Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin
Author:
Mao Yimin1ORCID, Li Yican2, Teng Fei2, Sabonchi Arkan K. S.3ORCID, Azarafza Mohammad4ORCID, Zhang Maosheng5
Affiliation:
1. School of Information and Engineering, Shaoguan University, Shaoguan 512005, China 2. Xi’an Center of Mineral Resources Survey, China Geological Survey, Xi’an 710105, China 3. Department of Computer Techniques Engineering, Imam Ja’far Al-Sadiq University, Baghdad 10011, Iraq 4. Geotechnical Unit, Faculty of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran 5. School of Human Settlement Environment and Civil Engineering, Xi’an Jiaotong University, Xi’an 712000, China
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.
Funder
Key Improvement Projects of Guangdong Province Shaoguan Science and Technology Plan Projects Science and technology projects of education government in Jiangxi province
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
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