A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers

Pham, Binh Thai; Prakash, Indra; Dou, Jie; Singh, Sushant K.; Trinh, Phan Trong; Tran, Hieu; Le, Tu Minh; Dang, Kim Khoi; Shirzadi, Ataollah; Bui, Dieu Tien

doi:10.1080/10106049.2018.1559885

Cited by 131 publications

(64 citation statements)

References 64 publications

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“…The valley depth above 160 m in the study area is highly prone to landslides and showed a positive association between the valley depth and landslides [53]. We have classified valley depth into six categories (Figure 3h).…”

Section: Valley Depthmentioning

confidence: 99%

“…Machine learning techniques have recently gained good attention among the environmental modeling research community as they are advantageous in efficiently capturing the complex relationship between the environmental predictors and the response, such as flood [33][34][35][36][37][38][39][40][41], wildfire [42], sinkhole [43], drought [44], gully erosion [45,46], groundwater [47][48][49] and land/ground subsidence [27], and landslide in this case [3,13,[50][51][52][53][54][55][56][57]. In due course, researches have also attempted to improve the prediction accuracy and the interpretability of the models through applying various decision-trees machine learning algorithms such as chi-square automatic interaction detector; quick, unbiased and efficient statistical tree [58]; J48 decision trees [59]; ID3 decision trees [60]; random forests [61]; classification and regression trees [62]; alternating decision trees [63]; reduced error pruning trees [3]; naïve Bayes [35,53]; naïve Bayes tree [13,64]; kernel logistic regression [37]; logistic model tree [38,…”

Section: Introductionmentioning

confidence: 99%

“…In due course, researches have also attempted to improve the prediction accuracy and the interpretability of the models through applying various decision-trees machine learning algorithms such as chi-square automatic interaction detector; quick, unbiased and efficient statistical tree [58]; J48 decision trees [59]; ID3 decision trees [60]; random forests [61]; classification and regression trees [62]; alternating decision trees [63]; reduced error pruning trees [3]; naïve Bayes [35,53]; naïve Bayes tree [13,64]; kernel logistic regression [37]; logistic model tree [38,65]; Bayesian logistical regression [66] and support vector regression [67]. There is a consistent recommendation of applying hybrid or ensemble models as they have shown promising results in correctly predicting the landslide susceptibility on smaller, but complex datasets [3,13,53,62,[68][69][70].…”

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUC training = 0.859; AUC validation = 0.813); however, the RS ensemble model (AUC training = 0.883; AUC validation = 0.842) outperformed and outclassed the RF (AUC training = 0.871; AUC validation = 0.840), and the BA (AUC training = 0.865; AUC validation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.

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Section: Valley Depthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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“…Recently, machine-learning (ML) techniques have become popular for the spatial prediction of natural hazards like wildfires [22], sinkholes [23], groundwater depletion and flooding [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], droughts [39], earthquakes [40], land subsidence [41], and landslides [42][43][44][45][46][47][48]. ML is a type of artificial intelligence (AI) that uses computer algorithms to analyze and forecast information by learning from training data.…”

Section: Introductionmentioning

confidence: 99%

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Arabameri

Chen

Blaschke

et al. 2019

Water

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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach-bagging-based alternating decision-tree classifier (bagging-ADTree)-and use it to model a landscape's susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model's goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models' analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model's results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately Water 2020, 12, 16 2 of 26 identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.

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“…However, these traditional statistical methods do not provide satisfactory evaluation of the correlation between landslide influencing factors [4,27].Therefore, machine learning technologies have drawn extensive attention, and many kinds of machine learning methods have been developed and used, such as classification and regression trees [28,29], adaptive neuro-fuzzy inference systems [30,31], fuzzy logic [32,33], alternating decision trees [34][35][36], support vector machine [37][38][39], artificial neural networks [40,41], and random forest [4,[42][43][44][45]. In particular, hybrid models are increasingly used, such as the rotation forest-based decision trees [46,47], frequency ratio-based ANFIS model [48], bagging-based reduced error pruning trees [49], and multiboost-based support vector machines [50].Spatial prediction of landslide is not only the first important step, but also one of the most difficult tasks [31,51]. Many modeling methods have been used in the construction of landslide susceptibility maps in the past, but the accuracy of these models has not been accepted by all researchers [52].…”

mentioning

confidence: 99%

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Chen

2019

Water

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In this study, Random SubSpace-based classification and regression tree (RSCART) was introduced for landslide susceptibility modeling, and CART model and logistic regression (LR) model were used as benchmark models. 263 landslide locations in the study area were randomly divided into two parts (70/30) for training and validation of models. 14 landslide influencing factors were selected, such as slope angle, elevation, aspect, sediment transport index (STI), topographical wetness index (TWI), stream power index (SPI), profile curvature, plan curvature, distance to rivers, distance to road, soil, normalized difference vegetation index (NDVI), land use, and lithology. Finally, the hybrid RSCART model and two benchmark models were applied for landslide susceptibility modeling and the receiver operating characteristic curve method is used to evaluate the performance of the model. The susceptibility is quantitatively compared based on each pixel to reveal the system spatial pattern between susceptibility maps. At the same time, area under ROC curve (AUC) and landslide density analysis were used to estimate the prediction ability of landslide susceptibility map. The results showed that the RSCART model is the optimal model with the highest AUC values of 0.852 and 0.827, followed by LR and CART models. The results also illustrate that the hybrid model generally improves the prediction ability of a single landslide susceptibility model.Water 2020, 12, 113 2 of 29 weights of evidence [10][11][12], frequency ratio [13][14][15][16][17], logistic regression [18][19][20][21], linear multivariate regression, multivariate adaptive regression spline [22][23][24], and statistical index [25,26] have been widely used. However, these traditional statistical methods do not provide satisfactory evaluation of the correlation between landslide influencing factors [4,27].Therefore, machine learning technologies have drawn extensive attention, and many kinds of machine learning methods have been developed and used, such as classification and regression trees [28,29], adaptive neuro-fuzzy inference systems [30,31], fuzzy logic [32,33], alternating decision trees [34][35][36], support vector machine [37][38][39], artificial neural networks [40,41], and random forest [4,[42][43][44][45]. In particular, hybrid models are increasingly used, such as the rotation forest-based decision trees [46,47], frequency ratio-based ANFIS model [48], bagging-based reduced error pruning trees [49], and multiboost-based support vector machines [50].Spatial prediction of landslide is not only the first important step, but also one of the most difficult tasks [31,51]. Many modeling methods have been used in the construction of landslide susceptibility maps in the past, but the accuracy of these models has not been accepted by all researchers [52]. Youssef et al. used CART model to study landslide susceptibility in Wadi Tayyah Basin. The result of post-cart model is not optimal [53]. However, when Felicísimo et al. studied the landslide susceptibility, CART shows...

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A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers

Cited by 131 publications

References 64 publications

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

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