RESUMEN

The accurate prediction of chlorophyll-a (chl-a) concentration in coastal waters is essential to coastal economies and ecosystems as it serves as the key indicator of harmful algal blooms. Although powerful machine learning methods have made strides in forecasting chl-a concentrations, there remains a gap in effectively modeling the dynamic temporal patterns and dealing with data noise and unreliability. To wiggle out of quagmires, we introduce an innovative deep learning prediction model (termed ChloroFormer) by integrating Transformer networks with Fourier analysis within a decomposition architecture, utilizing coastal in-situ data from two distinct study areas. Our proposed model exhibits superior capabilities in capturing both short-term and middle-term dependency patterns in chl-a concentrations, surpassing the performance of six other deep learning models in multistep-ahead predictive accuracy. Particularly in scenarios involving extreme and frequent blooms, our proposed model shows exceptional predictive performance, especially in accurately forecasting peak chl-a concentrations. Further validation through Kolmogorov-Smirnov tests attests that our model not only replicates the actual dynamics of chl-a concentrations but also preserves the distribution of observation data, showcasing its robustness and reliability. The presented deep learning model addresses the critical need for accurate prediction on chl-a concentrations, facilitating the exploration of marine observations with complex dynamic temporal patterns, thereby supporting marine conservation and policy-making in coastal areas.

Asunto(s)

Clorofila A , Monitoreo del Ambiente , Análisis de Fourier , Monitoreo del Ambiente/métodos , Clorofila/análisis , Agua de Mar/química , Predicción , Aprendizaje Profundo

RESUMEN

Geopolymer recycled aggregate concrete (GPRAC) is a new type of green material with broad application prospects by replacing ordinary Portland cement with geopolymer and natural aggregates with recycled aggregates. This paper summarizes the research about the mechanical properties, durability, and microscopic aspects of GPRAC. The reviewed contents include compressive strength, elastic modulus, flexural strength, splitting tensile strength, freeze-thaw resistance, abrasion resistance, sulfate corrosion resistance, and chloride penetration resistance. It is found that GPRAC can be made to work better by changing the curing temperature, using different precursor materials, adding fibers and nanoparticles, and setting optimal mix ratios. Among them, using multiple precursor materials in synergy tended to show better performance compared to a single precursor material. In addition, using modified recycled aggregates, the porosity and water absorption decreased by 18.97% and 25.33%, respectively, and the apparent density was similar to that of natural aggregates. The current results show that the performance of GPRAC can meet engineering requirements. In addition, compared with traditional concrete, the use of GPRAC can effectively reduce carbon emissions, energy loss, and environmental pollution, which is in line with the concept of green and low-carbon development in modern society. In general, GPRAC has good prospects and development space. This paper reviews the effects of factors such as recycled aggregate admixture and curing temperature on the performance of GPRAC, which helps to optimize the ratio design and curing conditions, as well as provide guidance for the application of recycled aggregate in geopolymer concrete, and also supply theoretical support for the subsequent application of GPRAC in practical engineering.