We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Reliable water quality prediction and parametric analysis using explainable AI models
ClearDrinking 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.
Decoding the transport thresholds of emerging contaminants in watersheds using explainable machine learning
Researchers collected 517 water samples from the Huangshui River over four years and used an explainable machine learning framework with SHAP analysis to model how land use, landscape metrics, and climate variables drive the transport of microplastics, antibiotics, and heavy metals through the watershed.
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.
Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review
This systematic review examines machine learning applications for monitoring, predicting, and controlling drinking water quality, covering contaminants from disinfection byproducts to biofilms and antimicrobial resistance genes. While not specifically about microplastics, the ML approaches described are directly applicable to detecting and predicting microplastic contamination in engineered water systems.
Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review
This systematic review found that machine learning techniques can effectively monitor, predict, and control drinking water quality across engineered water systems. The applications span detection of physical, chemical, and microbiological contaminants, offering a scalable alternative to labor-intensive traditional methods for ensuring safe drinking water and identifying emerging pollutants like microplastics.
Enhancing water quality prediction: a machine learning approach across diverse water environments
Researchers compared seven machine learning models for predicting water quality parameters using six years of wastewater treatment plant data. The gradient boosting model performed best overall, accurately predicting parameters related to water contamination. While the study focuses on general water quality rather than microplastics specifically, these predictive tools could be applied to monitoring microplastic-relevant conditions in treatment systems.
Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents
Researchers trained 18 machine learning algorithms to predict levels of Acinetobacter bacteria — an indicator of water contamination — in rivers near wastewater treatment plants, finding that a gradient boosting model (XGBoost) achieved over 99% accuracy and that water temperature was the single most important predictor.
Efficient Data-Driven Machine Learning Models for Water Quality Prediction
This study tested machine learning methods for predicting water quality based on physical, chemical, and biological measurements. While focused on water safety testing rather than microplastics specifically, the automated classification tools developed here could help water treatment facilities quickly identify contaminated water. Better monitoring technology is important because current methods for detecting microplastics in water are slow and expensive.
Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model
Researchers applied a Random Forest Regression model to predict water quality indicators in Northwest China's urban wastewater treatment systems, achieving near-perfect accuracy (R² > 0.999) for key pollutants like nitrogen and phosphorus. The model offers a powerful tool for optimizing wastewater treatment and managing water resources in rapidly urbanizing regions.
Advantages and Challenges of AI-Driven Water Quality Monitoring
This review outlined the opportunities and challenges of applying artificial intelligence to water quality monitoring, including real-time contaminant detection and predictive modeling. The authors highlight AI's potential to improve efficiency and reduce costs in monitoring systems, while noting data quality and model interpretability as key challenges.
Cloud-Based Smart Water Quality Monitoring System using IoT Sensors and Machine Learning
Researchers developed a cloud-based smart water quality monitoring system using IoT sensors and machine learning to detect contamination parameters such as pH, nitrate, conductivity, and fecal coliform in real time. The system applies machine learning classification to correlated sensor data to enable early detection of health hazards from contaminated water sources.
Artificial intelligence (AI) based rapid water testing system
Researchers developed an AI-powered portable water testing system that integrates five analytical techniques for real-time water quality monitoring. The system can detect a range of contaminants including microplastics, heavy metals, and pathogens within seconds, offering a cost-effective alternative to traditional laboratory-based water testing for both industrial and domestic use.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines five analytical techniques to detect contaminants including heavy metals, pathogens, and microplastics in real time. The device uses an embedded machine learning model trained on diverse water samples to recognize contamination patterns. The study demonstrates a cost-effective approach to rapid water quality monitoring that could help identify microplastic pollution in both industrial and domestic water supplies.
Machine learning modeling of microplastics removal by coagulation in water and wastewater treatment
Researchers developed machine learning models to predict how effectively coagulation, a common water treatment process, can remove microplastics under different conditions. The best model achieved 96% accuracy and found that water temperature had the biggest negative effect on removal, while adding coagulant aids had the most positive effect. These tools could help water treatment plants optimize their processes to better remove microplastics from drinking water.
Prediction and Optimization of Process Parameters using Artificial Intelligence and Machine Learning Models
This review examined how artificial intelligence and machine learning models are being used to predict and optimize parameters for removing heavy metals and textile dyes from water. Researchers evaluated common AI approaches including artificial neural networks and genetic algorithms for improving water treatment efficiency. The study highlights the growing role of computational tools in designing more effective environmental remediation processes.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines multiple sensing techniques, including capacitance, resistance, UV, infrared, and Raman spectroscopy, to detect contaminants in real time. The system can identify a wide range of pollutants including microplastics, heavy metals, and organic compounds within seconds. The device aims to provide an accessible, rapid monitoring tool for water quality assessment in both industrial and domestic settings.
Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution
Researchers developed a deep learning system that can predict water quality in real time based on measurements like pH, turbidity, and dissolved solids. While not directly about microplastics, this kind of AI-powered monitoring tool could eventually be adapted to detect microplastic contamination in water supplies more quickly and affordably than current lab-based methods.
Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine
Researchers developed a hybrid machine learning framework to predict landfill leachate and groundwater quality, providing a robust monitoring tool to assess contamination risk to water resources near landfill sites.
Water Quality Management in the Age of AI: Applications, Challenges, and Prospects
This review examines how artificial intelligence is transforming water quality management through improved monitoring, prediction, and pollution tracking. Researchers found that combining AI with technologies like the Internet of Things and remote sensing has significantly enhanced real-time water quality analysis and early warning systems. However, major challenges remain around data quality, model transparency, and the ability to detect emerging pollutants like microplastics.
Alleviating Health Risks for Water Safety: A Systematic Review on Artificial Intelligence-Assisted Modelling of Proximity-Dependent Emerging Pollutants in Aquatic Systems
This systematic review summarizes how artificial intelligence can help track emerging pollutants, including microplastics, in water systems. It highlights that AI-driven models can predict contamination patterns more efficiently than traditional methods, which could help protect drinking water safety and public health.
Ensuring reliable feature importance in food chemistry AI
Researchers demonstrated that standard machine learning models applied to a microplastic-cancer dataset can produce misleading feature importance scores because they optimize prediction rather than causal inference, and proposed a validated pipeline combining nonparametric association tests and stability audits to improve the reliability of AI-driven risk assessment in food chemistry.
Deciphering geospatial variations in water quality of a perennial river for human consumption and agricultural application
Researchers analyzed geospatial variation in water quality along a perennial river to assess human health risks from drinking water exposure, identifying hotspots of contamination exceeding safety thresholds. The study provides a risk-based framework for prioritizing water treatment interventions.
A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years
This comprehensive review analyzes over 170 studies on using machine learning to predict water quality, covering both individual pollutant indicators and overall water quality indices. The authors highlight key challenges including data acquisition, model uncertainty, and the need to incorporate water flow dynamics into predictions. While broadly focused on water quality, these predictive tools are relevant to microplastics research because they could help forecast microplastic concentrations in water systems based on environmental conditions.
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.