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Environmental Monitoring

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A hybrid vine copula-fuzzy model for groundwater level simulation under uncertainty.

Environmental monitoring and assessment
Accurate simulation of groundwater level is crucial for the sustainable management of water resources. However, the numerous uncertainties in input data, simulation model parameters, and physical processes, as well as the dependency between system va...

Automatic pre-screening of outdoor airborne microplastics in micrographs using deep learning.

Environmental pollution (Barking, Essex : 1987)
Airborne microplastics (AMPs) are prevalent in both indoor and outdoor environments, posing potential health risks to humans. Automating the process of identifying potential particles in micrographs can significantly enhance the research and monitori...

Integrated machine learning based groundwater quality prediction through groundwater quality index for drinking purposes in a semi-arid river basin of south India.

Environmental geochemistry and health
The main objective of this study is to predict and monitor groundwater quality through the use of modern Machine Learning (ML) techniques. By employing ML techniques, the research effectively evaluates groundwater quality to forecast its future trend...

AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers.

Environment international
The vast amount of registered chemicals leads to a high diversity of substances occurring in the environment and the creation of new substances outpaces chemical risk assessment as well as monitoring strategies. Hence, risk assessment strategies need...

Integrating partial least square structural equation modelling and machine learning for causal exploration of environmental phenomena.

Environmental research
Understanding the causes of environmental phenomena is crucial for promoting positive outcomes and mitigating negative ones. Partial least squares structural equation modelling (PLS-SEM) is becoming a valuable tool for evaluating causal relationships...

Artificial Intelligence in Gas Sensing: A Review.

ACS sensors
The role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in enhancing and automating gas sensing methods and the implications of these technologies for emergent gas sensor systems is reviewed. Applications of AI-based i...

Enhancing short-term algal bloom forecasting through an anti-mimicking hybrid deep learning method.

Journal of environmental management
Accurately predicting algal blooms remains a critical challenge due to their dynamic and non-stationary nature, compounded by high-frequency fluctuations and noise in monitoring data. Additionally, a common issue in time-series forecasting is data re...

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
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...

PM concentration prediction using machine learning algorithms: an approach to virtual monitoring stations.

Scientific reports
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...

Leveraging ML to predict climate change impact on rice crop disease in Eastern India.

Environmental monitoring and assessment
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...