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Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems
Abstract
The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas- machine learning
- artificial neural networks
- random forests
- water network pollution
- sensor networks
- parallel computing
- heat stress
- animal welfare
- climate change
- automation
- smoke taint
- remote sensing
- near-infrared spectroscopy
- volatile phenols
- electronic nose
- wine sensory
- smart village
- smart agriculture
- climate-smart agriculture
- technology
- sustainability
- skin temperature
- artificial intelligence
- heart rate
- respiration rate
- GIS
- Markov chain
- land use
- urban information
- Tabriz City
- water distribution networks
- water network contamination
- random forest
- neural network
- urban tree management
- tree monitoring
- computer vision
- tree water stress index
- leaf area index
- volatile compounds
- photosynthesis modeling
- plant water status modeling
- n/a
- bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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Last time updated on 17/06/2022
This paper was published in Directory of Open Access Books (DOAB).
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