RESUMEN
Environmental quality data sets are typically imbalanced, because environmental pollution events are rarely observed in daily life. Prediction of imbalanced data sets is a major challenge in machine learning. Our recent work has shown deep cascade forest (DCF), as a base learning model, is promising to be recommended for environmental quality prediction. Although some traditional models were improved by introducing the cost matrix, little is known about whether cost matrix could enhance the prediction performance of DCF. Additionally, feature extraction is also an important way to potentially improve the model's ability to predict the imbalanced data. Here, we developed two novelty learning models based on DCF: cost-sensitive DCF (CS-DCF) and DCF that combines unsupervised learning models and greedy methods (USM-DCF-G). Subsequently, CS-DCF and USM-DCF-G were successfully verified by an imbalanced drinking water quality data set. Our data presented both CS-DCF and USM-DCF-G show better prediction performance than that of DCF alone did. In particular, USM-DCF-G shows the best performance with the highest F1-score (95.12 ± 2.56%), after feature extraction and selection by using unsupervised learning models and greedy methods. Thus, the two learning models, especially USM-DCF-G, were promising learning models to address environmental imbalanced issues and accurately predict environmental quality.
Asunto(s)
Agua Potable , Bosques , Aprendizaje Automático , Calidad del AguaRESUMEN
Smart surfaces prepared using superhydrophobic coatings have been used to control the movement of tiny aqueous droplets for many years. However, the control of both aqueous droplets and oily droplets is still a challenge. Herein, a novel smart superamphiphobic composite film is developed with a superamphiphobic and magnetic surface as well as a soft elastic substrate for liquid droplets manipulation. The raspberry-like nanoparticles on the surface are synthesized by co-hydrolyzation of fluoroalkyl silane and tetraethoxysilane on iron oxide nanoparticles. The resulting composite nanoparticles (F/SiO2@Fe3O4 NPs) exhibit excellent superhydrophobicity (WCA of about 170°) and superoleophobicity (OCA of about 160°) as well as magnetism (saturated magnetization value of 12.0 emug-1). The morphologies of the F/SiO2@Fe3O4 NPs were investigated by transmission electron microscopy and scanning electron microscopy. Chemical composition and magnetization value of these magnetic nanoparticles as well as the magnetic field induced droplets manipulation behavior of the smart surface were also evaluated. The smart surface can realize the manipulation of both water droplets and oil droplets, which demonstrates potential applications in microfluidic technologies.