Combining Logistic Regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping

Landslide susceptibility mapping is a method used to assess the probability and spatial distribution of landslide occurrences. Machine learning methods have been widely used in landslide susceptibility in recent years. In this paper, six popular machine learning algorithms namely logistic regression, multi-layer perceptron, random forests, support vector machine, Adaboost, and gradient boosted decision tree were leveraged to construct landslide susceptibility models with a total of 1365 landslide points and 14 predisposing factors. Subsequently, the landslide susceptibility maps (LSM) were generated by the trained models. LSM shows the main landslide zone is concentrated in the southeastern area of Wenchuan County. The result of ROC curve analysis shows that all models fitted the training datasets and achieved satisfactory results on validation datasets. The results of this paper reveal that machine learning methods are feasible to build robust landslide susceptibility models.

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AN EVALUATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING REMOTE SENSING DATA AND MACHINE LEARNING ALGORITHMS IN IRAN

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-503-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 503-511 ◽

Cited By ~ 2

Author(s):

B. Kalantar ◽

N. Ueda ◽

H. A. H. Al-Najjar ◽

M. B. A. Gibril ◽

U. S. Lay ◽

...

Keyword(s):

Machine Learning ◽

Land Use ◽

Roc Curve ◽

Landslide Susceptibility ◽

Total Curvature ◽

Susceptibility Mapping ◽

Machine Learning Algorithms ◽

Landslide Susceptibility Mapping ◽

Inventory Data ◽

Prediction Rate

<p><strong>Abstract.</strong> Landslide is painstaking as one of the most prevalent and devastating forms of mass movement that affects man and his environment. The specific objective of this research paper is to investigate the application and performances of some selected machine learning algorithms (MLA) in landslide susceptibility mapping, in Dodangeh watershed, Iran. A 112 sample point of the past landslide, occurrence or inventory data was generated from the existing and field observations. In addition, fourteen landslide-conditioning parameters were derived from DEM and other topographic databases for the modelling process. These conditioning parameters include total curvature, profile curvature, plan curvature, slope, aspect, altitude, topographic wetness index (TWI), topographic roughness index (TRI), stream transport index (STI), stream power index (SPI), lithology, land use, distance to stream, distance to the fault. Meanwhile, factor analysis was employed to optimize the landslide conditioning parameters and the inventory data, by assessing the multi-collinearity effects and outlier detections respectively. The inventory data is divided into 70% (78) training dataset and 30% (34) test dataset for model validation. The receiver operating characteristics (ROC) curve or area under curve (AUC) value was used for assessing the model's performance. The findings reveal that TRI has 0.89 collinearity effect based on variance-inflated factor (VIF) and based on Gini factor optimization total curvature is not significant in the model development, therefore the two parameters are excluded from the modelling. All the selected MLAs (RF, BRT, and DT) shown promising performances on landslide susceptibility mapping in Dodangeh watershed, Iran. The ROC curve for training and validation for RF are 86% success rate and 83% prediction rate implies the best model performance compared to BRT and DT, with ROC curve of 72% and 70% prediction rate, respectively. In conclusion, RF could be the best algorithm for producing landslide susceptibility map, and such results could be adopted for the decision-making process to support land use planner for improving landslide risk assessment in similar environmental settings.</p>

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Using landslide-inventory mapping for a combined bagged-trees and logistic-regression approach to determining landslide susceptibility in eastern Kentucky, United States

Quarterly Journal of Engineering Geology and Hydrogeology ◽

10.1144/qjegh2020-177 ◽

2021 ◽

pp. qjegh2020-177

Author(s):

Matthew M. Crawford ◽

Jason M. Dortch ◽

Hudson J. Koch ◽

Ashton A. Killen ◽

Junfeng Zhu ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Standard Deviation ◽

Landslide Susceptibility ◽

Engineering Geology ◽

Susceptibility Mapping ◽

Landslide Inventory ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Landslide Occurrence

