AI Medical Compendium Topic:
Environmental Monitoring

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Deep learning-based total suspended solids concentration classification of stream water surface images captured by mobile phone.

Environmental monitoring and assessment
The continuous monitoring of total suspended solids (TSS) in streams plays an important role in the management of hydrological processes, and TSS is also a decisive parameter in the control of pollution in streams. Determination of TSS involves both ...

Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

Integrated environmental assessment and management
Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem se...

Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning Model.

Environmental science & technology
Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicabil...

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria.

Environmental monitoring and assessment
The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over th...

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia.

Environmental monitoring and assessment
Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines...

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...