scholarly journals Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling

2019 ◽  
Vol 11 (20) ◽  
pp. 5659 ◽  
Author(s):  
Soyoung Park ◽  
Se-Yeong Hamm ◽  
Jinsoo Kim
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Landslide Susceptibility ◽  
Mitigation Strategies ◽  
Slope Aspect ◽  
Kappa Index ◽  
Topographic Wetness Index ◽  
Slope Length ◽  
Rotation Forest ◽  
Conditioning Factors

This study analyzed and compared landslide susceptibility models using decision tree (DT), random forest (RF), and rotation forest (RoF) algorithms at Woomyeon Mountain, South Korea. Out of a total of 145 landslide locations, 102 locations (70%) were used for model training, and the remaining 43 locations (30%) were used for validation. Fourteen landslide conditioning factors were identified, and the contributions of each factor were evaluated using the RRelief-F algorithm with a 10-fold cross-validation approach. Three factors, timber diameter, age, and density had no contribution to landslide occurrence. Landslide susceptibility maps (LSMs) were produced using DT, RF, and RoF models with the 11 remaining landslide conditioning factors: altitude, slope, aspect, profile curvature, plan curvature, topographic position index, elevation-relief ratio, slope length and slope steepness, topographic wetness index, stream power index, and timber type. The performances of the LSMs were assessed and compared based on sensitivity, specificity, precision, accuracy, kappa index, and receiver operating characteristic curves. The results showed that the ensemble learning methods outperformed the single classifier (DT) and that the RoF model had the highest prediction capability compared to the DT and RF models. The results of this study may be helpful in managing areas vulnerable to landslides and establishing mitigation strategies.

scholarly journals Comparison of rotation forest based intelligence models for landslide susceptibility modeling

2021 ◽  
Author(s):  
Xia Zhao ◽  
Wei Chen ◽  
Tao Li ◽  
Faming Huang ◽  
Chaohong Peng ◽  
...  
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Spatial Prediction ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Rotation Forest ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract The precision of landslide susceptibility assessment has always been the focus of landslide spatial prediction research. It can be considered as the possibility of landslide disaster under the action of human activities or natural factors, or both of them. For the further exploration of the mechanism of this process, Muchuan County was proposed as the study area, and four well-known machine learning models, namely rotation forest (RF), J48 decision tree (J48), alternating decision tree (ADTree) and random forest (RaF), and their ensembles (RF-J48, RF-ADTree and RF-RaF) were introduced to explore the mechanism. These models are established by twelve landslide conditioning factors, which are selected based to the local special geological environment conditions and previous related researches, including plan curvature, profile curvature, slope angle, slope aspect, elevation, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), soil, lithology, distance to roads, and distance to rivers, as well as training (195 landslides) and validation (84 landslides) datasets were developed. The landslide prediction performance of the above conditioning factors was analyzed through the correlation attribute evaluation (CAE) model. Then, through the landslide susceptibility maps made by the above six different models, the Jenks natural breaks method is used to divide the landslide susceptibility into five grades, which are very low, low, moderate, high, and very high. In addition, the accuracy of the above six landslide susceptibility maps was verified by implementing the relative operating characteristic curve (ROC) and the area under the ROC (AUC). That is, the capabilities of the above six models are compared and verified in the landslide spatial prediction. Finally, the obtained results show that elevation, lithology and TWI are the three most principal landslide conditioning factors in this research. The RF-RaF and RaF models in the training dataset performed best, with the AUC value of 0.75, while the RF-ADTree model (0.74), RF-J48 model (0.74), ADTree model (0.71) and J48 model (0.70) performed poorly. Meanwhile, similar results also emerge from the validation dataset, in which the RF-RaF model acquired the best performance (0.82) and the rest are the RF-ADTree model (0.80), RaF model (0.79), RF-J48 model (0.77), ADTree model (0.76) and J48 model (0.71). Last but by no means the least, the results can provide scientific references for local natural resources departments.

scholarly journals Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 106 ◽  
Author(s):  
Qingfeng He ◽  
Zhihao Xu ◽  
Shaojun Li ◽  
Renwei Li ◽  
Shuai Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Entropy Method ◽  
Decision Tree Classifier ◽  
Rotation Forest ◽  
Conditioning Factors ◽  
Susceptibility Modeling ◽  
Landslide Hazards ◽  
Tree Classifier ◽  
Hybrid Intelligence

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

scholarly journals Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3590 ◽  
Author(s):  
Bui ◽  
Moayedi ◽  
Kalantar ◽  
Osouli ◽  
Gör ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Absolute Error ◽  
Slope Aspect ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Performance Error ◽  
Stream Power Index ◽  
Artificial Neural Network Ann

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.

scholarly journals Landslide Susceptibility Mapping using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh

2021 ◽  
Author(s):  
Md. Sharafat Chowdhury ◽  
Bibi Hafsa
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Information Value ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Conditioning Factors

Abstract This study attempts to produce Landslide Susceptibility Map for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). A secondary landslide inventory database was used to correlate the previous landslides with the landslide conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.

