Analysis of hyperspectral imagery (HSI) is a critical aspect of remote sensing in precision agriculture, for which effective dimensionality reduction (DR) strategies for the inherent complexity and uncertainty of the data are highly necessary. The fusion of fuzzy logic with DR techniques offers potential promises to refine enough feature information from the classification system, which may potentially compromise critical information. However, graph-based deep learning, especially the use of graph attention networks (GATs), has advanced the field by ensuring enhanced spatial and spatial-type feature relationships, which alleviates classification errors. Although graph convolution networks and their predecessors have greatly advanced the use of spatial relationship features, deep learning algorithms, such as three-dimensional convolutional neural networks (3D-CNNs), have resorted to reducing the need for and reliance on high samples through spatial feature learning and consideration. Therefore, to advance the field, a novel approach, Enhanced Transformation enabled Fuzzy Graph network (ETFG), was designed, which is a combination of deep fuzzy-based DR, enhanced through the use of 3D-CNN and GAT, with the application of principal component analysis (PCA) for optimized DR. The ETFG model entailed two major processing stages, where the initial stage involved classifying the raw data cube using the 3D-CNN, and then the results processed an algorithm enriched by lightweight GAT-based modules. The ETFG model combines fuzzy C-means clustering and optimized DR using PCA, contributing the best of PCA and GAT capabilities for optimized classification. At high-performance optimal DR, the ETFG model offered optimal multispectral imaging and the analysis and classification of hyperspectral data, which is promising enough to advance the field's needs.

The long-term development goals of most countries face significant challenges in reducing emissions, improving environmental sustainability, and mitigating the negative effects of climate change. This study looks at how the ecological sustainability of BRICS countries is affected by economic growth, financial development, new technologies, and renewable energy consumption with the mediating effect of trade openness. The study covers the years 2004–2023, and it was based on fixed-effect models that use static panel data. Data were collected from the World Development Indicators website. The countries and time frame for this study were selected on the basis of data availability. These findings show that the use of renewable energy sources, technological innovation, and financial development all have a significant and positive impact on environmental sustainability. Nevertheless, environmental sustainability is significantly and negatively impacted by economic growth. Furthermore, trade openness functions as a significant mediator between them. Based on empirical evidence, the paper suggests that the BRICS nations seek sustainable economic development. Moreover, government agencies need to accurately evaluate the connection between financial development and emission reduction when formulating programs to cut emissions.

Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R² value of more than 72% average in each year and decreased MAE value, which was better than other studies’ deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM_2.5 by 14%, PM₁₀ by 18%, NO₂ by 14%, and SO₂ by 16% during the COVID-B period. This study found an increase in O₃ by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM₁₀ by 42%, PM_2.5 by 97%, NO₂ by 89%, SO₂ by 36%, CO by 58%, O₃ by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.

Ozone (O3) pollution has surfaced as a significant threat to urban air quality in contemporary years. The precise and efficient forecast of ozone levels is fundamental in the mitigation and management of ozone pollution. Even though the air quality monitoring network offers useful multi-source pollutant concentration data for predicting ozone levels, existing models still grapple with issues arising from outlier and redundant sites influencing prediction precision, and cross-contamination between different pollutants. Also, the non-linear and volatile nature of monthly runoff makes accurate prediction more complex, provide a more granular and timely view of atmospheric flow variations. In this research, we introduce a hybrid model that unites Variational Modal Decomposition (VMD), particularly useful for separating mixed signals or extracting meaningful patterns from noisy or complex data, Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) is designed for processing sequences of data with grid-like structures, such as images or video frames. CNN-LSTMs use convolutional operations to capture spatial patterns and LSTM units to model temporal dependencies, making them effective for tasks like video analysis, image sequence prediction, and spatiotemporal data processing, and VMD-CNN-LSTM to counter these issues. We commence by deconstructing the historical data series from the Nanjing air quality monitoring stations using VMD. Then, the Ensemble Empirical Mode Decomposition (EEMD) algorithm is applied to the VMD residual to acquire characteristic components or Intrinsic Mode Functions (IMFs). Each IMF is independently trained via LSTM to produce predictions for each component. Ultimately, we secure the final prediction by linearly superimposing the predictions from all components. The LSTM’s adaptive learning ability and memory function make it ideal for managing long-term data, leading to more precise predictions. To evaluate the prediction performance on the test set, our VMD-CNN-LSTM model is compared with other models such as EMD-LSTM, EMD-CNN-LSTM, and VMD-LSTM using root mean square error (RMSE), mean absolute error (MAE), and Nash coefficient (NSE). Our findings reveal that the VMD-CNN-LSTM model surpasses the other models, displaying higher prediction precision and lower errors. Importantly, the model shows enhanced fitting of peak and valley values, thus providing a promising strategy for monthly runoff prediction. In this research, we’ve put forth a unique hybrid model, VMD-CNN-LSTM, for monthly ozone prediction. By amalgamating VMD, CNN, and LSTM, our model effectively tackles challenges associated with outlier and redundant sites, cross-pollution between pollutants, and nonlinearity makes it hard to model the intricate runoff relationships accurately, while instability results in unpredictable fluctuations, both of which impact the accuracy and reliability of monthly runoff predictions and make it more impactful in Environmental Management, Energy Optimization, Agriculture, Urban Planning, Climate Resilience

The vital ecosystem services of coastal areas support biodiversity while storing carbon, protecting coasts, and conserving habitat for coastal species. Accurate mapping and monitoring of coastal ecosystems are essential for conservation and sustainable management, as these ecosystems face growing threats from human activities, sea-level rise, and climate change. A supervised Swin Transformer-based deep learning method using different hyperspectral datasets serves as the proposed algorithm for coastal cover mapping. The data requires preprocessing procedures that combine feature learning with normalization and dimensionality reduction to improve both spectral and spatial feature extraction. The Swin Transformer model extracts hierarchical features through its shifted window attention mechanisms, which combine local and global information. Through spectral-spatial fusion, the model utilizes the specific characteristics of each data source to enhance feature representation, enabling better discrimination of coastal area, ship detection, and large-scale coastal mapping. The integration of high-resolution spatial data with broader spectral information through multisource data methods supports robust classification and object detection. The algorithm achieves 92.4% overall classification accuracy through cross-validation and hyperparameter optimization while minimizing overfitting. It specifically enhances coastal area identification (>91%) and ship object detection (>90%). The analysis demonstrates that combining deep learning methods with diverse remote sensing data sources enables effective and precise mapping of coastal ecosystems.