AI Medical Compendium Journal:
Environmental pollution (Barking, Essex : 1987)

Showing 71 to 80 of 106 articles

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach.

Environmental pollution (Barking, Essex : 1987)
Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approa...

A hybrid deep learning model for regional O and NO concentrations prediction based on spatiotemporal dependencies in air quality monitoring network.

Environmental pollution (Barking, Essex : 1987)
Short-term prediction of urban air quality is critical to pollution management and public health. However, existing studies have failed to make full use of the spatiotemporal correlations or topological relationships among air quality monitoring netw...

Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction.

Environmental pollution (Barking, Essex : 1987)
Mobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural netwo...

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...

Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter.

Environmental pollution (Barking, Essex : 1987)
From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutiona...

Critical features identification for chemical chronic toxicity based on mechanistic forecast models.

Environmental pollution (Barking, Essex : 1987)
Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cau...

Formulating Convolutional Neural Network for mapping total aquifer vulnerability to pollution.

Environmental pollution (Barking, Essex : 1987)
Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based o...

Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network.

Environmental pollution (Barking, Essex : 1987)
Water quality forecasting can provide useful information for public health protection and support water resources management. In order to forecast water quality more accurately, this paper proposes a novel hybrid model by combining data decomposition...

Pollutant specific optimal deep learning and statistical model building for air quality forecasting.

Environmental pollution (Barking, Essex : 1987)
Poor air quality is becoming a critical environmental concern in different countries over the last several years. Most of the air pollutants have serious consequences on human health and wellbeing. In this context, efficient forecasting of air pollut...

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms.

Environmental pollution (Barking, Essex : 1987)
The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geog...