Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

Pham, Binh Thai; Prakash, Indra; Singh, Sushant K.; Shirzadi, Ataollah; Shahabi, Himan; Tran, Thi-Thu-Trang; Bui, Dieu Tien

doi:10.1016/j.catena.2018.12.018

Cited by 257 publications

(122 citation statements)

References 74 publications

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“…For validation of the ML based models, many criteria could be applied for the validation/ assessment of the performance of the models. In this study, prediction accuracy of the proposed models was assessed by the positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), specificity (SPF), sensitivity (SST), kappa (k), Root Mean Square Error (RMSE), area under the ROC curve (AUC) [50][51][52]. More specifically, PPV and NPV are the probability of pixels associated with correct classification of landslide and non-landslide, respectively [53].…”

Section: Validation Criteriamentioning

confidence: 99%

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Nguyen

Tran

et al. 2019

Sustainability

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Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

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Section: Validation Criteriamentioning

confidence: 99%

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Nguyen

Tran

et al. 2019

Sustainability

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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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“…Some research shows that bivariate statistical models demonstrated better predictive power than both machine learning and data mining algorithms [23]; this is because machine learning and data mining algorithms are more complex and require an expert to perform accurate simulations. Therefore, bivariate models, which are very simple to run with similar or sometimes superior predictive power, can be used as adequate substitutes.At present, the EBF method is rarely applied for flood analysis, though it has been used for other categories of natural disasters such as landslide susceptibility assessment [21,33,58], and land subsidence [12,59], as well as having been used to predict groundwater potential zones [19,60].The main purpose of this research is to evaluate the possibilities of using the EBF method to generate flood susceptibility maps and to assess the strengths and weaknesses of this method, as EBF has rarely been used for floods but has shown high accuracy in previous studies involving natural hazards mapping. The specific objectives of the current study include, (i) determining the most significant factors in each model, (ii) application of a bivariate statistical model, EBF, to produce new ensemble models, in combination with a statistical model, LR, in order to generate a more accurate flood susceptibility map, (iii) detection of the flood-prone areas within the study region for improved management during flood occurrence.…”

mentioning

confidence: 99%

“…At present, the EBF method is rarely applied for flood analysis, though it has been used for other categories of natural disasters such as landslide susceptibility assessment [21,33,58], and land subsidence [12,59], as well as having been used to predict groundwater potential zones [19,60].…”

mentioning

confidence: 99%

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

et al. 2019

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Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.In recent years, many statistical and probabilistic models have been tested to prepare flood susceptibility maps [9,10]. Geographic Information System (GIS) has been used as an effective tool for spatial analysis and data manipulation due to its ability to handle large amounts of spatial data [11]. Specifically, the combination of statistical and probabilistic models with Remote Sensing (RS) and GIS has been widely used by different researchers [1,12]. Additionally, some scientists and researchers have studied natural disasters, specifically floods and FSM, with the help of RS and GIS, using different models such as Decision-Tree (DT) [6,13], Support Vector Machine (SVM) [14,15], Frequency Ratio (FR) [16,17], Evidential Belief Function (EBF) [18][19][20], EBF-AHP (Analytical Hierarchy Process) [21], Logistic Regression (LR) [22], Shannon's entropy and...

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Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

Cited by 257 publications

References 74 publications

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

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

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

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