4.4 Review

A comprehensive review of machine learning-based methods in landslide susceptibility mapping

Related references

Note: Only part of the references are listed.
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Summary: Machine learning models have been widely used in landslide susceptibility assessment, and the proper ratio of landslide to nonlandslide samples is crucial for model accuracy. This paper proposes a Bayesian optimization method to optimize the sample ratio and improve the performance of machine learning models. The results show that the optimized ratio enhances the performance of support vector machine, random forest, and gradient boost decision tree models.

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Summary: The aim of this study was to compare the prediction capabilities of four recent gradient boosting algorithms in modeling landslide susceptibility. The results showed that the CatBoost model had the highest prediction capability. Compared to other methods, the Random Forest method had lower prediction capability.

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Summary: This study aimed to map landslide susceptibility in the Bonghwa region of South Korea using a new approach with GMDH models and hybridized models. The results showed that the hybridized models outperformed traditional models, with higher predictive performance measured by evaluation metrics. The proposed approach could be applied in data-scarce regions for landslide risk assessment.

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A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan

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Summary: This study compared the performances of different algorithms for landslide susceptibility mapping in the rugged terrain of northern Pakistan. The results showed that support vector machine was the most promising model, followed by random forest and gradient-boosting machine. These models are effective for landslide susceptibility mapping and risk reduction measures in rugged terrains.

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Ground fissure susceptibility mapping based on factor optimization and support vector machines

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Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Moziihrii Ado et al.

Summary: This article surveys the current trend of machine learning models used for landslide susceptibility mapping and analyzes various factors such as models, causative factors, locations, datasets, evaluation methods, and model performance. The trend indicates a growing interest in this field, with China being the most studied location and the area under the receiver operating characteristic curve (AUC) considered as the best evaluation metric. Many ML models have achieved high AUC values, indicating a high reliability of the susceptibility map generated. The article also discusses the application of hybrid, ensemble, and deep learning models, which generally outperform traditional ML models.

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Wengang Zhang et al.

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Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies

Faming Huang et al.

Summary: This study aims to reduce the uncertainty in landslide susceptibility prediction (LSP) by exploring the neighborhood characteristics of landslide spatial datasets. Remote sensing and GIS were used to acquire and manage neighborhood environmental factors, and the landslide clustering effect was represented using the landslide aggregation index in GIS. Results showed that considering the neighborhood characteristics improved the accuracy of LSP.

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Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia

Ahmed Mohamed Youssef et al.

Summary: The study evaluated the capabilities of seven advanced machine learning techniques for landslide susceptibility modeling and found that Random Forest and Linear Discriminant Analysis performed the best. The results will be useful for environmental protection efforts.

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Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

Phuong Thao Thi Ngo et al.

Summary: In this study, a national-scale landslide susceptibility mapping of Iran was conducted using recurrent neural network (RNN) and convolutional neural network (CNN) algorithms. RNN algorithm outperformed CNN algorithm in both training and testing phases, with 6% and 14% of Iran's land area being highly susceptible to future landslide events. Additionally, 31% of cities in Iran are located in high and very high landslide susceptibility areas.

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A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping

Zhice Fang et al.

Summary: This study introduces four heterogeneous ensemble-learning techniques to predict landslide susceptibility, combining state-of-the-art classifiers in specific ways for reliable results and avoiding model selection issues. The proposed ensemble-learning methods show higher prediction accuracy than individual classifiers, with the blending method achieving the highest overall accuracy of 80.70%.

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Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

Husam A. H. Al-Najjar et al.

Summary: This research presents a novel method using Generative Adversarial Networks (GANs) to create synthetic inventory data for landslide prediction improvement, specifically in Cameron Highlands, Malaysia. The study demonstrates that utilizing GANs to generate supplementary samples can enhance the predictive capability of common landslide prediction models in data-scarce environments.

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Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India

Sunil Saha et al.

Summary: The study aimed to generate a landslide susceptibility map for the Sikkim Himalayan region using machine learning approaches, and found that the RS-RF model with a 70:30 sample ratio showed the highest goodness-of-fit and accuracy.

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Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest

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Summary: This study developed and optimized two landslide susceptibility mapping models, LR and RF, using the Bayesian algorithm, and found that the RF model had better stability and predictive capability compared to the LR model with the support of the Bayesian algorithm.

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Summary: The study evaluates the influence of variations in input data on landslide susceptibility mapping using a logistic regression approach. Models based on automatic landslide inventory show comparable overall prediction accuracy as those based on manual features, while finer resolution of topographic data leads to more accurate susceptibility models. The impact of the number of landslide-causing factors appears to be important for lower resolution data.

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Summary: The study developed a CNN-DNN model for landslide susceptibility mapping in Isfahan province, demonstrating high prediction accuracy compared to benchmark machine learning techniques. The model revealed high-susceptibility areas in the west and southwest of Isfahan province, proving useful for landslide risk management and land use planning.

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Summary: Snow plays a crucial role in preserving fresh water and influencing regional climate and environment. With the use of modern satellite Earth observations, an automated snow mapping system, called AutoSMILE, was developed based on machine learning. The system demonstrated high performance in accurately mapping snow depth in a mountainous region, showing its potential for large-scale high-accuracy snow mapping and monitoring.

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Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Zizheng Guo et al.

Summary: This study introduced a machine learning approach based on the C5.0 decision tree model and the K-means cluster algorithm to produce a regional landslide susceptibility map, which outperformed traditional models in terms of model performance according to the validation results.

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AI-powered landslide susceptibility assessment in Hong Kong

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Summary: The study introduces a novel AI-powered object-based landslide susceptibility assessment method that defines landslide and non-landslide objects based on historical statistics, constructs samples, and utilizes AI techniques for prediction. A comprehensive case study in Hong Kong demonstrates the superiority of this method over traditional approaches and produces territory-wide landslide susceptibility maps using AI algorithms.

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