AIMC Topic: Environmental Monitoring

Clear Filters Showing 51 to 60 of 1335 articles

Evaluating machine learning models and imputation strategies for Air Quality Index forecasting in urban India.

Environmental monitoring and assessment
Accurate Air Quality Index (AQI) prediction is essential for timely health risk management in urban environments, yet challenges such as missing data and complex pollutant interactions limit the performance of traditional approaches. This study inves...

Monitoring of granite quarries using deep learning and UAV photogrammetry in Bengaluru, India.

PloS one
Granite quarrying, a cornerstone of the construction industry in South India, yields significant economic benefits but poses substantial environmental and social challenges, including land degradation, dust pollution, alternation of the water regime,...

A novel integrated framework for long-term assessment of ecosystem service degradation and restoration prioritization in a semi-arid rift valley landscape.

Environmental monitoring and assessment
Wetland ecosystems in Africa's semi-arid rift valleys are crucial for supporting biodiversity, regulating water systems, and sustaining livelihoods; however, they are rapidly deteriorating due to agricultural expansion and urbanization. Previous asse...

Decoding climate-induced phytoplankton dynamics in a tropical macrotidal estuary using explainable machine learning.

The Science of the total environment
Phytoplankton communities in tropical macrotidal estuaries are highly sensitive to hydroclimatic variability, particularly during extreme El Niño-Southern Oscillation (ENSO) events. This study assessed ENSO effects on phytoplankton dynamics in the Sã...

Prediction of water quality in the middle area of Yangtze River using efficient machine learning model.

Environmental geochemistry and health
The Yangtze River, as the longest river in China and the third-longest in the world, holds immense significance for the country's ecological security and sustainable development. The water quality in its middle reaches directly impacts millions of pe...

DF-OSELM: a dynamic feedback feature learning model for air quality online prediction.

Environmental monitoring and assessment
Accurate and timely air quality forecasting is crucial for mitigating pollution risks and protecting public health. However, existing offline and online models face limitations in adaptability, computational efficiency, and interpretability. To addre...

Machine learning-based prediction of deep soil metal(loid) contamination in industrial areas: Role of surface environmental factors.

Environmental pollution (Barking, Essex : 1987)
Predicting the distribution of soil contamination is crucial for targeted remediation efforts and risk prevention, especially considering the high costs associated with in-situ contamination surveys. This study proposes a random forest (RF)-based app...

Using XGBoost and memetic programming to identify hotspots of sediment plastic pollution.

Environmental pollution (Barking, Essex : 1987)
Despite growing global initiatives on sustainable plastic management, less than 10 % of plastic waste is effectively recycled, resulting in widespread environmental dispersion and pollution. This study examines the relative influence of topographic, ...

Adversarial susceptibility analysis for water quality prediction models.

Scientific reports
Water quality is a critical factor for human health and environmental sustainability. Rapid urbanization and industrialization have led to significant water contamination, increasing the prevalence of waterborne diseases. This study investigates the ...

Integrating machine learning and geospatial approaches for multi-hazard vulnerability mapping: implications for environmental health and contaminant risk in fragile ecosystems.

Environmental geochemistry and health
High-altitude ecosystems face growing threats from natural hazards and human activities, intensifying socio-economic and environmental risks. The Nilgiris District, Tamil Nadu, is a hotspot where steep terrain, fragile ecosystems, climate variability...