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The Yellow River Delta wetlands in China belong to the coastal wetland ecosystem, which is one of the youngest and most characteristic wetlands in the world. The Yellow River Delta wetlands are constantly changed by inland sediment and the influence of waves and storm surges, so the accurate classification of the coastal wetlands in the Yellow River Delta is of great significance for the rational utilization, development and protection of wetland resources. In this study, the Yellow River Delta sentinel-2 multispectral data were processed by super-resolution synthesis, and the feature bands were optimized. The optimal feature-band combination scheme was screened using the OIF algorithm. A deep learning model attention mechanism ResNet based on feature optimization with attention mechanism integration into the ResNet network is proposed. Compared with the classical machine learning model, the AM_ResNet model can effectively improve the classification accuracy of the wetlands in the Yellow River Delta. The overall accuracy was 94.61% with a Kappa of 0.93, and they were improved by about 6.99% and 0.1, respectively, compared with the best-performing Random Forest Classification in machine learning. The results show that the method can effectively improve the classification accuracy of the wetlands in the Yellow River Delta. [ABSTRACT FROM AUTHOR]

The occurrence of rockburst can cause significant disasters in underground rock engineering. It is crucial to predict and prevent rockburst in deep tunnels and mines. In this paper, the deficiencies of ensemble learning algorithms in rockburst prediction were investigated. Aiming at these shortages, a novel machine learning model, deep forest, was proposed to predict rockburst risk. The deep forest combines the characteristics of deep learning and ensemble models, which can solve complex problems. To develop the deep forest model for rockburst prediction, 329 real rockburst cases were collected to build a comprehensive database for intelligent analysis. Bayesian optimization was proposed to tune the hyperparameters of the deep forest. As a result, the deep forest model achieved 100% training accuracy and 92.4% testing accuracy, and it has more outstanding capability to forecast rockburst disasters compared to other widely used models (i.e., random forest, boosting tree models, neural network, support vector machine, etc.). The results of sensitivity analysis revealed the impact of variables on rockburst levels and the applicability of deep forest with a few input parameters. Eventually, real cases of rockburst in two gold mines, China, were used for validation purposes while the needed data sets were prepared by field observations and laboratory tests. The promoting results of the developed model during the validation phase confirm that it can be used with a high level of accuracy by practicing engineers for predicting rockburst occurrences. [ABSTRACT FROM AUTHOR]

Fruit maturity is a crucial index for determining the optimal harvesting period of open-field loofah. Given the plant's continuous flowering and fruiting patterns, fruits often reach maturity at different times, making precise maturity detection essential for high-quality and high-yield loofah production. Despite its importance, little research has been conducted in China on open-field young fruits and vegetables and a dearth of standards and techniques for accurate and non-destructive monitoring of loofah fruit maturity exists. This study introduces a real-time detection and maturity classification method for loofah, comprising two components: LuffaInst, a one-stage instance segmentation model, and a machine learning-based maturity classification model. LuffaInst employs a lightweight EdgeNeXt as the backbone and an enhanced pyramid attention-based feature pyramid network (PAFPN). To cater to the unique characteristics of elongated loofah fruits and the challenge of small target detection, we incorporated a novel attention module, the efficient strip attention module (ESA), which utilizes long and narrow convolutional kernels for strip pooling, a strategy more suitable for loofah fruit detection than traditional spatial pooling. Experimental results on the loofah dataset reveal that these improvements equip our LuffaInst with lower parameter weights and higher accuracy than other prevalent instance segmentation models. The mean average precision (mAP) on the loofah image dataset improved by at least 3.2% and the FPS increased by at least 10.13 f/s compared with Mask R-CNN, Mask Scoring R-CNN, YOLACT++, and SOLOv2, thereby satisfying the real-time detection requirement. Additionally, a random forest model, relying on color and texture features, was developed for three maturity classifications of loofah fruit instances (M1: fruit setting stage, M2: fruit enlargement stage, M3: fruit maturation stage). The application of a pruning strategy helped attain the random forest model with the highest accuracy (91.47% for M1, 90.13% for M2, and 92.96% for M3), culminating in an overall accuracy of 91.12%. This study offers promising results for loofah fruit maturity detection, providing technical support for the automated intelligent harvesting of loofah. [ABSTRACT FROM AUTHOR]

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants. [ABSTRACT FROM AUTHOR]