High-resolution LiDAR-derived datasets from a 1.5-m digital elevation model and a detailed landslide inventory (N ≥ 1,000) for Magoffin County, Kentucky, USA, were used to develop a combined machine-learning and statistical approach to improve geomorphic-based landslide-susceptibility mapping.An initial dataset of 36 variables was compiled to investigate the connection between slope morphology and landslide occurrence. Bagged trees, a machine-learning random-forest classifier, was used to evaluate the geomorphic variables, and 12 were identified as important: standard deviation of plan curvature, standard deviation of elevation, sum of plan curvature, minimum slope, mean plan curvature, range of elevation, sum of roughness, mean curvature, sum of curvature, mean roughness, minimum curvature, and standard deviation of curvature. These variables were further evaluated using logistic regression to determine the probability of landslide occurrence and then used to create a landslide-susceptibility map.The performance of the logistic-regression model was evaluated by the receiver operating characteristic curve, area under the curve, which was 0.83. Standard deviations from the probability mean were used to set landslide-susceptibility classifications: low (0–0.10), low–moderate (0.11–0.27), moderate (0.28–0.44), moderate–high (0.45–0.7), and high (0.7–1.0). Logistic-regression results were validated by using a separate landslide inventory for the neighboring Prestonsburg 7.5-minute quadrangle, and running the same regression function. Results indicate that 74.9 percent of the landslide deposits were identified as having moderate, moderate–high, or high landslide susceptibility. Combining inventory mapping with statistical modelling identified important geomorphic variables and produced a useful approach to landslide-susceptibility mapping.Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/digitization-and-digitalization-in-engineering-geology-and-hydrogeology

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A Novel Performance Assessment Approach using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest

Sensors ◽

10.3390/s19183940 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3940 ◽

Cited By ~ 26

Author(s):

Sevgen ◽

Kocaman ◽

Nefeslioglu ◽

Gokceoglu

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Performance Assessment ◽

Landslide Susceptibility ◽

Machine Learning Algorithms ◽

Landslide Susceptibility Mapping ◽

Susceptibility Maps ◽

Surface Models ◽

Landslide Susceptibility Maps

Prediction of possible landslide areas is the first stage of landslide hazard mitigation efforts and is also crucial for suitable site selection. Several statistical and machine learning methodologies have been applied for the production of landslide susceptibility maps. However, the performance assessment of such methods have conventionally been carried out by utilizing existing landslide inventories. The purpose of this study is to investigate the performances of landslide susceptibility maps produced with three different machine learning algorithms, i.e., random forest, artificial neural network, and logistic regression, in a recently constructed and activated dam reservoir and assess the external quality of each map by using pre- and post-event photogrammetric datasets. The methodology introduced here was applied using digital surface models generated from aerial photogrammetric flight data acquired before and after the dam construction. Aerial photogrammetric images acquired in 2012 and 2018 (after the dam was filled) were used to produce digital terrain models and orthophotos. The 2012 dataset was used for producing the landslide susceptibility maps and the results were evaluated by comparing the Euclidian distances between the two surface models. The results show that the random forest method outperforms the other two for predicting the future landslides.

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Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms

Sustainability ◽

10.3390/su10103697 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3697 ◽

Cited By ~ 34

Author(s):

Hamid Pourghasemi ◽

Amiya Gayen ◽

Sungjae Park ◽

Chang-Wook Lee ◽

Saro Lee

Keyword(s):

Machine Learning ◽

Land Use ◽

Logistic Regression ◽

South Korea ◽

Landslide Susceptibility ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Slope Position ◽

Conditioning Factors ◽

Susceptibility Maps

The occurrence of landslide in the hilly region of South Korea is a matter of serious concern. This study tries to produce landslide susceptibility maps for Jumunjin Country in South Korea. Three machine learning algorithms, namely Logistic Regression (LR), LogitBoost (LB), and NaïveBayes (NB) are used, and their final model outcomes are compared to each other. Firstly, a landslide inventory map and the associated input data layers of the landslide conditioning factors were developed based on field verification, historical records, and high-resolution remote-sensing data in the geographic information system (GIS) environment. Seventeen landslide conditioning factors were prepared, including aspect, slope, altitude, maximum curvature, profile curvature, topographic wetness index (TWI), topographic positioning index (TPI), distance from fault, convexity, forest type, forest diameter, forest density, land use/land cover, lithology, soil, flow accumulation, and mid slope position. The result showed that the area under the curve (AUC) values of LR, LB, and NB models were 84.2%, 70.7%, and 85.2%, respectively. The results revealed that the LR and LB models produced reasonable accuracy than respect to NB model in landslide susceptibility assessment. The final susceptibility maps would be useful for preliminary land-use planning and hazard mitigation purpose.

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