scholarly journals Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey

2020 ◽  
Vol 9 (9) ◽  
pp. 553
Author(s):  
Halil Akinci ◽  
Cem Kilicoglu ◽  
Sedat Dogan
Keyword(s):  
Random Forest ◽  
Black Sea ◽  
Landslide Susceptibility ◽  
Machine Learning Techniques ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
The Black Sea ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Black Sea Region

Natural disasters such as landslides often occur in the Eastern Black Sea region of Turkey owing to its geological, topographical, and climatic characteristics. Landslide events occur nearly every year in the Arhavi, Hopa, and Kemalpaşa districts located on the Black Sea coast in the Artvin province. In this study, the landslide susceptibility map of the Arhavi, Hopa, and Kemalpaşa districts was produced using the random forest (RF) model, which is widely used in the literature and yields more accurate results compared with other machine learning techniques. A total of 10 landslide-conditioning factors were considered for the susceptibility analysis, i.e., lithology, land cover, slope, aspect, elevation, curvature, topographic wetness index, and distances from faults, drainage networks, and roads. Furthermore, 70% of the landslides on the landslide inventory map were used for training, and the remaining 30% were used for validation. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. Evaluation results indicated that the success and prediction rates of the model were 98.3% and 97.7%, respectively. Moreover, it was determined that incorrect land-use decisions, such as transforming forest areas into tea and hazelnut cultivation areas, induce the occurrence of landslides.

scholarly journals Landslide Susceptibility Zonation Using Geospatial Technologies and Multi Criteria Evaluation Techniques in Upper Didessa Sub-basin, Southwest Ethiopia

2020 ◽  
Author(s):  
Redwan Sultan Mohammednur ◽  
Dessalegn Obsi Gemeda ◽  
Kiros Tsegay Deribew
Keyword(s):  
Landslide Susceptibility ◽  
Causative Factor ◽  
Mitigation Strategies ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Multi Criteria Evaluation ◽  
Hypsometric Integral ◽  
Expert Interview ◽  
Very High

Abstract Landslide is a serious geo-hazard that poses destruction and loses of life in different part of the world. The severity of the problem is higher in developing countries like Ethiopia. This study is aimed at assessing the spatial landslide susceptibility in the upper Didessa sub-basin using GIS and multi criteria evaluation (MCE) techniques. In order to reach this objective both primary (field survey) and secondary data (expert interview, literature, remote sensing data, digital soil map and geological map) were obtained from various source. Eleven landslide causative factor identified in this research are slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index, hypsometric integral, lithology, LULC, soil texture, and distance from road. The analytical hierarchy process (AHP) method was employed to identify the weight of each indicator from the pairwise comparison matrix. The weighted linear combination was then used to generate landslide susceptibility map (LSM). Based on landslide susceptibility, the study area was classified into very high, high, moderate, low, and very low susceptibility zones. Finally, based on the eleven-landslide causative factor analysis, about 24% of the study area is moderately susceptible, while 12% and 6% were classified as high and very high susceptibility to landslides, respectively. The results of this study could help decision makers for future landslide hazardous preventions and mitigation strategies.

scholarly journals Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3066
Author(s):  
Guangzhi Rong ◽  
Si Alu ◽  
Kaiwei Li ◽  
Yulin Su ◽  
Jiquan Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Bayesian Optimization ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Gradient Boosting ◽  
Conditioning Factors ◽  
Before And After

Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects.

scholarly journals Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam

2020 ◽  
Vol 10 (11) ◽  
pp. 3710 ◽  
Author(s):  
Quoc Cuong Tran ◽  
Duc Do Minh ◽  
Abolfazl Jaafari ◽  
Nadhir Al-Ansari ◽  
Duc Dao Minh ◽  
...  
Keyword(s):  
Predictive Models ◽  
Landslide Susceptibility ◽  
Predictive Value ◽  
Performance Metrics ◽  
Mitigation Strategies ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Ensemble Models ◽  
Hp Model ◽  
Ensemble Techniques

Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides.

scholarly journals Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model

2020 ◽  
Vol 9 (10) ◽  
pp. 566
Author(s):  
Azam Kadirhodjaev ◽  
Fatemeh Rezaie ◽  
Moung-Jin Lee ◽  
Saro Lee
Keyword(s):  
Landslide Susceptibility ◽  
Slope Aspect ◽  
Group Method ◽  
Susceptibility Map ◽  
Gray Wolf ◽  
Data Handling ◽  
Topographic Wetness Index ◽  
Roc Curve Analysis ◽  
Conditioning Factors ◽  
Model Training

Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the “optimized” GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH–GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH–GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively.

scholarly journals Landslide Susceptibility Zonation Using Statistical and Machine Learning Approaches in Northern Lecco, Italy

2021 ◽  
Author(s):  
Mohammad Mehrabi
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Learning Approaches ◽  
Visual Interpretation ◽  
Topographic Wetness Index ◽  
Inference System ◽  
Conditioning Factors ◽  
Susceptibility Maps

Abstract This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen conditioning factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system (GIS). The used evaluative models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multi-layer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and conditioning factors. Then landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC = 0.916) presented the most accurate map, followed by the FR (AUC = 0. 898) and ANFIS (AUC = 0.889). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of conditioning factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

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