AIMC Topic: Environmental Monitoring

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Mapping spatiotemporal distribution of forest carbon density in Xizang, China.

PloS one
Climate warming is a major global challenge, and forests, essential carbon sinks, are critical in mitigating its effects. Forest carbon density is a key parameter in assessing the carbon sinks. Traditional estimating methods of forest carbon density ...

A hybrid deep learning framework combining transformer and logistic regression models for automatic marine mucilage detection using sentinel-1 SAR data: A case study in Armutlu-Zeytinbağı, Marmara Sea.

PloS one
The identification of various objects and species found in nature is of great importance today. Active and passive imaging systems are in a beneficial position in this direction, both in terms of cost and convenience. Recently, mucilage events in our...

Machine learning comparison for biomarker level estimation in wastewater dynamics monitoring.

Scientific reports
Wastewater surveillance is an emerging strategy that enables monitoring of the presence and dynamic changes of targeted substances, facilitating improved allocation of preventive actions and public health interventions. This paper investigates the ap...

A Novel Framework for Airshed Delineation and PM Estimation across India Using Machine Learning and Spatial Clustering.

Environmental science & technology
Air pollution continues to pose a major challenge in India, with PM being a key contributor to serious health risks. Its spatial distribution is influenced by climatic, topographic, and anthropogenic factors, which are often poorly represented in ana...

Integrating pollution indices, spatial interpolation, and machine learning for soil contamination analysis along the Zarqa River, Jordan.

Environmental monitoring and assessment
This study assesses soil contamination along the Zarqa River (ZR) in Jordan by integrating pollution indices, geostatistical interpolation, and machine learning models. We collected 34 soil samples from agricultural lands within the study area. Sampl...

Identifying Key Taxa for Algal Blooms in a Large Aquatic Ecosystem through Machine Learning.

Environmental science & technology
Identifying key species responsible for excessive growth of algae communities, as reflected by the floating algae index (FAI), is crucial for developing targeted management strategies to control algal blooms (ABs). However, current approaches for alg...

Unveiling chemical space, scaffold diversity, critical structural features of pesticides: A comprehensive QSAR, qRASAR, machine learning studies to predict pesticides toxicity.

The Science of the total environment
The increasing use of pesticides in agriculture and urban areas has led to significant contamination of aquatic ecosystems, posing risks to non-target species. Fish, particularly the rainbow trout (Oncorhynchus mykiss), are highly vulnerable due to t...

Geographically weighted random forest fusing multi-source environmental covariates for spatial prediction of soil heavy metals.

Environmental pollution (Barking, Essex : 1987)
Efficient spatial prediction models for soil heavy metals are crucial for maintaining soil ecosystem health, promoting high-quality regional agriculture, and national food security. Traditional machine learning (ML) models often overlook spatial auto...

A meta-analysis of predictive accuracies and errors of biomass estimation models in Sub-Saharan Africa.

The Science of the total environment
Accurate biomass estimation is essential for forest monitoring, energy planning and carbon accounting in Sub-Saharan Africa (SSA), where destructive sampling is often impractical. Biomass estimation models (BEMs) offer scalable alternatives, but thei...

Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms.

Environmental science & technology
Real-time monitoring of phytoplankton in freshwater systems is critical for early detection of harmful algal blooms (HABs) to enable efficient response by water management agencies. This manuscript presents an image processing pipeline developed to a...