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Papers
61,005 resultsShowing papers similar to The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed
ClearQuantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility
This paper is not about microplastics; it uses satellite imagery and machine learning to study how flood magnitude, duration, and hydrograph shape determine lateral erosion and channel change in rivers.
Insights into suspended sediment and microplastic budget of a lowland river: integrating in-situ measurements, Sentinel-2 imagery, and machine learning
Researchers combined river measurements, satellite imagery, and machine learning to track how much microplastic the Tisza River in Hungary carries downstream each day. They found that flood events spike microplastic transport five-fold, meaning a small number of high-water days drive the majority of plastic particles reaching the sea.
Flux to Flow: a Clearer View of Earth’s Water Cycle Via Neural Networks and Satellite Data
This dissertation developed neural network methods to enhance the spatial resolution of satellite measurements of Earth's water cycle, enabling finer-scale monitoring of hydrological processes such as precipitation, evaporation, and runoff across diverse environments.
Enhanced spatiotemporal mapping of urban wetland microplastics: An interpretable CNN-GRU approach using satellite imagery and limited samples
Researchers built an interpretable CNN-GRU deep learning model combining satellite remote sensing with limited in-situ measurements to map microplastic distribution in urban wetlands with enhanced spatiotemporal resolution, enabling more comprehensive monitoring with less field sampling.
Advancing hydrological monitoring using image-based techniques: challenges and opportunities
This paper is not about microplastics — it reviews image-based techniques (including remote sensing and computer vision) for hydrological monitoring of water bodies, discussing challenges and opportunities in measuring water flow, flood events, and water quality.
Machine Learning for Flood Resiliency—Current Status and Unexplored Directions
This systems review assessed machine learning applications across the flood management spectrum—reservoir inflow prediction (LSTM), levee failure detection (CNN), and low-impact development control (reinforcement learning). The paper is focused on flood resilience engineering and does not contain microplastics research.
Predicting microplastic quantities in Indonesian provincial rivers using machine learning models
This study used machine learning models to predict microplastic levels in rivers across 24 Indonesian provinces based on environmental and economic data. Temperature, economic output, and population density were the strongest predictors of microplastic pollution. The approach could help environmental agencies monitor and manage microplastic contamination in freshwater systems more efficiently.
Extraction of Surface Water Extent: Automated Thresholding Approaches
This paper is not relevant to microplastics — it evaluates automated thresholding algorithms applied to satellite remote sensing data for mapping surface water extent and monitoring floods and droughts.
Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy
Researchers developed satellite-based models using neural network algorithms to estimate riverine microplastic concentrations, using suspended sediment concentration as a proxy, offering a cost-effective approach for broad-scale freshwater microplastic monitoring.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment
Researchers developed a methodology to improve river discharge estimates from the SWOT satellite mission in tidally influenced river environments in southern Vietnam. They found that measurement errors from the satellite could be reduced by optimizing the size of river segments analyzed. While not directly related to microplastics, the study advances remote sensing tools that can help monitor coastal water dynamics relevant to understanding pollutant transport in estuarine systems.
Machine learning approaches for predicting microplastic pollution in peatland areas
Researchers used machine learning models to predict microplastic quantities in peatland sediments in Vietnam from easily measurable environmental parameters. The study found that pH, total organic carbon, and salinity were the most influential factors, and that Least-Square Support Vector Machines and Random Forest models could effectively predict microplastic contamination levels.
Evaluation of microplastic pollution in urban lentic ecosystem using remote sensing, GIS, and Support Vector Machine (SVM): relevance for environmental and ecological risk
Researchers assessed microplastic pollution in 24 urban ponds and lakes in Kolkata, India, finding significantly higher concentrations during the post-monsoon season, with fibers making up about 59% of all particles. They developed machine learning and remote sensing models that achieved up to 98% accuracy in identifying water bodies and predicting microplastic levels from satellite imagery. The study demonstrates that combining field sampling with remote sensing technology can enable large-scale monitoring of urban microplastic pollution.
Spatial prediction of physical and chemical properties of soil using optical satellite imagery: a state-of-the-art hybridization of deep learning algorithm
Not relevant to microplastics — this study uses deep learning models combining satellite imagery and topographic data to predict soil chemical properties (pH, organic carbon, phosphorus, potassium) across a region of Iran, with no connection to microplastic pollution.
Seasonal and annual tropical river pattern change detection using machine learning
Researchers applied machine learning to Sentinel-2 satellite imagery to detect seasonal and annual changes in tropical river channel patterns in a region with strongly seasonal rainfall, successfully classifying active channel landforms including water, bare sediment, and vegetated bars. The approach provides a scalable method for monitoring dynamic tropical river systems.
Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery
Researchers used satellite imagery and machine learning to detect and map litter accumulation in the Tisza River, Hungary, finding that dams are major hotspots and that flood events drive litter transport. While models performed well in controlled tests, real-world accuracy was moderate, highlighting the challenge of using satellite data to monitor riverine plastic pollution at scale. This matters because rivers are a primary pathway for plastic and microplastic debris reaching the ocean.
Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring
This meta-analysis and bibliometric review found that machine learning models, particularly random forest and neural networks, outperform conventional statistical methods for satellite-based water quality monitoring. While focused on remote sensing rather than microplastics directly, the methods could be applied to large-scale tracking of plastic pollution in surface waters.
The need of ecohydrological research in tropical forests for healthy watersheds
This paper is not relevant to microplastics; it argues for more ecohydrological research in tropical forests to understand water cycling and watershed health.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
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.
Estimating microplastic concentrations in surface water using satellite-based turbidity measurements: a case study on the New River, VA
Researchers used satellite-derived turbidity measurements as a proxy for microplastic concentrations in the New River, Virginia, developing and validating a model that enables broader spatial and temporal monitoring of riverine microplastic pollution without intensive field sampling.
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.
Coastal Dynamics Analysis Based on Orbital Remote Sensing Big Data and Multivariate Statistical Models
Not relevant to microplastics — this remote sensing study uses satellite data and statistical models to analyze 36 years of shoreline change along the São Paulo, Brazil coastline, focusing on erosion and accretion rates.
Microplastic deposits prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
Researchers integrated remote sensing, GNSS altimetric surveys, micro-Raman spectroscopy, and machine learning models to predict microplastic deposition patterns on urban sandy beaches along the central Sao Paulo coastline, finding MP concentrations ranging from 6 to 35 MPs/m2.