Improving the performance of artificial intelligence models using the rotation forest technique for landslide susceptibility mapping

Shen, Hong‐Bin; Huang, Faming; Fan, Xinyue; Shahabi, Himan; Shirzadi, Ataollah; Wang, D.; Peng, Chaohong; Zhao, Xiao-Ling; Chen, W.

doi:10.1007/s13762-022-04665-z

Cited by 4 publications

(3 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Landslide inventories are required for model training and validation in landslide susceptibility assessments. Inventories of landslides can be established from field survey data, news and government report detailing previous landslide events, and remote sensing data analysis [48]. Kavzoglu et al [49], Kilicoglu, [50], and Akinci et al [51] identified the locations of prior landslides using GNSS-based field surveys, high-resolution satellite imagery, Google Earth imagery, previous projects and reports, atlases, and other sources.…”

Section: Landslide Inventory Map (Lim)mentioning

confidence: 99%

Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran

Tavakolifar

Shahabi

Alizadeh

et al. 2023

Land

View full text Add to dashboard Cite

Landslides along the main roads in the mountains cause fatalities, ecosystem damage, and land degradation. This study mapped the susceptibility to landslides along the Saqqez-Marivan main road located in Kurdistan province, Iran, comparing an ensemble fuzzy logic with analytic network process (fuzzy logic-ANP; FLANP) and TOPSIS (fuzzy logic-TOPSIS; FLTOPSIS) in terms of their prediction capacity. First, 100 landslides identified through field surveys were randomly allocated to a 70% dataset and a 30% dataset, respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were considered. The performance of the methods was evaluated by inspecting the areas under the receiver operating curve (AUCROC). The prediction accuracies were 0.983 and 0.938, respectively, for the FLTOPSIS and FLANP methods. Our findings demonstrate that although both models are known to be promising, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the study area. Therefore, the susceptibility map developed through the FLTOPSIS method is suitable to inform management and planning of areas prone to landslides for land allocation and development purposes, especially in mountainous areas.

show abstract

Section: Landslide Inventory Map (Lim)mentioning

confidence: 99%

Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran

Tavakolifar

Shahabi

Alizadeh

et al. 2023

Land

View full text Add to dashboard Cite

show abstract

“…Landslide inventories are required for model training and validation in landslide susceptibility assessments. Inventories of landslides can be gathered using field surveys, news and government report detailing previous landslide events, and remote sensing data analysis [43]. Kavzoglu et al, 2014;Kilicoglu, 2021 [44,45] and Akinci et al [46]used GNSS-based field surveys, high-resolution satellite images, Google Earth images, previous projects, and reports, atlases, and other sources to find the location of previous landslides.…”

Section: Landslide Inventory Map (Lim)mentioning

confidence: 99%

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Shahabi¹,

Tavakolifar²,

Alizadeh³

et al. 2023

Preprint

View full text Add to dashboard Cite

Landslides around the main roads in the mountains not only cause fatal events but also cause ecosystem damage, including land degradation. This study aims to map the susceptibility of the landslides around the Saqqez-Marivan main rod of Kurdistan province, Iran, using ensemble Fuzzy logic with Analytic Network Process (Fuzzy Logic-ANP; FLANP), and with TOPSIS (Fuzzy Logic-TOPSIS; FLTOPSIS). A total of 100 landslides were first recognized by field surveys and then they were randomly divided into a 70% dataset (70 locations) and a 30% dataset (30 locations), respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were used. The performance of the methods was checked by the areas under the receiver operating curve (AUCROC). Results concluded that the prediction accuracy based on validating datasets were, respectively, 0.882 and 0.918 for FLANP and FLTOPSIS methods. Our findings demonstrated that although both models were known as promising techniques, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the studied area. Therefore, the susceptibility map developed by the FLTOPSIS method can be used for the proper management of areas with high landslide potential and also for managers and planners during the implementation of land allocation and development projects, especially in mountainous areas.

show abstract

“…In this regard, along with compulsory information, the utilization of cutting-edge mathematical models also remains pivotal to obtaining precise results. The prediction of landslide risk entails the application of different models, for instance, weight of evidence (WOE) [11], frequency ratio (FR) [12], the analytic hierarchy process (AHP) [13], and fuzzy logic (FL) [14], which are among the most elementary and widely used approaches. Contemporarily, these approaches are considered conventional and are being replaced by machine learning (ML) models.…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Ali,

Chen,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

show abstract

Improving the performance of artificial intelligence models using the rotation forest technique for landslide susceptibility mapping

Cited by 4 publications

References 90 publications

Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran

Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Contact Info

Product

Resources

About