Skip to main content
SpringerLink
Log in

GIS Applications and Machine Learning Approaches in Civil Engineering

  • Conference paper
  • First Online:
Recent Advances in Civil Engineering for Sustainable Communities (IACESD 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 459))

  • 39 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Huang P, Sun Y (2023) Geographic information system-assisted optimal design of renewable-powered electric vehicle charging stations in high-density cities. In: Future urban energy system for buildings: the pathway towards flexibility, resilience and optimization. Springer, Singapore, pp 383–403

    Google Scholar 

  2. Tello-Cifuentes L, Marulanda J, Thomson P (2023) Detection and classification of pavement damages using wavelet scattering transform, fractal dimension by box-counting method and machine learning algorithms. Road Mater Pavement Des 1–19

    Google Scholar 

  3. Droj G, Droj L, Badea A-C, Dragomir PI (2023) GIS-based urban traffic assessment in a historical European city under the influence of infrastructure works and COVID-19. Appl Sci 13:1355

    Article  Google Scholar 

  4. Zhang Z, Song Y, Archer N, Wu P (2023) Spatial disparity of urban performance from a scaling perspective: a study of industrial features associated with economy, infrastructure, and innovation. GIScience Remote Sens 60:2167567

    Article  Google Scholar 

  5. Madhukumar M, Santhi MH (2022) Seismic data mapping of South India from 1820 to 2020 using geographic information system. In: Kolathayar S, Chian SC (eds) Recent advances in earthquake engineering. Springer, Singapore, pp 503–516

    Google Scholar 

  6. Rezaeisabzevar Y, Bazargan A, Zohourian B (2020) Landfill site selection using multi criteria decision making: influential factors for comparing locations. J Environ Sci 93:170–184

    Article  Google Scholar 

  7. Siddik MSM, Ahmed TE, Awad Ahmed FR, Mokhtar RA, Ali ES, Saeed RA (2023) Development of health digital GIS map for tuberculosis disease distribution analysis in Sudan. J. Healthc Eng 15(6):1216

    Google Scholar 

  8. Ramires PF, Dos Santos M, Paz-Montelongo S, Rubio-Armendáriz C, Adamatti D, Fiasconaro ML, da Silva Júnior FMR (2023) Multiple exposure pathways and health risk assessment of potentially harmful elements for children and adults living in a coal region in Brazil. Environ Geochem Health 45(2):305–318

    Google Scholar 

  9. Tan X, Guo C, Sun P (2023) Study on rationality of public fitness service facilities in Beijing based on GIS. Sustainability 15:1496

    Article  Google Scholar 

  10. Mengüç K, Aydin N, Ulu M (2023) Optimisation of COVID-19 vaccination process using GIS, machine learning, and the multi-layered transportation model. Int J Prod Res 1–14

    Google Scholar 

  11. Kozak KH, Graham CH, Wiens JJ (2008) Integrating GIS-based environmental data into evolutionary biology. Trends Ecol Evol 23:141–148

    Article  Google Scholar 

  12. Benning TL, LaPointe D, Atkinson CT, Vitousek PM (2002) Interactions of climate change with biological invasions and land use in the Hawaiian Islands: modeling the fate of endemic birds using a geographic information system. Proc Natl Acad Sci USA 99:14246–14249

    Article  Google Scholar 

  13. Guarino L, Jarvis A, Hijmans RJ, Maxted N (2002) Geographic information systems (GIS) and the conservation and use of plant genetic resources. In: Engels JMM, Ramanatha Rao V, Brown AHD, Jackson MT (eds) Managing plant genetic diversity. Proceedings of an international conference, Kuala Lumpur, Malaysia. CABI Publishing, UK, pp 387–404

    Google Scholar 

  14. Batzias FA, Sidiras DK, Spyrou EK (2005) Evaluating livestock manures for biogas production: a GIS based method. Renew Energy 30:1161–1176

    Article  Google Scholar 

  15. Tang L (2022) Geographic information system and climate big data applied in the research of bird migration. In: 2022 IEEE international conference on electrical engineering, big data and algorithms (EEBDA). IEEE, Changchun, China, pp 338–341

    Google Scholar 

  16. Huang P, Ma Z, Xiao L, Sun Y (2019) Geographic information system-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities. Appl Energy 255:113855

    Article  Google Scholar 

  17. Arashpour M, Golafshani EM, Parthiban R, Lamborn J, Kashani A, Li H, Farzanehfar P (2023) Predicting individual learning performance using machine-learning hybridized with the teaching-learning-based optimization. Comput Appl Eng 31:83–99

    Article  Google Scholar 

  18. Lagaros ND (2023) Artificial neural networks applied in civil engineering. Appl Sci 13:1131. https://doi.org/10.3390/app13021131

    Article  Google Scholar 

  19. Sapnken FE, Hamed MM, Soldo B, Gaston Tamba J (2023) Modeling energy-efficient building loads using machine-learning algorithms for the design phase. Energy Build 283:112807

    Article  Google Scholar 

  20. Ge D-M, Zhao L-C, Esmaeili-Falak M (2023) Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models. J Sustain Cement-Based Mater 12:542–560

