Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area

Ghasemian, BaharehDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran (author)

Validerad;2022;Nivå 2;2022-06-15 (joosat);Funder: University of Kurdistan, Iran (00-9-34027-4469)
Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world.

NATURVETENSKAP Geovetenskap och miljövetenskap Naturgeografi hsv//swe
NATURAL SCIENCES Earth and Related Environmental Sciences Physical Geography hsv//eng
landslide susceptibility
spatial modeling
rotation forest
random forest
decision tree
GIS
Iran
Soil Mechanics
Geoteknik

Shahabi, HimanDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran (author)
Shirzadi, AtaollahDepartment of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran (author)
Al-Ansari, Nadhir,1947-Luleå tekniska universitet,Geoteknologi(Swepub:ltu)nadhir (author)
Jaafari, AbolfazlResearch Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran (author)
Geertsema, MartenResearch Geomorphologies, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Prince George, BC, Canada (author)
Melesse, Assefa M.Department of Earth and Environment, Institute of Environment, Florida International University, Miami, FL, United States (author)
Singh, Sushant K.The Center for Artificial Intelligence and Environmental Sustainability (CAIES) Foundation, Bihar, India (author)
Ahmad, AnuarDepartment of Geoinformation, Faculty of Built Environment and Surveying, University Technology Malaysia (UTM), Johor Bahru, Malaysia (author)
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, IranDepartment of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran (creator_code:org_t)

By the author/editor: Ghasemian, Bahar ...; Shahabi, Himan; Shirzadi, Ataoll ...; Al-Ansari, Nadhi ...; Jaafari, Abolfaz ...; Geertsema, Marte ...; show more...; Melesse, Assefa ...; Singh, Sushant K ...; Ahmad, Anuar; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Earth and Relate ...; and Physical Geograp ...

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