AI Medical Compendium Journal:
Marine pollution bulletin

Showing 11 to 20 of 64 articles

Detecting living microalgae in ship ballast water based on stained microscopic images and deep learning.

Marine pollution bulletin
Motivated by the need of rapid detection of living microalgae cells in ship ballast water, this study is intended to determine the activities of microalgae using stained microscopic images and detect the living cells with image processing algorithms....

A three-dimensional marine plastic litter real-time detection embedded system based on deep learning.

Marine pollution bulletin
Marine plastic pollution has emerged as a significant ecological and biological issue impacting global marine ecosystems. To develop real-time cleaning systems for marine plastic litter, we implemented a three-dimensional marine plastic litter real-t...

Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods.

Marine pollution bulletin
Microplastics (MPs) pose significant threats to ecosystems and human health due to their persistence and widespread distribution. This paper provides a comprehensive review of sampling methods for MPs in aquatic environments, soils, and biological sa...

The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential.

Marine pollution bulletin
Recent advancements in data analytics, predictive modeling, and optimization have highlighted the potential of integrating algal blooms (ABs) with Industry 4.0 technologies. Among these innovations, digital twins (DT) have gained prominence, driven b...

Identifying and quantifying multiple pollution sources in estuaries using fluorescence spectra and gradient-based deep learning.

Marine pollution bulletin
This study developed an intelligent method for identifying and quantifying water pollution sources in estuarine areas. It characterized the excitation-emission matrix (EEM) fluorescence spectra from seven end-members, including seawater, rainwater, a...

Long-term water quality assessment in coastal and inland waters: An ensemble machine-learning approach using satellite data.

Marine pollution bulletin
Accurate estimation of coastal and in-land water quality parameters is important for managing water resources and meeting the demand of sustainable development goals. The water quality monitoring based on discrete water sample analysis is limited to ...

Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments.

Marine pollution bulletin
Several coastal regions require operational forecast systems for predicting the transport of pollutants released during marine accidents. In response to this need, surrogate models offer cost-effective solutions. Here, we propose a surrogate modeling...

Water environment risk prediction method based on convolutional neural network-random forest.

Marine pollution bulletin
The accelerated processes of urbanization and industrialization globally have resulted in an increased risk to aquatic environments, posing a significant threat to the sustainable management of water resources and the health of ecosystems. Accurate p...

Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network.

Marine pollution bulletin
Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current automated monitoring technologies for detecting this litter face limitations in survey efficiency, c...

Plastic debris detection along coastal waters using Sentinel-2 satellite data and machine learning techniques.

Marine pollution bulletin
Few studies have effectively shown how to use satellites that gather optical data to monitor plastic debris in the marine environment. For the first time, floating macro-plastics distinguishable from seaweed are identified in optical data from the Eu...