Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Arabameri, Alireza; Santosh, M.; Saha, Sunil; Ghorbanzadeh, Omid; Tiefenbacher, John P.; Moayedi, Hossein; Costache, Romulus

doi:10.1080/19475705.2021.1914753

Cited by 17 publications

(14 citation statements)

References 131 publications

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“…Conversely, regions with high negative PrC values are inclined to experience rapid drainage, causing dryness and potential soil imbalance. Therefore, regions with high negative or positive PrC values have a higher chance of being affected by landslides than regions with lower PrC values (Arabameri, Santosh, et al, 2021). Elevated slope curvature scores can increase a region's susceptibility to landslides, as they indicate the assembling of water and soil on the slope, leading to increased weight and reduced stability.…”

Section: Conditioning Factors For Landslidesmentioning

confidence: 99%

“…This makes evaluating landslide vulnerability through numerical models challenging (Gutierrez-Lopez, 2022;Kim et al, 2018;Sassa et al, 2018). Nevertheless, the challenge has been surmounted by incorporating ML models with remote sensing (RS) and geographic information system (GIS), enabling the spatial modelling of landslide susceptibility over more extensive regions (Arabameri et al, 2019;Arabameri, Santosh, et al, 2021;Nwazelibe et al, 2023aNwazelibe et al, , 2023b. For instance, Arabameri et al (2019); Arabameri, Santosh, et al (2021) successfully applied statistical and artificial intelligence models for study suggests that the RF, NBTree, J48 and LMT models could still be applied in data-scarce and large regions, even with a limited landslide inventory.…”

Section: Implications and Benefits Of The Present Susceptibility Mode...mentioning

confidence: 99%

“…Nevertheless, the challenge has been surmounted by incorporating ML models with remote sensing (RS) and geographic information system (GIS), enabling the spatial modelling of landslide susceptibility over more extensive regions (Arabameri et al, 2019;Arabameri, Santosh, et al, 2021;Nwazelibe et al, 2023aNwazelibe et al, , 2023b. For instance, Arabameri et al (2019); Arabameri, Santosh, et al (2021) successfully applied statistical and artificial intelligence models for study suggests that the RF, NBTree, J48 and LMT models could still be applied in data-scarce and large regions, even with a limited landslide inventory. Thus, apart from having relevance for the present study area, the findings of this research promise to provide robust insights for landslide scholars across the globe.…”

Section: Implications and Benefits Of The Present Susceptibility Mode...mentioning

confidence: 99%

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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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Section: Conditioning Factors For Landslidesmentioning

confidence: 99%

Section: Implications and Benefits Of The Present Susceptibility Mode...mentioning

confidence: 99%

Section: Implications and Benefits Of The Present Susceptibility Mode...mentioning

confidence: 99%

See 1 more Smart Citation

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

Yang,

Jiang,

Arabameri

et al. 2023

Geological Journal

Self Cite

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show abstract

“…In the model, Gini coefficient is used to continuously test all the segmentation points of the feature subset of the same tree, and the branch corresponding to the smallest feature is selected. Then the model is verified by comparing different out-of-bag errors [66]. 4.6.…”

Section: Logistic Regressionmentioning

confidence: 99%

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Zhao

Zhu

et al. 2022

Geofluids

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The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. To this end, a landslide inventory map consisting of 4278 identified landslides is randomly divided into training and test landslides in a ratio of 7 : 3. The 15 landslide influencing factors are selected as follows: slope aspect, slope degree, elevation, terrain curvature, plane curvature, profile curvature, surface roughness, distance to faults, distance to roads, landform, lithology, distance to rivers, rainfall, stream power index (SPI), and normalized difference vegetation index (NDVI), and the potential multicollinearity problem among these factors is detected by Pearson correlation coefficient (PCC), variance inflation factor (VIF), and tolerance (TOL). We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. The training success rates of LR, SVM, RF, WOA-RF, and GA-RF models are 0.7546, 0.8317, 0.8561, 0.8804, and 0.8957; the testing success rates are 0.7551, 0.8375, 0.8395, 0.8348, and 0.85007. The results show that the GA significantly improves the predictive power of the RF model. This study provides a scientific reference for disaster prevention and control in this area and its surrounding areas.

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“…The true skill statistic (TSS), a threshold-dependent measurement metric (Allouche et al, 2006), has been recently used in estimating the efficiency of natural risks' prediction (Arabameri et al, 2021; Distefano et al, 2021). TSS ranges from À1 to +1.…”

Section: Tss Metricmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

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Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Cited by 17 publications

References 131 publications

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

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

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

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