disasters (X₆ and import dependency (X₅) require attention. The study demonstrates that the FGMC(1,N,2r) model achieves high predictive accuracy for regional aquaculture output. It identifies the primary drivers of abalone production: technology-extension funding in Fujian and Shandong, and import volume in Guangdong. These findings support the formulation of region-specific strategies, such as enhancing technological investment in Fujian and Shandong, and strengthening seed supply chains while reducing import dependency in Guangdong. Furthermore, by identifying vulnerabilities such as typhoon disasters and import reliance, the study underscores the need for resilient infrastructure and diversified seed sources, thereby providing a robust scientific basis for production optimization and policy guidance towards sustainable and environmentally sound aquaculture development. Full article

The scientific prediction of energy consumption plays an essential role in grasping trends in energy consumption and optimizing energy structures. Energy consumption will be affected by many factors. In this paper, in order to improve the accuracy of the prediction model, the grey correlation analysis method is used to analyze the relevant factors. First, the factor with the largest correlation degree is selected, and then a new grey multivariable convolution prediction model with dual orders is established. Different fractional orders are used to accumulate the target data sequence and the influencing-factor data sequence, and the model is optimized by particle swarm optimization algorithm. The model is used to fit and test the energy consumption of Shanghai, Guizhou and Shandong provinces in China from 2011 to 2020 compared with other multivariable grey prediction models. Experimental results with the MAPE and RMSPE measurements show that our improved model is reasonable and effective in energy consumption prediction. At the same time, the model is applied to forecast the energy consumption of the three regions from 2021 to 2025, providing reliable information for future energy distribution. Full article