AIMC Topic: Environmental Monitoring

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Air Quality Index prediction using an effective hybrid deep learning model.

Environmental pollution (Barking, Essex : 1987)
Environmentalism has become an intrinsic part of everyday life. One of the greatest challenge to the environment's long-term existence is the air pollution. Delhi, the capital of India, has experienced decreasing of air quality for several years. The...

Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model.

Journal of environmental sciences (China)
Recently, the global background concentration of ozone (O) has demonstrated a rising trend. Among various methods, groun-based monitoring of O concentrations is highly reliable for research analysis. To obtain information on the spatial characteristi...

Design of Service Robot Based on User Emotion Recognition and Environmental Monitoring.

Journal of environmental and public health
Robots may be able to comprehend human emotions better by adding speech emotion recognition and environment monitoring functions to human-computer interaction systems. Robots can offer more humanized services by adapting to human emotions, resulting ...

Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Songkhla River Basin, Thailand.

Environmental science and pollution research international
Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall...

Tunnel collapse risk assessment based on improved quantitative theory III and EW-AHP coupling weight.

Scientific reports
It is a multi-criteria decision issue to conduct a risk assessment of the tunnel. In this paper, a tunnel collapse risk assessment model based on the improved theory of quantification III and the fuzzy comprehensive evaluation method is proposed. Acc...

Vegetation detection using vegetation indices algorithm supported by statistical machine learning.

Environmental monitoring and assessment
In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing-based applications, vegetation detection, in...

Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model.

Computational intelligence and neuroscience
Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are ...

Machine learning and deep learning modeling and simulation for predicting PM2.5 concentrations.

Chemosphere
Particulate matter (PM) pollution greatly endanger human physical and mental health, and it is of great practical significance to predict PM concentrations accurately. This study measured one-year monitoring data of six main meteorological parameters...

Extraction of multi-scale features enhances the deep learning-based daily PM forecasting in cities.

Chemosphere
Characterising the daily PM2.5 concentration is crucial for air quality control. To govern the status of the atmospheric environment, a novel hybrid model for PM2.5 forecasting was proposed by introducing a two-stage decomposition technology of compl...

Deploying deep learning to estimate the abundance of marine debris from video footage.

Marine pollution bulletin
The insatiable desire of society for plastic goods has led to synthetic materials becoming omnipresent in the marine environment. In attempting to address the problem of plastic pollution, we propose an image classifier based on the YOLOv5 deep learn...