Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

Arabameri, Alireza; Pradhan, Biswajeet; Rezaei, Khalil; Lee, Chang-Wook

doi:10.3390/rs11090999

Cited by 117 publications

(77 citation statements)

References 93 publications

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“…In this study, locations at distances less than 100 m from a stream were more likely to see gully formation. Some researchers have confirmed these results [9,13,16,42]. The sheer force of flow can overcome and decrease the strength of soil along the sides of gully forms and lead to the development of gullies of greater dimensions.…”

Section: Discussionmentioning

confidence: 82%

“…Gullies occur mainly in the areas close to streams (<100 m) [13]. In addition, most of the gullies occur in areas receiving 68 to 85 mm of precipitation annually [16] (Table 2).…”

Section: Spatial Relationship Between Gully Locations and Conditioninmentioning

confidence: 99%

“…Roads may intercept overland flow and promote gully formation. Most of the gullies occur near roads (<1000 m) [16]; the strongest relationship is <500 m (FR = 6.43).…”

Section: Spatial Relationship Between Gully Locations and Conditioninmentioning

confidence: 99%

“…This approach can provide useful and easy-to-understand information to planners and hazard managers [9], but there is no standard procedure for producing these maps. In recent decades, researchers have devised and experimented with many GESM techniques and various traditional data-driven approaches, including logistic regression (LR) [10,11], weights of evidence (WoE) [12,13], conditional analysis (CA) [14,15], certainty factor (CF) [16], index or entropy (IOE) [17], analytical hierarchy process (AHP) [18,19], and frequency ratio (FR) [12].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, some factors contribute to gullying that are either difficult to recognize (and measure), or they are difficult to convert to raster formats for modeling. Therefore, one of the future fields of gully modeling should focus on the detection and Water 2020, 12,16 3 of 26 application of the unknown factors that influence gully formation. This may be achieved by combining gullying research with GIS and data-mining tools to create a tool or technique that can map future, unknown factors.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Discussionmentioning

confidence: 82%

“…Gullies occur mainly in the areas close to streams (<100 m) [13]. In addition, most of the gullies occur in areas receiving 68 to 85 mm of precipitation annually [16] (Table 2).…”

Section: Spatial Relationship Between Gully Locations and Conditioninmentioning

confidence: 99%

“…Roads may intercept overland flow and promote gully formation. Most of the gullies occur near roads (<1000 m) [16]; the strongest relationship is <500 m (FR = 6.43).…”

Section: Spatial Relationship Between Gully Locations and Conditioninmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Arabameri

Chen

Blaschke

et al. 2019

Water

Self Cite

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Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping

Lu,

Tang,

Yan

et al. 2024

Earth Surf Processes Landf

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In this study, the application of open‐source digital elevation model (DEM) is explored for regional landslide susceptibility mapping (LSM), and the potential impact of different DEM choices on the mapping accuracy is also examined. With the advancements in remote sensing technology, an increasing number of global open‐source DEMs have been available, with improvement in the accuracy. However, the latest released data are rarely evaluated in LSM research. In this paper, DEM‐based factors, including elevation, aspect, slope, plan curvature and profile curvature, were generated from seven open‐source DEMs, including Advanced Spaceborne Thermal Emission and Reflection (ASTER) V2, ASTERV3, ALOS World 3D‐30 m (AW3D30), Copernicus DEM 30 m (COP) Forest and Buildings removed Copernicus DEM (FABDEM), NASADEM, and Shuttle Radar Topography Mission (SRTM). DEM‐based factors were coupled with the distance to road, distance to river, land use, lithology, rain and normalized difference vegetation index (NDVI). The significant difference between DEMs is determined by comparing the area proportion. Slope, plane curvature and profile curvature are found to have a maximum difference of 15%–20%. Then, K‐Nearest Neighbours (KNN) and Random Forest (RF) were used to predict landslide susceptibility with two sampling methods, namely, 70% for training and 30% for testing (S1); 67% for training and 33% for testing (S2). For KNN with S1, the prediction rate is range from 0.8299 to 0.8701, with a difference of 0.0402. The difference of prediction rate is decreased to 0.0207 for S2 and 0.0258 for RF. COP has the highest prediction rate of 0.8701, 0.9254 and 0.9461 for KNN with S1 and RF with S1 and S2, respectively. ASTERV2 is the worst with prediction rate of 0.8897 and 0.8996 for KNN with S2 and RF with S1, respectively. The research result provides valuable insights for the selection of open‐source DEMs in future LSM.

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Landslide risk assessment and management using hybrid machine learning‐based empirical models

Yang,

Jiang,

Arabameri

et al. 2023

Geological Journal

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Landslides are a prevalent geologic phenomenon that substantially threatens human life and infrastructure, resulting in considerable loss and destruction. The practice of landslide susceptibility mapping is crucial for the mitigation of risks connected with this natural disaster. This work aims at investigating the influence of varying sample sizes on the precision of landslide susceptibility modelling using a case study conducted in the Alamout basin, Iran. The researchers used a machine learning methodology based on tree algorithms to construct a model for predicting the likelihood of landslides. Additionally, they adopted a multi‐scenario strategy to address the inherent uncertainty associated with the input data. The integration of the naive Bayes tree (NBTree), random forest (RF), logistic model tree (LMT) and J48 algorithms was performed. The modelling process included using 20 predictive parameters across four distinct scenarios. Four models, labelled S1, S2, S3 and S4, were used in this study. These models utilized 25%, 50%, 75% and 100% of the available inventory data. The research presented in this study is distinguished by using a tree‐based methodology for landslide susceptibility modelling and incorporating a multi‐scenario strategy to address the inherent uncertainty associated with the input data. The findings indicated that the augmentation of the sample size improved the precision of the models. The efficacy of using a multi‐scenario strategy in enhancing the dependability of the model is also underscored. Among the 20 input elements used in the modelling process, it was seen that slope angle accounted for the highest relative significance, constituting 25.60% of the overall influence. Following more closely, distance to fault contributed significantly, with a relative importance of 23.40%. Additionally, rainfall and elevation exhibited notable contributions, with relative volumes of 7.91% and 5.50%, respectively. All four landslide models showed adequate learning and forecasting ability throughout the training and testing phases. During the testing phase, the true skill score (TSS) values exhibited a range of 0.631–0.804, while the area under the receiver operating characteristic curve values showed a range of 0.745–0.921. The susceptibility maps indicated that a significant portion of the region exhibits moderate to very high susceptibility zones, with the northern and eastern sectors displaying greater landslide values than the western region. The model's performance showed improvement from S1 to S4 in both the training and testing phases. The performance of the models exhibited the following trend: in scenario 1, the RF model outperformed the J48, LMT and NBTree models; in scenario 2, the RF model surpassed the NBTree and LMT models, while being on par with the J48 model; in scenarios 3 and 4, the RF model showed superior performance compared to the NBTree, J48 and LMT models. Therefore, the RF model proved to be the most effective among the models evaluated. The findings derived from this research have the potential to serve as valuable references for the purposes of land‐use planning and catastrophe risk management.

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Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

Cited by 117 publications

References 93 publications

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

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

Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping

Landslide risk assessment and management using hybrid machine learning‐based empirical models

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