Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin

Yimin Mao; Yican Li; Fei Teng; Arkan K. S. Sabonchi; Mohammad Azarafza; Minghua Zhang

doi:10.3390/w16030380

journal article Open Access Jan 24, 2024

Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin

Yimin Mao

Yican Li Fei Teng

Arkan K. S. Sabonchi

Mohammad Azarafza

Minghua Zhang

Water Vol. 16 No. 3 pp. 380 · MDPI AG

View at Publisher Save 10.3390/w16030380

Abstract

The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake’s surface. Among these contributors, approximately half of the inflow is attributed to the Zarrineh River and the Simineh River. Remarkably, Lake Urmia lacks a natural outlet, with its water loss occurring solely through evaporation processes. This study employed a comprehensive methodology integrating ground surveys, remote sensing analyses, and meticulous documentation of historical landslides within the basin as primary information sources. Through this investigative approach, we preciselyidentified and geolocated a total of 512 historical landslide occurrences across the Urmia Lake drainage basin, leveraging GPS technology for precision. Thisarticle introduces a suite of hybrid machine learning predictive models, such as support-vector machine (SVM), random forest (RF), decision trees (DT), logistic regression (LR), fuzzy logic (FL), and the technique for order of preference by similarity to the ideal solution (TOPSIS). These models were strategically deployed to assess landslide susceptibility within the region. The outcomes of the landslide susceptibility assessment reveal that the main high susceptible zones for landslide occurrence are concentrated in the northwestern, northern, northeastern, and some southern and southeastern areas of the region. Moreover, when considering the implementation of predictions using different algorithms, it became evident that SVM exhibited superior performance regardingboth accuracy (0.89) and precision (0.89), followed by RF, with and accuracy of 0.83 and a precision of 0.83. However, it is noteworthy that TOPSIS yielded the lowest accuracy value among the algorithms assessed.

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Details

Published: Jan 24, 2024
Vol/Issue: 16(3)
Pages: 380
License: View

Authors

Y

Yimin Mao

School of Information and Engineering, Shaoguan University, Shaoguan 512005, China

Y

Yican Li

Xi’an Center of Mineral Resources Survey, China Geological Survey, Xi’an 710105, China

F

Fei Teng

Xi’an Center of Mineral Resources Survey, China Geological Survey, Xi’an 710105, China

A

Arkan K. S. Sabonchi

Department of Computer Techniques Engineering, Imam Ja’far Al-Sadiq University, Baghdad 10011, Iraq

M

Mohammad Azarafza

Geotechnical Unit, Faculty of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran

M

Minghua Zhang

School of Human Settlement Environment and Civil Engineering, Xi’an Jiaotong University, Xi’an 712000, China

Funding

Key Improvement Projects of Guangdong Province Award: 2022ZDJS048

Shaoguan Science and Technology Plan Projects Award: 2022ZDJS048

Science and technology projects of education government in Jiangxi province Award: 2022ZDJS048

Cite This Article

Yimin Mao, Yican Li, Fei Teng, et al. (2024). Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin. Water, 16(3), 380. https://doi.org/10.3390/w16030380

Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin

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