A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
GIS Applications and Machine Learning Approaches in Civil Engineering
https://orcid.org/http://orcid.org/0000-0003-4415-5998
https://orcid.org/http://orcid.org/0000-0003-0089-4385
The positions of earth observations or features, along with the properties that go with them and the spatial relationships that exist between them, are displayed using GIS (Geographic Information Systems) data. GIS statistical analysis ranges greatly and includes modeling and projections, these are typically highly computational and sophisticated, particularly whenever huge datasets must be handled. Due to its considerable quickness, precision, automation, and consistency, approaches like machine learning (ML) have been proposed as an imminent revolution in the evaluation of GIS data as computing technologies develop. The flexibility in transferring data from a particular database to a different one is possibly the most significant advantage when utilizing combined GIS and ML. The present study provides an overview of the ML models and their applications in infrastructure/urban development, health, flood prediction, groundwater detection and contamination, erosion modeling and prediction, landslide susceptibility prediction (LSP), LULCC modeling, managing forests and their resources, and biodiversity conservation using GIS tools. In addition to this, the study highlights several limitations associated with deploying different ML models in conjunction with GIS.
GIS Applications and Machine Learning Approaches in Civil Engineering
https://orcid.org/http://orcid.org/0000-0003-4415-5998
https://orcid.org/http://orcid.org/0000-0003-0089-4385
The positions of earth observations or features, along with the properties that go with them and the spatial relationships that exist between them, are displayed using GIS (Geographic Information Systems) data. GIS statistical analysis ranges greatly and includes modeling and projections, these are typically highly computational and sophisticated, particularly whenever huge datasets must be handled. Due to its considerable quickness, precision, automation, and consistency, approaches like machine learning (ML) have been proposed as an imminent revolution in the evaluation of GIS data as computing technologies develop. The flexibility in transferring data from a particular database to a different one is possibly the most significant advantage when utilizing combined GIS and ML. The present study provides an overview of the ML models and their applications in infrastructure/urban development, health, flood prediction, groundwater detection and contamination, erosion modeling and prediction, landslide susceptibility prediction (LSP), LULCC modeling, managing forests and their resources, and biodiversity conservation using GIS tools. In addition to this, the study highlights several limitations associated with deploying different ML models in conjunction with GIS.
GIS Applications and Machine Learning Approaches in Civil Engineering
https://orcid.org/http://orcid.org/0000-0003-4415-5998
https://orcid.org/http://orcid.org/0000-0003-0089-4385
The positions of earth observations or features, along with the properties that go with them and the spatial relationships that exist between them, are displayed using GIS (Geographic Information Systems) data. GIS statistical analysis ranges greatly and includes modeling and projections, these are typically highly computational and sophisticated, particularly whenever huge datasets must be handled. Due to its considerable quickness, precision, automation, and consistency, approaches like machine learning (ML) have been proposed as an imminent revolution in the evaluation of GIS data as computing technologies develop. The flexibility in transferring data from a particular database to a different one is possibly the most significant advantage when utilizing combined GIS and ML. The present study provides an overview of the ML models and their applications in infrastructure/urban development, health, flood prediction, groundwater detection and contamination, erosion modeling and prediction, landslide susceptibility prediction (LSP), LULCC modeling, managing forests and their resources, and biodiversity conservation using GIS tools. In addition to this, the study highlights several limitations associated with deploying different ML models in conjunction with GIS.
GIS Applications and Machine Learning Approaches in Civil Engineering
Lecture Notes in Civil Engineering
Menon, N. Vinod Chandra (editor) / Kolathayar, Sreevalsa (editor) / Rodrigues, Hugo (editor) / Sreekeshava, K. S. (editor) / Asha Rani, N. R. (author) / Bal, Sasmita (author) / Inayathulla, M. (author)
International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development ; 2023
Recent Advances in Civil Engineering for Sustainable Communities ; Chapter: 14 ; 157-166
2024-03-26
10 pages
Article/Chapter (Book)
Electronic Resource
English
Machine learning applications in civil engineering
| TIBKAT | 2024
A primer on machine learning applications in civil engineering
| TIBKAT | 2020
Machine Learning Techniques for Civil Engineering Problems
| Wiley | 1997
Machine Learning Techniques for Civil Engineering Problems
| Online Contents | 1997