Assessing landslide susceptibility using machine learning models: a comparison between ANN, ANFIS, and ANFIS-ICA

Sadighi, Mehdi; Motamedvaziri, Baharak; Hasan, Ahmadi; Moeini, Abolfazl

doi:10.1007/s12665-020-09294-8

Cited by 28 publications

(15 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of the validation measures, the CNN-RLV model had the highest goodness-off-fit and excellent predictive performance, followed by the CNN-LS, CNN-SV, DLNN-LS, DLNN-S, DLNN- RLV, ANN-LS, ANN-SV and ANN-RLV models. Sadighi et al 53 for landslide susceptibility assessment used MLP-NN with a Back-Propagation algorithm (BPANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) models. However, result of models shows that the ANFIS-ICA had the superior results but ANN had quite good predictive accuracy i.e.…”

Section: Discussionmentioning

confidence: 99%

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

Sarkar

Saha

Hembram³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. Selection of the factors was conceded by the collinearity test and information gain ratio. Using Google Earth images, official data, and field inquiry a total of 350 (present and historical) landslides were recorded and training and validation sets were prepared following the 70:30 ratio. Nine LVMs were produced i.e. a landslide susceptibility (LS), one social vulnerability (SV) and a relative vulnerability (RLV) map for each model. The performance of the models was evaluated by area under curve (AUC) of receiver operating characteristics (ROC), relative landslide density index (R-index) and different statistical measures. The combined vulnerability map of social and physical factors using CNN (CNN-RLV) had the highest goodness-of-fit and excellent performance (AUC = 0.921, 0.928) followed by DLNN and ANN models. This approach of combined physical and social factors create an appropriate and more accurate LVM that may—support landslide prediction and management.

show abstract

Section: Discussionmentioning

confidence: 99%

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

Sarkar

Saha

Hembram³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Generally, specific methods developed for LSM include: (1) inventory-based and knowledge-driven methods, such as the analytic hierarchy process (AHP) [5][6][7]; (2) bivariate and multivariate statistical methods [8], such as frequency ratio (FR) [9][10][11], statistical index (SI) [12,13], evidential belief function (EBF) [14,15], index of entropy (IOE) [16][17][18], weighted linear combination (WLC) [19,20], certainty factors (CF) [21,22], logistic regression (LR) [23][24][25], weights-of-evidence (WOE) [26,27], and fuzzy logic (FL) [28,29]; (3) machine learning methods, such as support vector machines (SVM) [30][31][32], artificial neural network (ANN) [23,33,34], and decision tree (DT) [35][36][37]. Machine learning methods have been more widely used in LSM because of their high accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China

et al. 2022

View full text Add to dashboard Cite

The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF+LR, GBDT+LR, and XGB+LR), in order to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, 15 landslide conditioning factors (i.e., altitude, slope, aspect, plane curvature, profile curvature, relief, roughness, rainfall, topographic wetness index (TWI), normalized difference vegetative index (NDVI), distance to roads, distance to rivers, land use/land cover (LULC), soil texture, and lithology) were initially selected to establish a landslide factor database. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT+LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB+LR, XGB, RF+LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.

show abstract

“…A similar effort and conclusion were reported by Yilmaz (2009) for a case study from Kat landslides in Tokat City of Turkey. Sadighi et al (2020) showed the ANFIS model outperforms ANN for landslide susceptibility modeling at Tajan Watershed, Northern Iran. The AUC values were 0.902 and 0.866.…”

Section: Introductionmentioning

confidence: 96%

Landslide Susceptibility Zonation Using Statistical and Machine Learning Approaches in Northern Lecco, Italy

Mehrabi

2021

Preprint

View full text Add to dashboard Cite

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen conditioning factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system (GIS). The used evaluative models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multi-layer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and conditioning factors. Then landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC = 0.916) presented the most accurate map, followed by the FR (AUC = 0. 898) and ANFIS (AUC = 0.889). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of conditioning factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

show abstract

Assessing landslide susceptibility using machine learning models: a comparison between ANN, ANFIS, and ANFIS-ICA

Cited by 28 publications

References 58 publications

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China

Landslide Susceptibility Zonation Using Statistical and Machine Learning Approaches in Northern Lecco, Italy

Contact Info

Product

Resources

About