A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Application of Artificial Intelligence, Machine Learning, and Deep Learning in Contaminated Site Remediation
https://orcid.org/http://orcid.org/0000-0002-8509-0820
https://orcid.org/http://orcid.org/0000-0002-6577-1151
Soil and groundwater contamination is caused by improper waste disposal practices and the accidental spills, posing threat to public health and the environment. It is imperative to assess and remediate these contaminated sites to protect public health and the environment as well as to assure sustainable development. Site remediation is inherently complex due to the many variables involved, such as contamination chemistry, fate and transport, geology, and hydrogeology. The selection of remediation method also depends on the contaminant type and distribution and subsurface soil and groundwater conditions. Depending on the type of remediation method, many system and operating variables can affect the remedial efficiency. The design and implementation of site remediation can be expensive, time-consuming and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have potential to make the site remediation cost-effective with reduced human effort. This study provides a brief overview of these emerging technologies and presents case studies demonstrating how these technologies can help contaminated site remediation decisions.
Application of Artificial Intelligence, Machine Learning, and Deep Learning in Contaminated Site Remediation
https://orcid.org/http://orcid.org/0000-0002-8509-0820
https://orcid.org/http://orcid.org/0000-0002-6577-1151
Soil and groundwater contamination is caused by improper waste disposal practices and the accidental spills, posing threat to public health and the environment. It is imperative to assess and remediate these contaminated sites to protect public health and the environment as well as to assure sustainable development. Site remediation is inherently complex due to the many variables involved, such as contamination chemistry, fate and transport, geology, and hydrogeology. The selection of remediation method also depends on the contaminant type and distribution and subsurface soil and groundwater conditions. Depending on the type of remediation method, many system and operating variables can affect the remedial efficiency. The design and implementation of site remediation can be expensive, time-consuming and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have potential to make the site remediation cost-effective with reduced human effort. This study provides a brief overview of these emerging technologies and presents case studies demonstrating how these technologies can help contaminated site remediation decisions.
Application of Artificial Intelligence, Machine Learning, and Deep Learning in Contaminated Site Remediation
https://orcid.org/http://orcid.org/0000-0002-8509-0820
https://orcid.org/http://orcid.org/0000-0002-6577-1151
Soil and groundwater contamination is caused by improper waste disposal practices and the accidental spills, posing threat to public health and the environment. It is imperative to assess and remediate these contaminated sites to protect public health and the environment as well as to assure sustainable development. Site remediation is inherently complex due to the many variables involved, such as contamination chemistry, fate and transport, geology, and hydrogeology. The selection of remediation method also depends on the contaminant type and distribution and subsurface soil and groundwater conditions. Depending on the type of remediation method, many system and operating variables can affect the remedial efficiency. The design and implementation of site remediation can be expensive, time-consuming and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have potential to make the site remediation cost-effective with reduced human effort. This study provides a brief overview of these emerging technologies and presents case studies demonstrating how these technologies can help contaminated site remediation decisions.
Application of Artificial Intelligence, Machine Learning, and Deep Learning in Contaminated Site Remediation
Lecture Notes in Civil Engineering
Shukla, Sanjay Kumar (editor) / Raman, Sudharshan N. (editor) / Bhattacharjee, B. (editor) / Singh, Priyanka (editor) / Raviteja, K. V. N. S. (author) / Reddy, Krishna R. (author)
International Conference on Trends and Recent Advances in Civil Engineering ; 2022 ; Noida, India
2023-06-22
15 pages
Article/Chapter (Book)
Electronic Resource
English
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