Improving Citizens’ Health in Underground Public Interior Spaces Through AI Powered Green Walls

Document Type : Original Article

Authors

1 October University for Modern Sciences and Arts

2 Faculty of Arts and Design, October University for Modern Sciences and Arts

3 GSE, Faculty of Engineering, October University for Modern Sciences and Arts, Giza, Egypt

Abstract

 In almost 600 BC, the Green Walls concept was presented. Most of the
previous research has proved that the use of green walls in different types of
interior spaces has its considerable merits in improving users’ overall health and
productivity. Interior Designers seem reluctant to integrate green walls in
underground spaces due to its drawbacks as one of the most expensive man-made
walls, needing scheduled water requirements, and its susceptibility to adversities
such as fungi growth.
The current study offers an intelligent solution predicting the performance
of self-sustainable green wall systems improving underground air quality. The
system is mitigating the issue of the green wall’s short life span. The
sustainability of green walls and air quality in underground interior spaces is
investigated by applying IoT and AI technologies.  Results of the present work show that different Random forests which an
example of an ensemble learner built on decision trees. The Decision trees are
extremely intuitive ways to classify or label objects models were generated
compared and evaluated for accuracy and sensitivity. These Models were built
simulating IOT-based-Air quality monitoring systems integrated with selfsustainable green walls. The Datasets selected included features like temperature
close to that of the targeted public interior spaces like underground stations in
Egypt. Predicting underground air quality has been conducted by indicating predefined parameters such as PM and CO. These results can evolve in the near
future, enabling decision-making systems to predict the performance of similar
self-sustainable multi-purpose green walls in maintaining underground space air
quality..

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MSA Engineering Journal
Volume 2, Issue 2 - Serial Number 6
Special issue for Artificial Intelligence & Sustainable Engineering Conference: A Path to The Future
March 2023
Pages 289-318

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