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Papers
61,005 resultsShowing papers similar to Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus
ClearData-Driven Models’ Integration for Evaluating Coastal Eutrophication: A Case Study for Cyprus
Researchers developed and compared two artificial neural network models trained on in situ monitoring data to predict coastal eutrophication in Cypriot waters, demonstrating a data-driven approach to environmental monitoring that supports the aquaculture industry's regulatory compliance requirements.
Simulation of nutrient management and hydroclimatic effects on coastal water quality and ecological status—The Baltic Himmerfjärden Bay case
Researchers used computer modeling to simulate how different nutrient management scenarios and climate conditions would affect water quality and ecological status in the Baltic Sea's Himmerfjarden Bay. The study provides a tool for coastal managers to evaluate strategies for reducing eutrophication under future climate scenarios.
Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake
Researchers used a combination of field measurements, weather data, and satellite imagery to estimate chlorophyll-a concentrations at different depths in a Chilean lake. They compared deep learning and statistical models and found all three approaches performed well for predicting algal levels in the freshwater ecosystem. The study advances water quality monitoring techniques that can help track environmental changes, including those potentially linked to pollution.
Good eutrophication status is a challenging goal for coastal waters
Not relevant to microplastics — this study models nutrient pollution and eutrophication in the Baltic Sea's Archipelago Sea, finding that meeting current international nutrient reduction targets can improve outer coastal water quality but is insufficient for inner coastal zones, without addressing microplastic pollution.
Water Quality Monitoring And Ground Water Level Prediction Using Machine Learning
Researchers applied machine learning techniques to water quality monitoring and groundwater level prediction, demonstrating the potential of data-driven approaches for environmental sensing and resource management.
Drinking water potability prediction using machine learning approaches: a case study of Indian rivers
Researchers applied machine learning techniques to predict drinking water quality in Indian rivers based on key parameters like pH, dissolved oxygen, and bacterial counts. Their models achieved high accuracy in classifying water as potable or non-potable. The study demonstrates how data-driven approaches could help developing countries monitor water safety more efficiently, especially in regions where traditional testing infrastructure is limited.
Use of the Sentinel-2 and Landsat-8 Satellites for Water Quality Monitoring: An Early Warning Tool in the Mar Menor Coastal Lagoon
Researchers used Sentinel-2 and Landsat-8 satellites to monitor water quality during the 2021 ecological crisis in Mar Menor, a large coastal lagoon in the Western Mediterranean. The satellite-based methods accurately measured chlorophyll-a and turbidity with low error margins, enabling identification of eutrophication hotspots. The study demonstrates that satellite remote sensing can serve as a cost-effective early warning tool for monitoring water quality in coastal environments.
Regional Satellite Algorithms to Estimate Chlorophyll-a and Total Suspended Matter Concentrations in Vembanad Lake
Researchers developed regional satellite algorithms to estimate chlorophyll-a concentrations and total suspended matter in Vembanad Lake, India, using remote sensing data to monitor water quality in a highly productive but increasingly polluted coastal ecosystem. The algorithms were calibrated against in-situ measurements and found to improve the accuracy of water quality assessments compared to global ocean-color models, supporting sustainable development monitoring goals.
Preliminary approach to modelling eutrophication – anthropopressure impact on sea water quality
This chapter reviews methods for modeling eutrophication — the process by which excess nutrients cause algal blooms and oxygen depletion in water — with a focus on the Baltic Sea. Eutrophication interacts with microplastic pollution because nutrient-rich conditions promote the biofilm communities that colonize plastic particles.
A review of remote sensing in coastal aquaculture: data, geographic hotspots, methods, and challenges
This review examines remote sensing applications in coastal aquaculture, synthesising data sources, geographic hotspots, and methodological advances that allow satellite and aerial imagery to monitor aquaculture facility extent, water quality, and environmental impacts including plastic debris from aquaculture infrastructure.
Machine Learning Approaches for Microplastic Pollution Analysis in Mytilus galloprovincialis in the Western Black Sea
Machine learning models were applied to microplastic data from Mediterranean mussels (Mytilus galloprovincialis) in the western Black Sea, successfully predicting MP contamination levels and identifying pollution hotspots relevant to seafood safety and fisheries management.
Coastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery
Researchers used high-resolution satellite imagery combined with machine learning to detect and map coastal marine debris density in southern Japan, finding that satellite-based methods can estimate debris amounts and types on beaches with reasonable accuracy.
Simulation of Chlorophyll a Concentration in Donghu Lake Assisted by Environmental Factors Based on Optimized SVM and Data Assimilation
An optimized machine learning model was developed and combined with data assimilation techniques to simulate chlorophyll-a concentrations — an indicator of algal growth — in Donghu Lake, China. The model accurately reproduced observed chlorophyll patterns by incorporating environmental factors like temperature and nutrients. Better lake eutrophication models support water quality management and early warning of harmful algal blooms.
A review of remote sensing in coastal aquaculture: data, geographic hotspots, methods, and challenges
This review synthesises remote sensing methods for monitoring coastal aquaculture, covering satellite and aerial data sources, identifying geographic hotspots of aquaculture expansion, and evaluating current and emerging techniques for assessing environmental impacts such as plastic debris from nets, cages, and buoys.
Predicting Aquaculture Water Quality Using Machine Learning Approaches
Researchers compared four machine learning approaches for predicting water quality parameters in industrial aquaculture systems, finding that back propagation and radial basis function neural networks outperformed support vector machine models for most parameters. The models achieved sufficient accuracy to support real-time management decisions without continuous in-situ monitoring.
A review of remote sensing in coastal aquaculture: data, geographic hotspots, methods, and challenges
A review of remote sensing applications in coastal aquaculture examined available data sources, geographic coverage, and analytical methods for monitoring aquaculture zones. This is relevant to microplastic research because aquaculture operations are both exposed to and potential sources of microplastic contamination in coastal waters.
Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review
This systematic review of over 15,000 papers identified 103 coastal phenomena and 39 parameters that can now be accurately mapped and monitored using remote sensing data. The authors validated 91% of retrieved parameters, demonstrating that satellite and aerial remote sensing has become a comprehensive tool for tracking coastal environmental changes including pollution and habitat degradation.
Data-driven machine learning modeling reveals the impact of micro/nanoplastics on microalgae and their key underlying mechanisms
Researchers used machine learning to predict how micro- and nanoplastics affect freshwater algae, training models on a decade of published experimental data. The best-performing model identified plastic concentration, exposure time, and particle size as the most important factors determining toxicity. The study offers a data-driven framework that could reduce the need for time-consuming laboratory experiments when assessing microplastic risks to aquatic organisms.
Water Quality Modelling, Monitoring, and Mitigation
This special issue review examines advances in water quality modelling, monitoring, and mitigation approaches, noting that while models and indices have become central tools for water resource management, site-specific limitations and high uncertainty in predictions remain key challenges for reliably assessing freshwater body health.
Microplastic Loads in Freshwater Lakes: Prioritized Regions and Management Strategies
Researchers compiled trawl-net survey data from freshwater lakes globally, applied redundancy analysis and structural equation modeling to identify key drivers of microplastic concentrations, and used machine learning to estimate loads in under-sampled regions, producing the first global prioritization framework for lake microplastic management.
Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery
Researchers developed classification algorithms using Sentinel-2 satellite imagery to detect floating plastic debris in coastal waters near Cyprus and Greece. They tested both unsupervised and supervised methods and found that a semi-supervised fuzzy c-means approach achieved the highest accuracy for identifying plastics. The study demonstrates that remote sensing technology can be an effective tool for monitoring and mapping marine plastic pollution at scale.
Machine learning approach for automated beach waste prediction and management system: A case study of Mumbai
Researchers developed a machine learning system to predict beach waste generation patterns in Mumbai, aiming to enable more effective and automated waste management for one of the world's most polluted coastal cities.
An Effective Machine Learning Scheme to Analyze and Predict the Concentration of Persistent Pollutants in the Great Lakes
Scientists applied multiple machine learning methods to predict concentrations of persistent organic pollutants in the Great Lakes, finding that LSTM neural networks outperformed simpler models for these complex time-series patterns. Similar predictive modeling could track microplastic concentrations in large water bodies over time.
Integrated Analytical Approach: An Added Value in Environmental Diagnostics
Researchers demonstrated the value of an integrated multi-technique analytical approach for environmental diagnostics, showing through three marine case studies that combining multiple survey methods yields a more complete and accurate picture of anthropogenic environmental impacts than any single method alone.