Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(13), P. 3029 - 3029

Published: June 24, 2022

Landslide is a devastating natural disaster, causing loss of life and property. It likely to occur more frequently due increasing urbanization, deforestation, climate change. susceptibility mapping vital safeguard This article surveys machine learning (ML) models used for landslide understand the current trend by analyzing published articles based on ML models, causative factors (LCFs), study location, datasets, evaluation methods, model performance. Existing literature considered in this comprehensive survey systematically selected using ROSES protocol. The indicates growing interest field. choice LCFs depends data availability case location; China most studied area under receiver operating characteristic curve (AUC) best metric. Many have achieved an AUC value > 0.90, indicating high reliability map generated. paper also discusses recently developed hybrid, ensemble, deep (DL) mapping. Generally, DL outperform conventional models. Based survey, few recommendations future works which may help new researchers field are presented.

Language: Английский

---

Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy

Geoscience Frontiers, Journal Year: 2023, Volume and Issue: 14(6), P. 101645 - 101645

Published: June 7, 2023

The application of ensemble learning models has been continuously improved in recent landslide susceptibility research, but most studies have no unified framework. Moreover, few papers discussed the applicability model mapping at township level. This study aims defining a robust framework that can become benchmark method for future research dealing with comparison different models. For this purpose, present work focuses on three basic classifiers: decision tree (DT), support vector machine (SVM), and multi-layer perceptron neural network (MLPNN) two homogeneous such as random forest (RF) extreme gradient boosting (XGBoost). hierarchical construction deep relied leading technologies (i.e., homogeneous/heterogeneous bagging, boosting, stacking strategy) to provide more accurate effective spatial probability occurrence. selected area is Dazhou town, located Jurassic red-strata Three Gorges Reservoir Area China, which strategic economic currently characterized by widespread risk. Based long-term field investigation, inventory counting thirty-three slow-moving polygons was drawn. results show do not necessarily perform better; instance, Bagging based DT-SVM-MLPNN-XGBoost performed worse than single XGBoost model. Amongst eleven tested models, Stacking RF-XGBoost model, ensemble, showed highest capability predicting landslide-affected areas. Besides, factor behaviors DT, SVM, MLPNN, RF reflected characteristics landslides reservoir area, wherein unfavorable lithological conditions intense human engineering activities water level fluctuation, residential construction, farmland development) are proven be key triggers. presented approach could used occurrence prediction similar regions other fields.

Language: Английский

---

A physics-informed data-driven model for landslide susceptibility assessment in the Three Gorges Reservoir area

Geoscience Frontiers, Journal Year: 2023, Volume and Issue: 14(5), P. 101621 - 101621

Published: April 26, 2023

Landslide susceptibility mapping is a crucial tool for analyzing geohazards in region. Recent publications have popularized data-driven models, particularly machine learning-based methods, owing to their strong capability dealing with complex nonlinear problems. However, significant proportion of these models neglected qualitative aspects during analysis, resulting lack interpretability throughout the process and causing inaccuracies negative sample extraction. In this study, Scoops 3D was employed as physics-informed qualitatively assess slope stability study area (the Hubei Province section Three Gorges Reservoir Area). The non-landslide samples were extracted based on calculated factor safety (FS). Subsequently, random forest algorithm landslide under receiver operating characteristic curve (AUC) serving model evaluation index. Compared benchmark (i.e., standard method utilizing pure algorithm), proposed method's AUC value improved by 20.1%, validating effectiveness dual-driven (physics-informed data-driven).d.

Language: Английский

---

A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(5), P. 1717 - 1743

Published: Jan. 29, 2023

Language: Английский

---

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(6), P. 988 - 988

Published: March 12, 2024

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

Language: Английский

---

Exploring the uncertainty of landslide susceptibility assessment caused by the number of non–landslides

CATENA, Journal Year: 2023, Volume and Issue: 227, P. 107109 - 107109

Published: March 28, 2023

Language: Английский

---

Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(6), P. 2243 - 2270

Published: March 13, 2023

Language: Английский

---

Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution

Remote Sensing Applications Society and Environment, Journal Year: 2023, Volume and Issue: 29, P. 100905 - 100905

Published: Jan. 1, 2023

Language: Английский

---

Sustainable groundwater management in coastal cities: Insights from groundwater potential and vulnerability using ensemble learning and knowledge-driven models

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 442, P. 141152 - 141152

Published: Feb. 1, 2024

Language: Английский

---

Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 2947 - 2947

Published: Aug. 12, 2024

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution research publication trends, (3) progress of algorithms, (4) application techniques models for landslide susceptibility mapping, detections, prediction, inventory deformation monitoring, assessment, extraction management. selections were based on keyword searches using title/abstract keywords from Web Science Scopus. A total 186 articles published between 2011 2024 critically reviewed to provide answers questions related the recent advances use technologies combined with artificial intelligence (AI), machine (ML), deep (DL) algorithms. review revealed that these methods have high efficiency detection, hazard mapping. few current issues also identified discussed.

Language: Английский

---