A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China

Wang, Yumiao; Liao, Feng; Li, Sijia; Ren, Fei; Du, Qingyun

doi:10.1016/j.catena.2019.104425

Cited by 134 publications

(65 citation statements)

References 41 publications

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“…GDBT is an ensemble machine learning method combining multiple decision trees based on the Boosting concept [46]. It continuously improves the prediction accuracy through interactions.…”

Section: Gradient Boosting Decision Treementioning

confidence: 99%

“…AdaBoost is the most widely used Boosting algorithm involving an adaptive resampling method with enhanced predictive ability [40,45]. GBDT is also a representative Boosting algorithm, which is an iterative decision tree algorithm, and the results of all trees are aggregated as the final result with high precision [40,46]. The GBDT model has often been implemented for various hazard susceptibility mappings [47,48].…”

Section: Introductionmentioning

confidence: 99%

“…RF, based on Bagging and random feature selection, is a popular machine learning method and is known to provide high accuracy rates [17,49]. Several studies have successfully carried out studies on landslides and wildfire susceptibility modeling and mapping using RF [8,50], GBDT [46,47] and AdaBoost [26,43,51].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Jiang

Wang

et al. 2021

Remote Sensing

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Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on susceptibility modeling for the two hazards together have been conducted for this region, and the intersection and the uncertainty of the two hazards are rarely assessed. Thus, the intersection of landslide and wildfire susceptibility and the spatial uncertainty of the susceptibility maps were studied in this paper. Reliable landslide and wildfire susceptibility maps are necessary for disaster management and land use planning. This work used three advanced ensemble machine learning algorithms: RF (Random Forest), GBDT (Gradient Boosting Decision Tree) and AdaBoost (Adaptive Boosting) to assess the landslide and wildfire susceptibility for SEA. A geo-database was established with 2759 landslide locations, 1633 wildfire locations and 18 predictor variables in total. The performances of the models were assessed using the overall classification accuracy (ACC), Precision, the area under the ROC (receiver operating curve) (AUC) and confusion matrix values. The results showed RF performs superior in both landslide (ACC = 0.81, Precision = 0.78 and AUC= 0.89) and wildfire (ACC= 0.83, Precision = 0.83 and AUC = 0.91) susceptibility modeling, followed by GBDT and AdaBoost. The overall superiority of RF over other models indicates that it is potentially an efficient model for landslide and wildfire susceptibility mapping. The landslide and wildfire susceptibility were obtained using the RF model. This paper also conducted an overlay analysis of the two hazards. The uncertainty of the susceptibility was further assessed using the coefficient of variation (CV). Additionally, the distance to roads is relatively important in both landslide and wildfire susceptibility, which is the most important in landslides and the second most important in wildfires. The result of this paper is useful for mastering the whole situation of hazard susceptibility and proves that RF is a robust model in the hazard susceptibility assessment in SEA.

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“…GDBT is an ensemble machine learning method combining multiple decision trees based on the Boosting concept [46]. It continuously improves the prediction accuracy through interactions.…”

Section: Gradient Boosting Decision Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Jiang

Wang

et al. 2021

Remote Sensing

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“…In fact, heterogeneity can importantly affect the model (Wang et al, 2020). Considering roads as promoters of development, it is necessary to research on the detail of how vulnerability is produced at the household scale in terms of construction techniques and soil management in the resulting landslide-susceptible areas from this model.…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…These were the one-by-one 'simple' method (variation of the coefficient of one variable at a time, while the others remain unchanged) and the random-variation one, also called MonteCarlo (in which all variables may change randomly). The Area Under the ROC Curve (AUC), also called ROC value has been adopted, considering it is a common measure to evaluate prediction accuracy of models for natural hazards (Abbaszadeh Shahri et al, 2019;Wang et al, 2020). By interpreting the ROC value as a metric from the simple method, it can be observed that the slope, road density, intense precipitations and population variables' curves importantly vary along with the variations of the factors that multiply the reference coefficients.…”

Section: Discussionmentioning

confidence: 99%

Landslide Susceptibility Mapping for Quito with Logistic Regression and Sensitivity Analysis

Puente-Sotomayor

Mustafà

Teller

2020

Preprint

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The Andean region is one of the highest landslide-susceptible areas worldwide, while still limited attention has been devoted to the topic in this context in terms of research, risk reduction practice and urban policy. Given the collection of a few early datasets for landslides after a dozen years by the Andean city of Quito, this article aims to explore the predictive power of a binary logistic regression model (LOGIT) to test the provided data and multicriteria evaluation for landslide susceptibility in this urban area. Furthermore, two types of sensitivity analysis, the univariate and the MonteCarlo methods, were applied in order to suggest a better calibration of the LOGIT model. Amongst the ten variables included in the model for the context of Quito, which help to explain its landslide susceptibility, results showed that population and road density have relevant predicting power for high landslide susceptibility when adopting a weights-based data normalization for the model. This model was validated with a receiving operating characteristic of 0.79. Sensitivity analyses suggested calibrations of the model different from the referential one that would improve this metric up to 2%. The calibration process suggests further experimentation regarding other methods of normalization and a finer level of disaggregation of data. In terms of policy design, the LOGIT model coefficients values suggest the need of a deep analysis of the impacts that urban features such as population, road density, building footprint and floor area at a household scale have on the generation of landslide susceptibility in Andean cities like Quito.

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A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

et al. 2023

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Landslide susceptibility mapping (LSM) has been widely used as an important reference for development and construction planning to mitigate the potential social‐eco impact caused by landslides. Originally, most of those maps were generated by the judgements of experts, which is time‐consuming and laborious, and whose accuracy is difficult to be quantified because of the subjective effects. With the development of machine learning algorithms and the methods of data collection, big data and artificial intelligence have now been popularized in this field, significantly improving mapping accuracy and efficiency. Various machine learning‐based methods, mainly including conventional machine learning, deep learning, and transfer learning have been applied and compared in LSM in different areas by previous researchers. Nevertheless, none of them can be effective in all cases. Although deep learning‐based methods were proven more accurate than conventional machine learning‐based methods in most data‐rich situations, the latter is sometimes more popularly used in LSM, as there is not that much data in this field to train a deep learning network perfectly. In a more rigorous situation when there is very limited data, transfer learning‐based approaches are applied by several researchers, which have contributed to improve the workability and the accuracy of LSM in data‐limited areas. Such technical explosion has promoted the application of landslide susceptibility maps, thus contributing to mitigating the social‐eco impact associated with landslides. This paper comprehensively reviews the whole process of generating landslide susceptibility maps based on machine learning methods, introduces and compares the commonly used machine learning methods, and discusses the topics for future research.

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A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China

Cited by 134 publications

References 41 publications

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Landslide Susceptibility Mapping for Quito with Logistic Regression and Sensitivity Analysis

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

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