Multi-task learning (MTL) improves the performance achieved on each task by exploiting the relevant information between tasks. At present, most of the mainstream deep MTL models are based on hard parameter sharing mechanisms, which can reduce the risk of model overfitting. However, negative knowledge transfer may occur, which hinders the performance improvement achieved for each task. In this paper, for situations when multiple tasks are jointly trained, we propose the adaptive hard parameter sharing method. On the basis of the adaptive hard parameter sharing method, the number of nodes in the network is dynamically updated by setting a continuous gradient difference-based sign threshold and a warm-up training iteration threshold through the relationships between the parameters and the loss function. After each task fully utilizes the shared information, adaptive nodes are used to further optimize each task, reducing the impact of negative migration. By using simulation studies and instance analyses, we demonstrate theoretical proof that the performance of the proposed method is better than that of the competing method.

With the continuous expansion of the industrial production scale and the rapid promotion of urbanization, more and more serious air pollution threatens people’s lives and social development. To reduce the losses caused by polluted weather, it is popular to predict the concentration of pollutants timely and accurately, which is also a research hotspot and challenging issue in the field of systems engineering. However, most studies only pursue the improvement of prediction accuracy, ignoring the function of robustness. To make up for this defect, a novel air pollutant concentration prediction (APCP) system is proposed for environmental system management, which is constructed by four modules, including time series reconstruction, submodel simulation, weight search, and integration. It not only realizes the filtering and reconstruction of redundant series based on the decomposition-ensemble mode, but also the weight search mechanism is designed to trade off precision and stability. Taking the hourly concentration of PM_2.5 in Guangzhou, Shanghai, and Chengdu, China as an example, the simulation results show that the APCP system has perfect prediction capacity and superior stability performance, which can be used as an effective tool to guide early warning decision-making in the management of environmental engineering.