Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost

Şahin, Emrehan Kutluğ

doi:10.1007/s00477-022-02330-y

Cited by 27 publications

(10 citation statements)

References 97 publications

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“…A number of locations exhibiting vulnerability to landslides were discovered within Noelmina Village, Oesusu Village, and Takari Sub-district. Furthermore, the inclusion of a geological map is imperative in the detection of landslide-prone areas [24]. Geological mapping serves the purpose of identifying rock formations, geological structures, and hydrogeological conditions prevalent within a given region.…”

Section: Mapping the Geographic Condition In Takari Districtmentioning

confidence: 99%

Implementation of Mapping-Based Machine Learning Algorithm as Non-Structural Disaster Mitigation to Detect Landslide Susceptibility in Takari District

Fallo,

Nipu

2024

BAREKENG: J. Math. & App.

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This research is primarily dedicated to providing a comprehensive exposition of the methodology applied in the deployment of a cartographic-based machine learning algorithm designed for the precise identification of areas susceptible to landslides within the geographical confines of the Takari District.This research delves into the application of mapping-based machine learning algorithms in the domain of non-structural disaster mitigation, with a specific emphasis on the detection of landslide susceptibility within the Takari District. A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. An evaluation of model performance, anchored in accuracy and Kappa metrics, unveiled that both the Ordinal Logistic Regression and Random Forest models exhibited noteworthy precision, reaching a commendable 74.36%. Nevertheless, a meticulous examination of Kappa values disclosed the ascendancy of the Random Forest model, which achieved a superior Kappa value of 0.5397. As portrayed in the visual representation provided, it becomes manifest that the Random Forest algorithm's prognostications yield 66 instances of cloudy atmospheric conditions, 48 occurrences of light precipitation, and 3 episodes of moderate rainfall. These predictions are influenced by several factors, including average temperature, humidity levels, wind speed, duration of sunlight, and wind direction at maximum speed. Consequently, this comprehensive analysis underscores the Random Forest algorithm as the most efficacious model for landslide susceptibility prediction. Furthermore, the study seamlessly integrated overlay maps, encompassing the Slope Inclination Map of the Takari District, Geological Map of the Takari District, and Soil Type Map of the Takari District, to contribute to the formulation of a definitive map delineating the susceptibility to landslides in the Takari District. Furthermore, further research could conduct spatial validation of the model predictions using additional datasets or remote sensing data to validate the accuracy of the landslide susceptibility map and ensure its applicability across different geographical regions.

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Section: Mapping the Geographic Condition In Takari Districtmentioning

confidence: 99%

Implementation of Mapping-Based Machine Learning Algorithm as Non-Structural Disaster Mitigation to Detect Landslide Susceptibility in Takari District

Fallo,

Nipu

2024

BAREKENG: J. Math. & App.

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“…In the past few years, there has been an upward pattern in the application of ML techniques, for instance, support vector machines (SVMs) for identifying landslide-prone locations [15][16][17], logistic model trees (LMTs) [18,19], artificial neural networks (ANNs) [20][21][22], and decision trees (DTs) [23][24][25]. Most scholars claim that ML techniques are comparatively more efficient and effective than conventional approaches.…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Ali,

Chen,

et al. 2024

Remote Sensing

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Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

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“…It should be considered that one of the most important aspects for the generation of LSM with greater accuracy is not only the ML model to be applied but also the selection of an appropriate set of conditioning factors [16]. In this sense, factor selection analyzes the relevance of the factors to be used to build prediction models and is applied to eliminate irrelevant variables and simplify LSM generation [17].…”

Section: Introductionmentioning

confidence: 99%

“…Daviran et al [44] compared three ML techniques (SVM, ANN, and RF) in the Tarom-Khalkhal sub-basin (Iran) and obtained superior performance with RF. Sahin [16] developed a framework for LSM in the Babadag district (Turkey) and obtained excellent performance with both XGBoost and RF. Zhang et al [45] generated LSM in Fengjie County (China) and obtained similar performance for both algorithms, although with a slight superiority for RF.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

Bravo-López

Sellers

Fernández

2023

Land

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Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (>0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (>72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area.

show abstract

Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost

Cited by 27 publications

References 97 publications

Implementation of Mapping-Based Machine Learning Algorithm as Non-Structural Disaster Mitigation to Detect Landslide Susceptibility in Takari District

Implementation of Mapping-Based Machine Learning Algorithm as Non-Structural Disaster Mitigation to Detect Landslide Susceptibility in Takari District

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

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