AIMC Topic: Environmental Monitoring

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Water level estimation in sewage pipes using texture-based methods and machine learning algorithms.

Water science and technology : a journal of the International Association on Water Pollution Research Mar 10, 2025
Water companies use closed-circuit television (CCTV) to inspect the condition of sewage pipes. The reports generated by surveyors help companies to plan for the maintenance and rehabilitation of sewage pipes. A surveyor needs to record the water leve...
Sewage Machine Learning Water Water Supply Environmental Monitoring Algorithms
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PM concentration prediction using machine learning algorithms: an approach to virtual monitoring stations.

Scientific reports Mar 8, 2025
One of the most important pollutants is PM, which is particularly important to monitor pollutant levels to keep the pollutant concentration under control. In this research, an attempt has been made to predict the concentrations of PM using four Machi...
SARS-CoV-2 Humans Environmental Monitoring Machine Learning COVID-19 Air Pollutants Algorithms Air Pollution Particulate Matter
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Leveraging ML to predict climate change impact on rice crop disease in Eastern India.

Environmental monitoring and assessment Mar 8, 2025
Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical factors for...
Plant Diseases Climate Change Crops, Agricultural Environmental Monitoring Oryza India Agriculture Machine Learning
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Predicting surface soil pH spatial distribution based on three machine learning methods: a case study of Heilongjiang Province.

Environmental monitoring and assessment Mar 8, 2025
Comprehensive and accurate acquisition of surface soil pH spatial distribution information is essential for monitoring soil degradation and providing scientific guidance for agricultural practices. This study focused on Heilongjiang Province in China...
Models, Chemical Hydrogen-Ion Concentration Environmental Monitoring Support Vector Machine Machine Learning Soil China
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Explainable AI analysis for smog rating prediction.

Scientific reports Mar 8, 2025
Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, and ammonia from fertilizers, vehicles play a si...
Environmental Monitoring Vehicle Emissions Humans Machine Learning
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Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata.

The Science of the total environment Mar 7, 2025
The island of New Guinea harbors some of the world's most biologically diverse and highly endemic tropical ecosystems. Nevertheless, progressing land-use change in the region threatens their integrity, which will adversely affect their biodiversity a...
Machine Learning New Guinea Papua New Guinea Forecasting Ecosystem Forests Environmental Monitoring Conservation of Natural Resources Carbon
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Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis.

Environmental pollution (Barking, Essex : 1987) Mar 6, 2025
Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, tra...
Models, Chemical Machine Learning China Air Pollution Ozone Environmental Monitoring Air Pollutants
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The role of reservoir size in driving methane emissions in China.

Water research Mar 5, 2025
Reservoirs play a crucial role as sources of methane (CHâ‚„) emissions, with emission rates and quantities varying widely depending on reservoir size due to factors such as surface area, water depth, usage, operational methods, and spatial distribution...
Environmental Monitoring Air Pollutants Methane China
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Advanced deep learning models for predicting elemental concentrations in iron ore mine using XRF data: a cost-effective alternative to ICP-MS methods.

Environmental geochemistry and health Mar 5, 2025
Accurate elemental analysis is a critical requirement for mineral exploration, particularly in regions like Iran, where the mining sector has experienced a substantial increase in exploration activities over the past decade. Inductively Coupled Plasm...
Environmental Monitoring Cost-Benefit Analysis Mass Spectrometry Mining Deep Learning Iron Spectrometry, X-Ray Emission Iran
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Prediction of chlorination degradation rate of emerging contaminants based on machine learning models.

Environmental pollution (Barking, Essex : 1987) Mar 4, 2025
Assessing the degradation of emerging contaminants in water through chlorination is crucial for regulatory monitoring of these contaminants. In this study, we developed a machine learning model to predict the apparent second-order reaction rate const...
Machine Learning Chlorine Water Pollutants, Chemical Halogenation Models, Chemical Water Purification Environmental Monitoring
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