Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Nhu, Viet Ha; Shirzadi, Ataollah; Shahabi, Himan; Chen, Wei; Clague, John J.; Geertsema, Marten; Jaafari, Abolfazl; Avand, Mohammadtaghi; Miraki, Shaghayegh; Asl, Davood Talebpour; Pham, Binh Thai; Ahmad, Baharin Bin; Lee, Saro

doi:10.3390/f11040421

Cited by 95 publications

(52 citation statements)

References 130 publications

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“…Slope has the highest average merits and is deemed to be the most critical factor in determining landslide susceptibility in the Bijar study area. Landslides in this area are most common on the steep, relatively wet slopes with sparse vegetation [54,164].…”

Section: Discussionmentioning

confidence: 99%

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu

Shirzadi

Shahabi

et al. 2020

IJERPH

Self Cite

195

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Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

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Section: Discussionmentioning

confidence: 99%

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu

Shirzadi

Shahabi

et al. 2020

IJERPH

Self Cite

195

View full text Add to dashboard Cite

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“…The results were validated with the help of the receiver operating characteristic (ROC) curves [18,73,110]. The ROC curve is plotted based on a 1-specificity (x-axis) against sensitivity (y-axis) [111,112]. The area under the ROC curve (AUC) represents the ability to model whether the predetermined event will occur [113,114].…”

Section: Roc Curve and Aucmentioning

confidence: 99%

GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran

et al. 2020

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In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.

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“…2) Landslide causative factors: According to the previous research, the selection of the landslide causative factors should consider study area characteristics, scale of the analysis and the data availability [38,39]. Base on the this summarization, we use six geomorphic factors (altitude, slope, aspect, plan curvature, profile curvature and sediment transport index(STI)), a tectonic factor (distance to fault), a geologic factor (lithology), a triggering factor (rainfall), three hydrological factors (stream power index (SPI), topographic wetness index (TWI) and distance to river), four land-related factors (land use, normalized difference vegetation index (NDVI), distance to road and soil).…”

Section: B Data Preparation 1) Landslide Inventory Mapmentioning

confidence: 99%

Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping

Song

Wang

Fang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Ensemble learning methods have been widely used due to their remarkable generalized performance, but their potential in landslide spatial prediction application is not fully studied. To take full advantage of ensemble learning techniques, the classification and regression tree classifier and four treebased ensemble classifiers of random forest, extremely randomized tree, gradient boosting decision trees and extreme gradient boosting decision trees are used in this study for landslide susceptibility assessment. Specifically, a stacking ensemble learning framework coupled with embedded feature selection is presented, consisting of multiple tree-based classifiers mentioned previously as base learners and logistic regression as a meta-learner in a two-layer structure. In the study area of Yongxin, China, 364 historical landslide locations were first randomly partitioned into a ratio of 7/3 for training and testing the model. Then, a spatial database of sixteen landslide causative factors were constructed for landslide prediction. Meanwhile, the relative importance of these factors were quantified by using the total number of feature splits and the average Gini index during the training process, and a novel embedded feature selection method was used in the base learner of the proposed framework to further improve computational efficiency and predictive performance by allowing each base learner to obtain its own optimal sub-feature space. Finally, different methods were assessed by using several evaluation criteria. Experimental results demonstrated that the proposed ensemble learning framework had the highest area under the curve value of 0.864, and it is more effective than the conventional tree-based classifiers and other ensemble learning methods.

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Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Cited by 95 publications

References 130 publications

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran

Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping

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