scholarly journals Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

2019 ◽  
Vol 9 (5) ◽  
pp. 942 ◽  
Author(s):  
Soyoung Park ◽  
Jinsoo Kim
Keyword(s):  
Random Forest ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Information Gain ◽  
Regression Tree ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Boosted Regression Tree ◽  
Prediction Rate ◽  
Better Than

This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain, South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) model. Through the construction of a landslide inventory map, 140 landslide locations were found. Among these, 42 (30%) were reserved to validate the model after 98 (70%) had been selected at random for model training. Fourteen landslide explanatory variables related to topography, hydrology, and forestry factors were considered and selected, based on the results of information gain for the modeling. The results were evaluated and compared using the receiver operating characteristic curve and statistical indices. The analysis showed that the RF model was better than the BRT model. The RF model yielded higher specificity, overall accuracy, and kappa index than the BRT model. In addition, the RF model, with a prediction rate of 0.865, performed slightly better than the BRT model, which had a prediction rate of 0.851. These results indicate that the landslide susceptibility maps (LSMs) produced in this study had good performance for predicting the spatial landslide distribution in the study area. These LSMs could be helpful for establishing mitigation strategies and for land use planning.

scholarly journals Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy

2020 ◽  
Author(s):  
Vasil Yordanov ◽  
Maria Antonia Brovelli
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Risk Mitigation ◽  
Model Performance ◽  
Initial Step ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Statistical Index ◽  
Local Conditions ◽  
Basin Scale

Abstract Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations.

scholarly journals Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 421 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Wei Chen ◽  
John J Clague ◽  
...  
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Information Gain ◽  
Characteristic Curve ◽  
Model Performance ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Conditioning Factors

We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

scholarly journals Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

2019 ◽  
Vol 9 (1) ◽  
pp. 171 ◽  
Author(s):  
Wei Chen ◽  
Zenghui Sun ◽  
Jichang Han
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Weights Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset

The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.

scholarly journals GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 162
Author(s):  
Anna Roccati ◽  
Guido Paliaga ◽  
Fabio Luino ◽  
Francesco Faccini ◽  
Laura Turconi
Keyword(s):  
Risk Assessment ◽  
Land Use ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Risk Mitigation ◽  
Current Model ◽  
Susceptibility Mapping ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk

Landslide susceptibility mapping is essential for a suitable land use managing and risk assessment. In this work a GIS-based approach has been proposed to map landslide susceptibility in the Portofino promontory, a Mediterranean area that is periodically hit by intense rain events that induce often shallow landslides. Based on over 110 years landslides inventory and experts’ judgements, a semi-quantitative analytical hierarchy process (AHP) method has been applied to assess the role of nine landslide conditioning factors, which include both natural and anthropogenic elements. A separated subset of landslide data has been used to validate the map. Our findings reveal that areas where possible future landslides may occur are larger than those identified in the actual official map adopted in land use and risk management. The way the new map has been compiled seems more oriented towards the possible future landslide scenario, rather than weighting with higher importance the existing landslides as in the current model. The paper provides a useful decision support tool to implement risk mitigation strategies and to better apply land use planning. Allowing to modify factors in order to local features, the proposed methodology may be adopted in different conditions or geographical context featured by rainfall induced landslide risk.

Communality from equal weight for GIS-based landslide susceptibility mapping

2020 ◽  
Author(s):  
Sandip Som ◽  
Saibal Ghosh ◽  
Soumitra Dasgupta ◽  
Thrideep Kumar ◽  
J. N. Hindayar ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Roc Curves ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Equal Weight ◽  
Landslide Susceptibility Index

Abstract Modeling landslide susceptibility is one of the important aspects of land use planning and risk management. Several modeling methods are available based either on highly specialized knowledge on causative attributes or on good landslide inventory data to use as training and testing attribute on model development. Understandably, these two criteria are rarely available for local land regulators. This paper presents a new model methodology, which requires minimum knowledge of causative attributes and does not depend on landslide inventory. As landslide causes due to the combined effect of causative attributes, this model utilizes communality (common variance) of the attributes, extracted by exploratory factor analysis and used for calculation of landslide susceptibility index. The model can understand the inter-relationship of different geo-environmental attributes responsible for landslide along with identification and prioritization of attributes on model performance to delineate non-performing attributes. Finally, the model performance is compared with the well established AHP method (knowledge driven) and FRM method (data driven) by cut-off independent ROC curves along with cost-effectiveness. The model shows it’s performance almost at par with the established models, involving minimum modeling expertise. The findings and results of the present work will be helpful for the town planners and engineers on a regional scale for generalized planning and assessment.

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