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61,005 resultsShowing papers similar to Data-Driven Models’ Integration for Evaluating Coastal Eutrophication: A Case Study for Cyprus
ClearData-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus
Researchers developed data-driven models to evaluate coastal eutrophication using Cyprus as a case study, examining how monitoring data can be used to assess hypoxia and harmful cyanotoxin production risks in island coastal waters. The models demonstrated the utility of machine learning approaches for eutrophication assessment where direct measurement programmes are limited.
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.
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.
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.
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.
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.
Advancements and Challenges in Deep Learning-Driven Marine Data Assimilation: A Comprehensive Review
This review surveys how deep learning is being applied to marine data assimilation — the process of combining model predictions with real-world observations to improve ocean forecasting. The authors identify key challenges in data quality, model interpretability, and integration with physical ocean models.
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.
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.
AI-Driven Framework Development for Predictive Classification of Microplastic Concentration of Aquatic Systems in the United States
Researchers compared four machine learning models—logistic regression, random forest, support vector machine, and a neural network—for predicting microplastic density in US coastal waters across three regions. The support vector machine performed best with 93.94% average accuracy, demonstrating the potential of AI-driven tools for microplastic monitoring.
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.
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.
A Combination of Machine Learning Algorithms for Marine Plastic Litter Detection Exploiting Hyperspectral PRISMA Data
Researchers applied a combination of machine learning algorithms to hyperspectral satellite imagery from the PRISMA satellite to detect marine plastic litter along coastlines and ocean surfaces. The multi-algorithm approach improved detection accuracy over single-model methods and demonstrated the potential for satellite-based monitoring of ocean plastic pollution at scale.
Modeling of daily groundwater level using deep learning neural networks
Researchers applied a CNN-biLSTM deep learning model to predict daily groundwater levels, finding it outperformed conventional modeling approaches by capturing both spatial patterns and temporal dependencies in the data. The method offers improved accuracy for groundwater monitoring, which is critical for managing increasingly stressed freshwater resources.
Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen
Researchers developed and compared neural network models for predicting dissolved oxygen concentrations in water using machine learning metaheuristic algorithms. Dissolved oxygen is a key indicator of aquatic ecosystem health, and accurate prediction tools support monitoring of water bodies affected by plastic and other pollutants.
Predicting microplastic accumulation zones and shoreline changes along the Kelantan coast, Malaysia, using integrated GIS and ANN models
Researchers combined GIS with an artificial neural network to predict microplastic accumulation zones along Malaysia's Kelantan coast, achieving R=0.972 predictive accuracy and identifying shoreline erosion-prone areas as the primary deposition hotspots for microplastic pollution.
New Graph-Based and Transformers Deep Learning Models for River Dissolved Oxygen Forecasting
Researchers developed new graph-based and transformer deep learning models to forecast dissolved oxygen levels in the Credit River Watershed, outperforming earlier approaches. Accurate dissolved oxygen prediction is important for detecting eutrophication and assessing water quality impacts from pollution.
Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis
Researchers used artificial neural network analysis to assess water quality and identify pollution causes in the Tuojiang River Basin in China, examining parameters including dissolved oxygen and ammonia-nitrogen to understand contamination patterns and risks in this waterway.
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.
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.
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.
First Steps towards a near Real-Time Modelling System of Vibrio vulnificus in the Baltic Sea
Researchers developed initial steps toward a near real-time modeling system for Vibrio vulnificus in the Baltic Sea, testing hydrodynamic and biogeochemical model data as inputs to predict pathogen concentrations along the German coast.
Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach
Researchers used artificial neural network models to predict heavy metal contamination in soils near illegal landfills close to agricultural areas. The study found that illegal landfilling significantly impacts surrounding soil quality and proposes these predictive models as effective tools for environmental risk management and decision-making.
Advancing Plastic Pollution Monitoring Through Enhanced Protocols and Deep Learning: applicability and effectiveness in real-world scenarios (Le Stang, France)
Researchers developed and tested a deep learning image analysis tool to enhance monitoring of beach plastic pollution, specifically targeting meso- and large microplastics at the wrack line in Brittany, France. The AI model achieved high detection accuracy under real-world conditions and integrated with established French national monitoring protocols, demonstrating feasibility for scalable automated beach litter surveillance.