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Papers
61,005 resultsShowing papers similar to Web-Based Information and Analytical Monitoring System Tools – Online Visualization and Analysis of Surface Water Quality of Mining and Chemical Enterprises
ClearDesign and Analysis of a Water Quality Monitoring Data Service Platform
Researchers designed a water quality monitoring data service platform that integrates real-time data collection with analysis and data mining capabilities, addressing gaps in traditional systems that focus solely on collection while ignoring dirty data and transmission failures.
A WebGIS-Based System for Supporting Saline–Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China
Researchers developed a web-based geographic information system for monitoring and predicting soil ecological conditions in the Yellow River Delta region of China, an area affected by saline-alkali soils. The system uses machine learning models to assess soil health indicators and provides online visualization and prediction tools. This platform could help land managers make more informed decisions about agricultural practices and environmental risk reduction in vulnerable soil ecosystems.
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.
ОЦІНКА ЯКОСТІ ТА ЕКОЛОГІЧНИЦЙ СТАН РІЧКИ САКСАГАНЬ У КОНТЕКСТІ ГОСПОДАРСЬКО-ПИТНОГО ТА РИБОГОСПОДАРСЬКОГО ПРИЗНАЧЕННЯ
This Ukrainian-language study assessed the water quality and ecological status of the Saksahan River in the context of its suitability for drinking water supply and fisheries management, in a region affected by intensive iron ore mining and significant mine water discharge. The research characterized the specific hydrochemical conditions resulting from industrial exploitation of the Kryvbas ore deposits.
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.
An Environment-visualization System with Image-based Retrieval and Distance Calculation Method
This paper describes an environment visualization system that uses satellite imagery and field data to display pollution levels and calculate distances between environmental hazard sites. It is a geographic information systems paper aimed at improving environmental communication and monitoring.
Foss4g Based High Frequency and Interoperable Lake Water-quality Monitoring System
This paper describes an open-source, high-frequency water quality monitoring system for lakes using free geospatial software tools. The system enables continuous tracking of changes including those driven by pollution, supporting better management of freshwater resources.
A First Step towards Developing a Decision Support System Based on the Integration of Environmental Monitoring Activities for Regional Water Resource Protection
Researchers developed a decision support system integrating environmental monitoring data on diffuse pollutants - including nitrates, pesticides, metals, and plastics - to characterise the degradation state of water resources at the municipal level. The open-source system identifies areas sharing similar environmental characteristics and pollution pressure values, providing policymakers with tools for more accurate land management decisions.
An Environment-Visualization System with Image-Based Retrieval and Distance Calculation Method
This paper describes an environmental visualization system using satellite imagery and field data to display pollution information and calculate distances between environmental hazard sites. It is a geographic information systems paper aimed at improving environmental communication.
Study on the Influence of Mining Activities on the Quality of Deep Karst Groundwater Based on Multivariate Statistical Analysis and Hydrochemical Analysis
This study examined how long-term coal mining activities in China affected deep karst groundwater chemistry, finding significant changes to water quality that threaten safe drinking water supply in mining regions.
Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management
This review examined the importance of integrating multiple information sources in surface water monitoring systems, including sampling techniques, process analysis, and early warning systems for sustainable watershed management.
The Challenge of Interdisciplinarity at the Intersection of Groundwater Management and Visualization Research
Researchers conducted a design study developing an interactive visualization prototype for temporal and spatial hydrogeological data, created in close collaboration with hydrogeological researchers to support model calibration and industry communication. Key findings from the iterative design process showed that enabling scientists to visualize their own data dramatically increased engagement among initially skeptical users, and rapid prototyping tools proved essential for unlocking the practical value of visual analytics in groundwater management.
Integrated assessment of the surface source of water supply according to environmental-risk indicators
Researchers assessed surface water quality and environmental health risks in the Dnipro Reservoir watershed using a four-stage risk assessment framework, identifying wastewater discharge, uncontrolled runoff, and bank erosion as major pollution sources. The study quantified health risks from multiple contaminants to support water supply management.
Analysis of concentrations of biogenic compounds discharged into water bodies with municipal wastewater
Researchers investigated how elevated concentrations of nitrogen and phosphorus compounds in municipal wastewater discharged into Ukrainian surface water bodies drive eutrophication and cyanobacterial blooms, identifying biogenic nutrient pollution as the primary cause of poor water quality compared to European standards.
Аналіз досвіду застосування біологічних систем для раннього запобігання забрудненню вод
This review analyzed international experience with biological early-warning systems for water pollution monitoring, evaluating how aquatic organisms are used as sentinel species to detect contamination from industrial, agricultural, and domestic sources before it reaches critical levels.
Monitoring Water Quality: Suggestions and Prospects
This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
Monitoring Water Quality: Suggestions and Prospects
This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
Knowledge Mapping Analysis of Water EcologicalRisk Studies in Mining Areas Basedon Web of Science
This bibliometric review maps 15 years of research on water ecological risks in mining areas, analyzing nearly 2,000 papers to identify trends and hotspots. Research has shifted from studying single pollutants to examining multiple combined substances, with China producing the most publications in this field.
Detecting Chemical Contaminants in Water Using AI
This review examines how artificial intelligence and machine learning tools are being applied to detect chemical contaminants in water, including microplastics, covering sensor technologies, data processing approaches, and the potential for real-time monitoring systems.
Towards non-contact pollution monitoring in sewers with hyperspectral imaging
This laboratory proof-of-concept demonstrated that hyperspectral imaging combined with chemometric modeling enables fast, precise, and real-time measurement of wastewater pollution in sewers without physical contact. The approach could enable continuous automated sewer quality monitoring as an alternative to traditional sampling.
Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification
Researchers developed an urban water quality assessment method combining remote sensing reflectance optical classification with traditional water quality grading principles, enabling spatially and temporally continuous monitoring of urban water bodies.
Blockchain-Orchestrated Intelligent Water Treatment Plant Profiling Framework to Enhance Human Life Expectancy
Researchers proposed a blockchain-based framework for monitoring and managing water treatment plant operations to improve water quality. The system integrates Internet of Things sensors with machine learning to detect contaminants including microorganisms and heavy metals in real time. The study suggests that combining these technologies could help optimize water treatment processes and reduce health risks from contaminated water.
Water Quality Monitoring Using Physical Web Concept and Alert via Whatsapp Messenger
Not a microplastics paper — this engineering paper describes a low-cost IoT system that monitors water quality parameters (pH and total dissolved solids) in real time and sends alerts via WhatsApp when quality falls below safe thresholds.
Automated Targeted Sampling of Waterborne Pathogens and Microbial Source Tracking Markers Using Near-Real Time Monitoring of Microbiological Water Quality
Researchers developed an automated sampling system that collects water samples in real time based on microbiological monitoring data, improving the detection of short-term contamination events missed by routine testing. Such systems could also be adapted to monitor microplastic contamination events in waterways.