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Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
Detecting hazardous substances in the environment is crucial for protecting human
wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) …
wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) …
Deep learning for IoT big data and streaming analytics: A survey
In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …
and/or generate various sensory data over time for a wide range of fields and applications …
A review of artificial neural network models for ambient air pollution prediction
Research activity in the field of air pollution forecasting using artificial neural networks
(ANNs) has increased dramatically in recent years. However, the development of ANN …
(ANNs) has increased dramatically in recent years. However, the development of ANN …
A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities
In modern society, air pollution is an important topic as this pollution exerts a critically bad
influence on human health and the environment. Among air pollutants, Particulate Matter …
influence on human health and the environment. Among air pollutants, Particulate Matter …
Deep air quality forecasting using hybrid deep learning framework
Air quality forecasting has been regarded as the key problem of air pollution early warning
and control management. In this article, we propose a novel deep learning model for air …
and control management. In this article, we propose a novel deep learning model for air …
Long short-term memory-Fully connected (LSTM-FC) neural network for PM2. 5 concentration prediction
People have been suffering from air pollution for a decade in China, especially from PM 2.5
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …
Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation
Air pollutant concentration forecasting is an effective method of protecting public health by
providing an early warning against harmful air pollutants. However, existing methods of air …
providing an early warning against harmful air pollutants. However, existing methods of air …
Spatio-temporal air quality analysis and PM2. 5 prediction over Hyderabad City, India using artificial intelligence techniques
Air pollution is one of the most serious environmental issues faced by humans, and it affects
the quality of life in cities. PM 2.5 forecasting models can be used to create strategies for …
the quality of life in cities. PM 2.5 forecasting models can be used to create strategies for …
Urban flow prediction from spatiotemporal data using machine learning: A survey
Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …
public safety. This prediction task is affected by several complex and dynamic factors, such …
Comparative analysis of machine learning techniques for predicting air quality in smart cities
Dealing with air pollution presents a major environmental challenge in smart city
environments. Real-time monitoring of pollution data enables local authorities to analyze the …
environments. Real-time monitoring of pollution data enables local authorities to analyze the …