AIMC Topic: Environmental Monitoring

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HySwinFormer: A hybrid deep learning architecture for fine-grained classification of marine microalgae.

The Science of the total environment
Accurate identification of microalgae is vital for marine ecological monitoring, algal bloom early warning, and environmental management. However, existing methods often struggle with misclassification due to the morphological similarity and biologic...

The potential of decision tree application in threshold analysis of hazardous volatile organic compound release from biochar: Implications for environmental risk assessment.

The Science of the total environment
The release of hazardous volatile organic compounds (HVOCs) from biochar poses a potential threat to both human health and the environment. This study investigates how low pyrolysis temperature (HTT) and the chemical characteristics of lignocellulosi...

Exploring the influence of hydrological indicators on flow regimes through a data-driven modeling approach in the Huai river basin, China.

Environmental research
Understanding the impact of hydrological indicators on flow regimes is essential for sustainable water resource management. This study presents a data-driven framework integrating eXtreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations...

Machine learning-based ensemble of Global climate models and trend analysis for projecting extreme precipitation indices under future climate scenarios.

Environmental monitoring and assessment
Monitoring changes in climatic extremes is vital, as they influence current and future climate while significantly impacting ecosystems and society. This study examines trends in extreme precipitation indices over an Indian tropical river basin, anal...

Machine learning-assisted triple-emission Ln-MOFs sensor array for detection of multiple PFCs in aqueous environments.

Biosensors & bioelectronics
Perfluorinated compounds (PFCs) are persistent environmental pollutants with potential carcinogenicity, posing a major threat to ecosystems and human health. Rapid identification of PFCs in complex environmental matrices remains challenging due to th...

Wetland dynamics in the Indus River Delta: A Sentinel-2 and machine learning approach.

Journal of environmental management
Coastal wetlands of the Indus River Delta are vital ecological regions that have undergone significant transformations driven by anthropogenic activities and environmental stressors. This study assesses the dynamics of wetlands and reclamation in the...

Influence of sample size and machine learning algorithms on digital soil nutrient mapping accuracy.

Environmental monitoring and assessment
The objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, viz., multi-layer perceptron (MLP), random forest (RF), extra trees regressor (ETR), CatBoost, and gradient boost (GB), considering ...

Analysis of spatiotemporal variation characteristics of atmospheric quality in China's city clusters from 2015 to 2023 and their socio-economic driving forces.

Journal of environmental management
With the rapid economic development in China, air quality issues have emerged as major challenges to the country's sustainable development. This study utilizes ground monitoring data from 1248 monitoring Stations across China, constructs a kilometer ...

Machine learningdriven framework for realtime air quality assessment and predictive environmental health risk mapping.

Scientific reports
This research introduces a practical and innovative approach for real-time air quality assessment and health risk prediction, focusing on urban, industrial, suburban, rural, and traffic-heavy environments. The framework integrates data from multiple ...

Improved early-stage crop classification using a novel fusion-based machine learning approach with Sentinel-2A and Landsat 8-9 data.

Environmental monitoring and assessment
Crop classification during the early stages is challenging because of the striking similarity in spectral and texture features among various crops. To improve classification accuracy, this study proposes a novel fusion-based deep learning approach. T...