    Article  Google Scholar 

  21. Wang T, Reiffsteck P, Chevalier C, Zhu Z, Chen CW, FranSchmidt F (2023) A novel extreme gradient boosting algorithm based model for predicting the scour risk around bridge piers: application to French railway bridges. Eur J Environ Civ Eng 27(3):1104–1122

    Article  Google Scholar 

  22. Nirbhav, Malik A, Maheshwar, Jan T, Prasad M (2023) Landslide susceptibility prediction based on decision tree and feature selection methods. J Indian Soc Remote Sens 51:771–786

    Google Scholar 

  23. Rani P, Sharma R (2023) Intelligent transportation system for internet of vehicles based vehicular networks for smart cities. Comput Electr Eng 105:108543

    Article  Google Scholar 

  24. Roozbahani A, Ebrahimi E, Banihabib ME (2018) A framework for ground water management based on Bayesian network and MCDM techniques. Water Resour Manage 32:4985–5005

    Article  Google Scholar 

  25. Drgoňa J, Arroyo J, Figueroa IC, Blum D, Arendt K, Kim D, Ollé EP, Oravec J, Wetter M, Vrabie DL, Helsen L (2020) All you need to know about model predictive control for buildings. Annu Rev Control 50:190–232

    Article  MathSciNet  Google Scholar 

  26. Rabbani A, Samui P, Kumari S (2023) Implementing ensemble learning models for the prediction of shear strength of soil. Asian J Civ Eng 1–17

    Google Scholar 

  27. Pachauri N, Ahn CW (2023) Weighted aggregated ensemble model for energy demand management of buildings. Energy 263:125853

    Article  Google Scholar 

  28. Hakam O, Baali A, Azennoud K, Lyazidi A, Bourchachen M (2023) Assessments of drought effects on plant production using satellite remote sensing technology, GIS and observed climate data in Northwest Morocco, case of the lower Sebou Basin. Int J Plant Prod 1–16

    Google Scholar 

  29. What is machine learning and how does it work? https://www.androidauthority.com/machine-learning-explained-3074635/. Last Accessed 14 May 2023

  30. Ray S (2019) A quick review of machine learning algorithms. In: 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon). IEEE, Faridabad, India, pp 35–39

    Google Scholar 

  31. Tohidi NB, Rustamov R (2020) A review of the machine learning in GIS for megacities application. In: Rustamov BR (ed) Geographic information systems in geospatial intelligence. IntechOpen

    Google Scholar 

  32. Yun J, Ryu KR, Ham S (2022) Spatial analysis leveraging machine learning and GIS of socio-geographic factors affecting cost overrun occurrence in roadway projects. Autom Constr 133:104007

    Article  Google Scholar 

  33. Devarakonda P, Sadasivuni R, Nobrega RAA, Wu J (2022) Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning. Multi Criteria Decis Anal 29:23–36

    Article  Google Scholar 

  34. Ekeanyanwu CV, Bose P, Beavers M, Yuan Y, Obisakin I (2022) Modeling and mapping flood hazard with a flood risk assessment tool: a case study of Austin. Texas. JGIS 14:332–346

    Article  Google Scholar 

  35. Arabameri A, Pal SC, Rezaie F, Nalivan OA, Chowdhuri I, Saha A, Lee S, Moayedi H (2021) Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. J Hydrol Reg Stud 36:100848

    Article  Google Scholar 

  36. Garosi Y, Sheklabadi M, Conoscenti C, Pourghasemi HR, Van Oost K (2019) Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Sci Total Environ 664:1117–1132

    Article  Google Scholar 

  37. Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hübl J (2019) Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. Sci Total Environ 653:801–814

    Article  Google Scholar 

  38. Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, Van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, Van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T (2022) Perspectives in machine learning for wildlife conservation. Nat Commun 13:792

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, U.V.C.E., Bangalore, Karnataka, India

    N. R. Asha Rani & M. Inayathulla

  2. Alliance University, Bengaluru, Karnataka, India

    Sasmita Bal

Authors
  1. N. R. Asha Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sasmita Bal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Inayathulla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sasmita Bal .

Editor information

Editors and Affiliations

  1. Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

    N. Vinod Chandra Menon

  2. Department of Civil Engineering, National Institute of Technology, Mangalore, Karnataka, India

    Sreevalsa Kolathayar

  3. Department of Civil Engineering, University of Aveiro, Aveiro, Portugal

    Hugo Rodrigues

  4. Department of Civil Engineering, Jyothy Institute of Technology, Bangalore, Karnataka, India

    K. S. Sreekeshava

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Asha Rani, N.R., Bal, S., Inayathulla, M. (2024). GIS Applications and Machine Learning Approaches in Civil Engineering. In: Menon, N.V.C., Kolathayar, S., Rodrigues, H., Sreekeshava, K.S. (eds) Recent Advances in Civil Engineering for Sustainable Communities. IACESD 2023. Lecture Notes in Civil Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-97-0072-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-0072-1_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0071-4

  • Online ISBN: 978-981-97-0072-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation