kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping
Univ Tabriz, Dept Civil Engn, Tabriz 5166616471, Iran..
KN Toosi Univ Technol, Fac Civil Engn, Tehran 1996715433, Iran..
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Water and Environmental Engineering.
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Water and Environmental Engineering.ORCID iD: 0000-0002-7978-0040
2024 (English)In: Land, E-ISSN 2073-445X, Vol. 13, no 3, article id 322Article in journal (Refereed) Published
Abstract [en]

Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate.

Place, publisher, year, edition, pages
MDPI AG , 2024. Vol. 13, no 3, article id 322
Keywords [en]
land subsidence modeling, classification, machine learning algorithms, Semnan plain, Kashmar Plain
National Category
Geotechnical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-345031DOI: 10.3390/land13030322ISI: 001192505300001Scopus ID: 2-s2.0-85189172630OAI: oai:DiVA.org:kth-345031DiVA, id: diva2:1849548
Note

QC 20240408

Available from: 2024-04-08 Created: 2024-04-08 Last updated: 2024-04-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Behboudian, MassoudKalantari, Zahra

Search in DiVA

By author/editor
Behboudian, MassoudKalantari, Zahra
By organisation
Water and Environmental Engineering
In the same journal
Land
Geotechnical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 30 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf