AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China
Abstract
:1. Introduction
2. Study Area
3. Methods
3.1. Landslide Inventory and Mapping
3.2. Landslide Susceptibility Evaluation Based on Machine Learning
3.2.1. Selection of Factors Influencing Landslides
Parameters Related to Geomorphological Conditions
Parameters Related to Landslide Materials and Geological Conditions
3.2.2. Parameter Preprocessing
Parameters Related to Landslide Materials and Geological Conditions
Resampling
Data Standardization
3.2.3. Candidate Machine Selection
Ensemble Methods
Generalized Linear Models (GLMs)
Nearest Neighbors
Support Vector Machines (SVM)
Trees
Discriminant Analysis
eXtreme Gradient Boosting (XGBoost)
3.2.4. Model Fitting and Tuning
4. Results
4.1. Landslide Inventory and Mapping
4.2. Landslide Susceptibility Mapping
5. Discussion
6. Conclusions
- (1)
- The 21 initial models were trained with the training data in the cross-validation dataset, and the models were then sorted according to the average accuracy score (ACC). The results showed that the overall fitting effect of the comprehensive model was better than for the other models. The ExtraTrees model had the highest score, with an average test data accuracy of 0.91, and the average AUC after 10-times cross validation was 0.97. This model can be effectively used for the susceptibility evaluation of shallow landslides.
- (2)
- Among all of the selected evaluation factors, slope aspect made a larger contribution to landslide development than the other factors. For 94° < SA < 246°, LSs accounted for the highest proportion, which indicates that sunlit slopes are significantly more prone to landslides than shaded slopes, followed by PLC, DR, DTF, NDVI, and DTR. Geomorphic conditions are the most important factors in triggering landslides induced by heavy rainfall, followed by fluvial erosion and fault distribution, while human activities have only a small influence.
- (3)
- In the evaluation of landslide susceptibility based on machine learning, the prediction performance of various models is significantly different. Extensive comparative prediction in different environments, closely linking the model evaluation with the goals of the study and increasing the understanding of the ability and limitations of the model are the key to model selection in the future, so as to strengthen the application of artificial intelligence technology in the field of geological disaster prevention and improve the prediction accuracy and efficiency.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Field | Parameter | Units | ||
---|---|---|---|---|
1 | ID | Identification field | / | |
2 | LO | Landslide occurrence or not | / | |
3 | Parameters related to geomorphological conditions | AS | Average slope | ° |
4 | SA | Slope aspect | ° | |
5 | LR | Local relief | km | |
6 | PRC | Profile curvature | / | |
7 | PLC | Planar curvature | / | |
8 | SUA | Slope unit area | km2 | |
9 | E | Elevation | km | |
10 | TWI | Topographic wetness index | / | |
11 | WA | Watershed area | km2 | |
12 | Parameter related to material and geology conditions | NDVI | Normalized Difference Vegetation Index | / |
13 | FLI | Formation lithological index | / | |
14 | DTF | Distance to fault | km | |
15 | ST | Soil type | / | |
16 | SC | Sand content | % | |
17 | SG | Gravel content | % | |
18 | SIC | Silt content | % | |
19 | CC | Clay content | % | |
20 | SB | Soil bulk | N/m3 | |
21 | DR | Distance to river | km | |
22 | Parameter related to engineering activities | DTR | Distance to road | km |
Predicted Label | |||
---|---|---|---|
Positive | Negative | ||
True label | Positive | True Positive (TP) | False Negative (FP) |
Negative | False Positive (FP) | True Negative (TN) |
Classifier Algorithm | Best Parameter | Runtime (s) | |
---|---|---|---|
1 | ExtraTreesClassifier | ‘n_estimators’ = 500 | 50,615.69 |
‘random_state’ = 0 | |||
‘criterioin’ = gini | |||
2 | RandomForestClassifier | ‘criterioin’ = ‘entropy’ | 329,402.72 |
‘max_depth’ = 54 | |||
‘n_estimators’=500 | |||
‘oob_score’ = True | |||
3 | BaggingClassifier | ‘max_samples’ = 1.0 | 35,085.42 |
‘n_estimators’ = 500 | |||
4 | KNeighborsClassifer | ‘algorithm’ = auto | 41,816.73 |
‘n_neighbors’ = 8 | |||
‘weithts’ = ‘distance’ |
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Qi, T.; Zhao, Y.; Meng, X.; Chen, G.; Dijkstra, T. AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China. Remote Sens. 2021, 13, 1819. https://doi.org/10.3390/rs13091819
Qi T, Zhao Y, Meng X, Chen G, Dijkstra T. AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China. Remote Sensing. 2021; 13(9):1819. https://doi.org/10.3390/rs13091819
Chicago/Turabian StyleQi, Tianjun, Yan Zhao, Xingmin Meng, Guan Chen, and Tom Dijkstra. 2021. "AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China" Remote Sensing 13, no. 9: 1819. https://doi.org/10.3390/rs13091819