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Papers
20 resultsShowing papers similar to Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis
ClearEnvironmental Risk Assessment of the Harbin Section of the Songhua River Basin Based on Multi-Source Data Fusion
This paper is not about microplastic pollution. It evaluates environmental risks to water quality in the Harbin section of the Songhua River Basin in China, using neural networks and multi-source data to assess pollution from agricultural, industrial, and domestic sources across different districts.
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.
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.
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.
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.
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 Comprehensive Method for Water Environment Assessment considering Trends of Water Quality
Researchers developed a comprehensive water quality assessment method that accounts for both current pollution levels and trends over time, applying it to rivers feeding a major Chinese reservoir. Water quality assessment frameworks are increasingly being adapted to include microplastic contamination as a standard monitoring parameter.
Microplastic pollution in the Yangtze River: Characterization, influencing factors, and scenario-based predictions using machine learning method
Microplastic pollution in the Yangtze River was characterized across multiple sampling sites, documenting spatial patterns in particle abundance, polymer types, and size distributions. As one of the world's largest rivers, the Yangtze's microplastic burden has major implications for plastic delivery to the Pacific Ocean.
Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China
This study used spatial cluster analysis to identify surface water quality pollution areas and trace pollution sources across Henan Province, China. Spatial dependence analysis revealed distinct contaminated zones and their likely sources, enabling targeted remediation strategies for different pollution types.
Microplastics in China’s surface water systems: Distribution, driving forces and ecological risk
Researchers compiled over 14,000 samples from across China to map microplastic pollution in surface water systems using machine learning models. They found that microplastic abundance varied enormously across regions, driven by a complex mix of human activities and natural conditions. The ecological risk assessment revealed that watersheds in nearly all Chinese provinces face high to extremely high contamination levels, underscoring the urgency of nationwide management efforts.
A Low-Cost Detection Method for Nitrite Content in a Mariculture Water Environment Based on an Improved Residual Network
This paper is not about microplastic pollution. It describes a low-cost method for detecting nitrite levels in aquaculture water using chemical reagents and a neural network for image recognition, aimed at helping small-scale fish farmers in China monitor water quality more affordably.
Water Quality Evaluation, Spatial Distribution Characteristics, and Source Analysis of Pollutants in Wanquan River, China
This paper is not about microplastics — it assesses water quality in a Chinese river basin, finding that agricultural runoff and domestic sewage are the main pollution sources, without examining plastic contamination.
Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay, China
Researchers combined dual stable isotope analysis with statistical modeling to trace nitrogen pollution sources in two coastal rivers flowing into Hangzhou Bay, finding that soil runoff and domestic wastewater together contributed roughly two-thirds of total nitrogen, with aquaculture tailwater as the second-largest source.
Evaluation of nitrate pollution sources in surface water across the typical rural-urban interface: a case study of Wen-Rui Tang River, China
Researchers identified the main sources of nitrate pollution in a rural-urban Chinese river, finding that human sewage and agricultural runoff were the primary contributors. While focused on nitrogen pollution, the study illustrates how mixed land use creates complex water quality challenges in rivers that also carry microplastics.
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.
AI-Based Waste Detection for Water Quality Monitoring in the Cisadane River: A Deep Learning Approach
Researchers developed a hybrid CNN+YOLOv7 deep learning model for detecting organic and inorganic waste in the Cisadane River, Indonesia, achieving 87% classification accuracy. The AI system enables real-time water quality monitoring for waste including plastics, supporting faster intervention by environmental agencies.
Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
Researchers used machine learning to predict microplastic levels in rivers across seven coastal regions of China, identifying population density, urbanization, and industrial activity as the strongest predictors of contamination. The models successfully captured how microplastics accumulate and move through river systems using 19 different environmental and human factors. This approach could reduce the need for costly field sampling while helping target pollution management efforts where they are needed most.
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.
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.