Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments

Chen, Wei; Chen, Yunzhi; Tsangaratos, Paraskevas; Ilia, Ioanna; Wang, Xiaojing

doi:10.3390/rs12233854

Cited by 72 publications

(17 citation statements)

References 95 publications

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“…In this paper, hazard susceptibility modeling and mapping is a classification problem, with binary outcomes of the presence and absence of hazards, meaning that measurements assessing the model performance by evaluating the prediction results and accuracy are important [17]. In the literature, the overall accuracy (ACC), precision and the area under the ROC curve (AUC) are considered the main metrics by which to evaluate the overall results [8,17,86]. In addition, the confusion matrix is also implemented as a further metric to evaluate the model performances quantitatively and graphically.…”

Section: Model Performance and Accuracy Assessmentmentioning

confidence: 99%

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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Section: Model Performance and Accuracy Assessmentmentioning

confidence: 99%

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

Jiang

Wang

et al. 2021

Remote Sensing

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“…Moreover, the receiver operating characteristic curve (ROC) and the area under the curve (AUC) [116] are also employed for assessing the prediction model performance [56,117,118]. It is because the AUC can express the overall predictive accuracy of the employed classifiers used for soil erosion prediction.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Dinh

Nguyen

Tran

et al. 2021

Mathematical Problems in Engineering

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Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

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“…Up till now, there is no universally agreed upon condition-specific determination of LCF. Studies like [13,28] use ML models to select LCFs for better accuracy. Popular LCFs can be broadly categorized as follow: In the papers we have surveyed, the LCFs, namely slope, elevation, rainfall, distance to rivers, LULC, NDVI, and distance to roads, are commonly used in landslide susceptibility mapping.…”

Section: Landslide Causative Factorsmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

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Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments

Cited by 72 publications

References 95 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

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

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