AIMC Topic: Environmental Monitoring

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Temporal variability in temperate mesophotic ecosystems revealed with over a decade of monitoring with an autonomous underwater vehicle.

Marine environmental research
Rocky reef temperate mesophotic ecosystems (TMEs) are increasingly recognised for their spatial extent and high biodiversity. Platforms such as autonomous underwater vehicles (AUVs) allow large-scale collection of benthic imagery, facilitating descri...

A sustainable industrial waste control with AI for predicting CO2 for climate change monitoring.

Journal of environmental management
As the challenge of climate change continues to grow, we need creative solutions to predict better and track industrial waste carbon emissions, focusing on sustainable waste management practices. The present study proposes a state-of-the-art Metavers...

Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh.

Environmental geochemistry and health
Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Developing a reliable predictive model for heavy metal concentrations in this region has proven to be a...

Source apportionment of PM particles in the urban atmosphere using PMF and LPO-XGBoost.

Environmental research
Atmospheric particulate matter (PM), as a leading part of air pollution, affects health in many ways. Thus, identifying and quantifying the contribution of atmospheric particulate matter sources of PM is vital for developing effective air quality man...

Temporally boosting neural network for improving dynamic prediction of PM concentration with changing and unbalanced distribution.

Journal of environmental management
Increasing medical research evidence suggests that even low PM concentrations may trigger significant health issues. Hence, an accurate prediction of PM holds immense significance in securing public health safety. However, current data-drive predicti...

Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method.

Journal of environmental management
The use of data driven deep learning models to predict and monitor Harmful Algal Blooms (HABs) has evolved over the years due to increasing technologies, availability of high frequency data, and statistical prowess. Despite the prowess of these data ...

Assessment of marine eutrophication: Challenges and solutions ahead.

Marine pollution bulletin
Marine eutrophication remains a pressing global environmental challenge, demanding urgent advances in science-based assessment frameworks to mitigate its ecological and socio-economic impacts. Current methodologies, however, face critical limitations...

Assessing the impacts of cascade reservoirs on Pearl River environmental status using machine learning and satellite-derived chlorophyll-a concentrations.

Journal of environmental management
Rivers play a crucial role in in global matter cycling and energy flow, contributing significantly to biogeochemical cycles and the development of human civilization. Reservoirs, as prevalent artificial water bodies, modify river flow and impact ener...

An Approach for Detecting Mangrove Areas and Mapping Species Using Multispectral Drone Imagery and Deep Learning.

Sensors (Basel, Switzerland)
Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is crucial to develop more efficient, intelligent, and accurate monitoring methods for mang...

Identifying and addressing challenges in gross pollutant trap maintenance: perspectives from the Australian stormwater industry.

Marine pollution bulletin
A common approach to removing pollution from stormwater is through the installation of gross pollutant traps (GPTs). However, GPTs are often not maintained effectively, leading to pollution accumulation and additional pollution bypassing into natural...