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Papers
61,005 resultsShowing papers similar to Flux to Flow: a Clearer View of Earth’s Water Cycle Via Neural Networks and Satellite Data
ClearThe Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed
This paper is not about microplastics; it uses satellite rainfall data, HEC-RAS flood modeling, and artificial neural networks to predict flood inundation heights in the Majalaya Watershed of Indonesia.
Quantifying 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.
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.
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.
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.
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.
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.
Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model
Researchers developed a deep neural network model applied to Landsat-8 satellite data to automatically identify water quality grades for lakes in the middle Yangtze River reaches, demonstrating that machine learning and remote sensing can provide cost-effective large-scale monitoring as an alternative to labor-intensive in situ measurements.
Coupled intelligent prediction model for medium- to long-term runoff based on teleconnection factors selection and spatial-temporal analysis
This paper developed a coupled intelligent prediction model for medium- to long-term river runoff forecasting, combining teleconnection analysis with machine learning to improve prediction accuracy for water resource planning. The model outperformed traditional hydrological forecasting methods.
Detection of Vegetation Spectral Signatures in Hyperspectral Images using Artificial Neural Networks
This study developed a computer program that can identify plants and vegetation in detailed satellite images by analyzing how they reflect different colors of light. The technology successfully detected about 42% of an area as vegetation in a test neighborhood, which was more accurate than older methods. This could help scientists better monitor environmental changes like deforestation or urban green spaces that affect air quality and human health.
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.
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.
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.
Analysing of rainfall-runoff modelling using a hybrid DNN-SGD optimisation in Sub Basin of Brahmaputra River, India
Researchers developed a hybrid deep neural network optimized with stochastic gradient descent (DNN-SGD) for rainfall-runoff modelling in a sub-basin of the Brahmaputra River in India, evaluating model accuracy using coefficient of determination, mean squared error, and root mean squared error metrics. The study demonstrated that the DNN-SGD model achieved superior runoff prediction performance compared to conventional hydrological approaches in the monsoon-dominated river basin.
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.
SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements
Researchers developed SNOWED, an automatically constructed dataset of satellite imagery with labeled water edges, enabling deep learning models to accurately detect and monitor shoreline changes for environmental monitoring applications.
Spatiotemporal graph neural networks for analyzing the influence mechanisms of river hydrodynamics on microplastic transport processes
Spatiotemporal graph neural networks were applied to model how microplastic contamination spreads across connected water bodies over time. This AI-driven modeling approach can improve real-time prediction and management of microplastic pollution in river and lake networks.
Water environment response of urban water networks in the Pearl River Delta (China) under the influence of typhoon rain events
This study used artificial neural networks to model water quality parameters in the urban water network of China's Pearl River Delta region, examining how typhoon rain events affect pollutant concentrations. The research contributes to understanding how extreme weather events — which are increasing with climate change — flush pollutants including microplastics from urban environments into waterways.
The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical
Researchers analyzed climate variation and human activity effects on karst spring discharge using wavelet coherence analysis, finding that anthropogenic factors including land-use changes increasingly influence groundwater dynamics alongside natural climate variability.
Response Time of Vegetation to Drought in Weihe River Basin, China
This is a hydrology study analyzing how vegetation in China's Weihe River Basin responds to drought using satellite vegetation indices; it is not a microplastics research paper.
Assessment of surface water dynamics through satellite mapping with Google Earth Engine and Sentinel-2 data in Manipur, India
Researchers used Google Earth Engine and Sentinel-2 satellite imagery to map seasonal surface water dynamics in Manipur, India, accurately tracking the extent and timing of water body changes across the region to support watershed planning.
Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models
Researchers used Landsat 8 satellite imagery and machine learning to assess the spatial extent and recovery trajectory of oil spill damage to coastal vegetation and wetlands in Nigeria, demonstrating that remote sensing combined with AI models can track long-term ecosystem recovery.
Quantifying the environmental impact of pollutant plumes from coastal rivers with remote sensing and river basin modelling
Researchers combined satellite remote sensing with river basin modeling to track pollution plumes from four coastal rivers in Italy, measuring their size, timing, and pollutant loads. The method can estimate how much contamination comes from rainfall runoff versus wastewater discharge, helping managers better understand and address coastal pollution threats.
Quantifying geomorphically effective floods using satellite observations of river mobility
This study used satellite imagery to measure how floods reshape river channels by tracking river mobility over time. Researchers found that cumulative flood power — combining magnitude and duration — better predicts channel change than either factor alone. The findings improve understanding of how rivers respond to extreme flood events.