Selection of weightages for causative factors used in preparation of landslide susceptibility zonation (LSZ)

Gupta, Sharad; Shukla, Dericks Praise; Thakur, Manoj

doi:10.1080/19475705.2018.1447027

Cited by 39 publications

(11 citation statements)

References 40 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tsangaratos and Ilia [99] stated that multicollinearity analysis can be used in order to determine the conditional independence among variables for the feature selection process. Also, feature selection algorithm as fisher score [40,56], gain ratio [94,107], χ 2 [46,56], learning vector quantization [66], correlation based feature selection [84] and information gain [11,79] was used to determine the best feature combination or feature ranking by several researches. Due to the complexity of mechanism of system structure of landslide more flexible nonlinear ML algorithms [14,63,64] have also been utilized to found more efficient or irrelevant factors for producing landslide related applications.…”

Section: Introductionmentioning

confidence: 99%

Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest

Şahin

2020

SN Appl. Sci.

192

View full text Add to dashboard Cite

Decision tree-based classifier ensemble methods are a machine learning (ML) technique that combines several tree models to produce an effective or optimum predictive model, and that allows well-predictive performance especially compared to a single model. Thus, selecting a proper ML algorithm help us to understand possible future occurrences by analyzing the past more accurate. The main purpose of this study is to produce landslide susceptibility map of the Ayancik district of Sinop province, situated in the Black Sea region of Turkey using three featured regression tree-based ensemble methods including gradient boosting machines (GBM), extreme gradient boosting (XGBoost), and random forest (RF). Fifteen landslide causative factors and 105 landslide locations occurred in the region were used. The landslide inventory map was randomly divided into training (70%) and testing (30%) dataset to construct the RF, XGBoost and GBM prediction models. Symmetrical uncertainty measure was utilized to determine the most important causative factors, and then the selected features were used to construct susceptibility prediction models. The performance of the ensemble models was validated using different accuracy metrics including Area under the curve (AUC), overall accuracy (OA), Root mean square error (RMSE), and Kappa coefficient. Also, the Wilcoxon signed-rank test was used to assess differences between optimum models. The accuracy results showed that the model of XgBoost_Opt model (the model created by optimum factor combination) has the highest prediction capability (OA = 0.8501 and AUC = 0.8976), followed by the RF_opt (OA = 0.8336 and AUC = 0.8860) and GBM_Opt (OA = 0.8244 and AUC = 0.8796). When the Wilcoxon sign-rank test results were analyzed, XgBoost_Opt model, which is the best subset combinations, were confirmed to be statistically significant considering other models. The results showed that, the XGBoost method according to optimum model achieved lower prediction error and higher accuracy results than the other ensemble methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest

Şahin

2020

SN Appl. Sci.

192

View full text Add to dashboard Cite

show abstract

“…A multiplicative factor is required for projection of the data. This multiplicative factor is used for giving weights to all the thematic layers (Gupta et al, 2018).…”

Section: Fisher Discriminant Analysis (Fda)mentioning

confidence: 99%

“…It is a special case of linear regression and predicts the probability of the occur-rence of an event by using logit function (Gupta et al, 2018). In this method, the probability of presence or ab-sence of a binary outcome (1 = landslide and 0 = no land-slide) is modelled based on the values of predictor variables (Shukla et al, 2016).…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

“…The weights obtained in previous section are used for computation of landslide susceptibility index (LSI). LSI is calculated using Weighted linear combination method (Jhunjhunwalla et al, 2019, Gupta et al, 2018, Michael, Samanta, 2016. LSI can be calculated as given in Eq-5…”

Section: Landslide Susceptibility Index Computation and Susceptibilitmentioning

confidence: 99%

“…India has more than 15% of its land area prone to landslides, hence mapping of these areas for the presence/absence of landslides is of utmost importance. Numerous studies have contributed to reduce the damage from landslides through modelling and production of susceptibility maps (Roodposhti et al, 2019, Jhunjhunwalla et al, 2019, Gupta et al, 2018, Shukla et al, 2016. The susceptibility mapping can be a crucial tool for a wide range of end-users, from both private and public sectors, aimed at hazard mitigation purposes at both local and international levels.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Imbalance in Landslide Susceptibility Zonation: Under-Sampling for Class-Imbalance Learning

Gupta

Jhunjhunwalla

Bhardwaj

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Machine learning methods such as artificial neural network, support vector machine etc. require a large amount of training data, however, the number of landslide occurrences are limited in a study area. The limited number of landslides leads to a small number of positive class pixels in the training data. On contrary, the number of non-landslide pixels (negative class pixels) are enormous in numbers. This under-represented data and severe class distribution skew create a data imbalance for learning algorithms and suboptimal models, which are biased towards the majority class (non-landslide pixels) and have low performance on the minority class (landslide pixels).In this work, we have used two algorithms namely EasyEnsemble and BalanceCascade for balancing the data. This balanced data is used with feature selection methods such as fisher discriminant analysis (FDA), logistic regression (LR) and artificial neural network (ANN) to generate LSZ maps The results of the study show that ANN with balanced data has major improvements in preparation of susceptibility maps over imbalanced data, where as the LR method is ill-effected by data balancing algorithms. The FDA does not show significant changes between balanced and imbalanced data.

show abstract

An Ordinal Scale Weighting Approach for Susceptibility Mapping Around Tehri Dam, Uttarakhand, India

Singh

Gupta

Dubey

et al. 2020

Understanding and Reducing Landslide Disaster Risk

View full text Add to dashboard Cite

Selection of weightages for causative factors used in preparation of landslide susceptibility zonation (LSZ)

Cited by 39 publications

References 40 publications

Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest

Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest

Data Imbalance in Landslide Susceptibility Zonation: Under-Sampling for Class-Imbalance Learning

An Ordinal Scale Weighting Approach for Susceptibility Mapping Around Tehri Dam, Uttarakhand, India

Contact Info

Product

Resources

About