AIMC Topic: Environmental Monitoring

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Deep learning based soft-sensor for continuous chlorophyll estimation on decentralized data.

Water research
Monitoring the concentration of pigments like chlorophyll (Chl) in water-bodies is a key task to contribute to their conservation. However, with the existing sensor technology, measurement in real-time and with enough frequency to ensure proper risk ...

Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging.

Environmental pollution (Barking, Essex : 1987)
The fast and accurate identification of MPs in environmental samples is essential for the understanding of the fate and transport of MPs in ecosystems. The recognition of MPs in environmental samples by spectral classification using conventional libr...

New deep learning-based methods for visualizing ecosystem properties using environmental DNA metabarcoding data.

Molecular ecology resources
Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The complexity of these data prevents current methods from extracting and analyzing all the relevant eco...

Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Journal of environmental sciences (China)
Severe ground-level ozone (O) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution c...

Is deeper always better? Evaluating deep learning models for yield forecasting with small data.

Environmental monitoring and assessment
Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based o...

Air pollution forecasting based on wireless communications: review.

Environmental monitoring and assessment
The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and lo...

Ocean Stratification Impacts on Dissolved Polycyclic Aromatic Hydrocarbons (PAHs): From Global Observation to Deep Learning.

Environmental science & technology
Ocean stratification plays a crucial role in many biogeochemical processes of dissolved matter, but our understanding of its impact on widespread organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), remains limited. By analyzing disso...

Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows.

Environmental monitoring and assessment
Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), a...

Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.

Journal of environmental sciences (China)
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on s...

Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India.

Environmental monitoring and assessment
The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are wi...