Reprint

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

Edited by
January 2022
206 pages
  • ISBN978-3-0365-2904-2 (Hardback)
  • ISBN978-3-0365-2905-9 (PDF)

This book is a reprint of the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary

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.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
machine learning; artificial neural networks; random forests; water network pollution; sensor networks; parallel computing; machine learning; heat stress; animal welfare; climate change; automation; smoke taint; remote sensing; climate change; near-infrared spectroscopy; volatile phenols; climate change; machine learning; electronic nose; smoke taint; wine sensory; smart village; smart agriculture; climate-smart agriculture; technology; sustainability; animal welfare; skin temperature; artificial intelligence; heart rate; respiration rate; remote sensing; GIS; Markov chain; land use; urban information; Tabriz City; water distribution networks; water network contamination; machine learning; random forest; neural network; urban tree management; tree monitoring; computer vision; tree water stress index; leaf area index; remote sensing; volatile compounds; artificial neural networks; photosynthesis modeling; plant water status modeling; n/a