The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China

Zhang, Kaixiang; Wu, Xueling; Niu, Ruiqing; Ke, Yang; Zhao, Lingran

doi:10.1007/s12665-017-6731-5

Cited by 162 publications

(68 citation statements)

References 63 publications

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“…Furthermore, our analysis showed that the spatial probability of landslide significantly increased from 0.75 to 1 when the TRI increased from 14 to 27 (Figure 6a) and the slope increased from 25 • to 51 • (Figure 6b) in the protected forest. The high importance of the TRI [42,52] and slope [41,48,50,132] for mapping the landslide susceptibility has also been reported in several studies. Nevertheless, some research has addressed the low importance of slope for mapping landslide susceptibility [42,47,58].…”

Section: The Importance Of Conditioning Factors For Mapping Landslidementioning

confidence: 63%

“…Both studied forests showed almost similar topographic characteristics; however, the aspect (Figure 7b) and elevation ( Figure 7c) recorded higher scores among the topographic features in the non-protected forest (Figure 4). Likewise, several studies have confirmed the high importance of aspect and elevation for landslide susceptibility mapping [49][50][51][52][53].…”

Section: The Importance Of Conditioning Factors For Mapping Landslidementioning

confidence: 84%

“…The statistical models are dependent on the input data characteristics, where any uncertainty in data may lead to a huge error in mapping the landslide susceptibility [40,41]. In contrast, machine learning applies algorithms for modeling through learning data, where their high ability in the estimation of a model has made them more popular for analyzing the landslide susceptibility at a regional scale [42] such as artificial neural networks (ANN) [43], decision trees (DT) [44], Bayesian network (BN) and naïve Bayes [45], support vector machines (SVM) [46], and random forest (RF) [42,[47][48][49][50][51][52][53]. RF, as an ensemble machine learning algorithm, is known for its ability in handling both parametric and non-parametric variables, working with big data without any selection, reduction, or preprocessing, handling missing values automatically, avoiding the risk of over-fitting, self-testing using "out of bag" data, and yielding high satisfactory accuracy in modeling [54].…”

Section: Introductionmentioning

confidence: 99%

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A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Shirvani

2020

Remote Sensing

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Despite recent progress in landslide susceptibility mapping, a holistic method is still needed to integrate and customize influential factors with the focus on forest regions. This study was accomplished to test the performance of geographic object-based random forest in modeling the susceptibility of protected and non-protected forests to landslides in northeast Iran. Moreover, it investigated the influential conditioning and triggering factors that control the susceptibility of these two forest areas to landslides. After surveying the landslide events, segment objects were generated from the Landsat 8 multispectral images and digital elevation model (DEM) data. The features of conditioning factors were derived from the DEM and available thematic layers. Natural triggering factors were derived from the historical events of rainfall, floods, and earthquake. The object-based image analysis was used for deriving anthropogenic-induced forest loss and fragmentation. The layers of logging and mining were obtained from available historical data. Landslide samples were extracted from field observations, satellite images, and available database. A single database was generated including all conditioning and triggering object features, and landslide samples for modeling the susceptibility of two forest areas to landslides using the random forest algorithm. The optimal performance of random forest was obtained after building 500 trees with the area under the receiver operating characteristics (AUROC) values of 86.3 and 81.8% for the protected and non-protected forests, respectively. The top influential factors were the topographic and hydrologic features for mapping landslide susceptibility in the protected forest. However, the scores were loaded evenly among the topographic, hydrologic, natural, and anthropogenic triggers in the non-protected forest. The topographic features obtained about 60% of the importance values with the domination of the topographic ruggedness index and slope in the protected forest. Although the importance of topographic features was reduced to 36% in the non-protected forest, anthropogenic and natural triggering factors remarkably gained 33.4% of the importance values in this area. This study confirms that some anthropogenic activities such as forest fragmentation and logging significantly intensified the susceptibility of the non-protected forest to landslides.

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Section: The Importance Of Conditioning Factors For Mapping Landslidementioning

confidence: 63%

Section: The Importance Of Conditioning Factors For Mapping Landslidementioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Shirvani

2020

Remote Sensing

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“…The group of the quantitative methods includes statistical methods such as frequency ratio or logistic regression (Lee and Pradhan 2007;Vakhshoori and Pourghasemi 2019), machine learning methods, e.g. random forest (Zhang et al 2017), artificial neural networks (Dou et al 2015) or support vector machines (Hong et al 2015;Zhang et al 2018). Many landslide susceptibility studies aimed at comparing these methods and finding the most suitable alternative (Abedini and Tulabi 2018).…”

Section: Introductionmentioning

confidence: 99%

Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia

Vojteková

Vojtek

2020

Geomatics, Natural Hazards and Risk

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Landslide susceptibility is an important topic mainly because its geo-spatial analysis provides a useful tool for planning, disaster management and hazard mitigation. In this study, the aim is to identify and analyze landslide susceptibility at a local spatial scale, which is represented by the town of Handlov a, using the multicriteria evaluation (i.e., the analytical hierarchy process technique -AHP) and geographic information systems (GIS). The following landslide conditioning factors were selected representing the local terrain predispositions: slope angle, geology, slope aspect, elevation, distance from rivers, distance from faults and land use. The raster-based analysis was performed using the spatial resolution of 10 Â 10 m. The weights for each factor were determined by the AHP technique where slope angle had the highest relative importance. Based on the resulting susceptibility map, 51.98% out of the total study area is characterized by high and very high susceptibility class. The Atlas of Slope Stability of the Slovak Republic, which contains past landslides until 2006, was used for verification of the results. The verification confirmed a moderate accuracy between the landslide susceptibility map and landslide inventory from the atlas since 60.8% of all landslide areas from the atlas corresponded with high and very high susceptibility class. ARTICLE HISTORY

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“…As the prediction ability of the above models has needed to be improved, machine learning methods have been introduced, such as support vector machine (SVM) [34][35][36], random forest (RF) [37,38], adaptive neuro-fuzzy inference systems (ANFIS) [39,40], artificial neural network (ANN) [41][42][43], decision tree (DT) [44,45], and classification and regression tree (CART) [46,47]. Many researchers have gradually found that the prediction ability of a single model is limited [48].…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China

Chen

Fan²,

Li³

et al. 2019

Applied Sciences

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The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration approaches of these two bivariate models and logistic regression (LR) were used as benchmark models. Nanzheng County was considered as the study area. First, a landslide distribution map was produced using news reports, interpreting satellite images and a regional survey. A total of 202 landslides were identified and marked. According to the previous studies and local geological environment conditions, 16 landslide conditioning factors were chosen for landslide spatial prediction research: elevation, profile curvature, plan curvature, slope angle, slope aspect, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), distance to roads, distance to rivers, distance to faults, lithology, rainfall, soil, normalized different vegetation index (NDVI), and land use. Then, the 202 landslides were randomly segmented into two parts with a ratio of 70:30. Seventy percent of the landslides (141) were used as the training dataset and the remaining landslides (61) were used as the validating dataset. Next, the evaluation models were built using the training dataset and compared by the receiver operating characteristics (ROC) curve. The results showed that all models performed well; the FR_SysFor model exhibited the best prediction ability (0.831), followed by the IoE_SysFor model (0.819), IoE_LR model (0.702), FR_LR model (0.696), IoE model (0.691), and FR model (0.681). Overall, these six models are practical tools for landslide spatial prediction research and the results can provide a reference for landslide prevention and control in the study area.

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The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China

Cited by 162 publications

References 63 publications

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia

Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China

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