Using the rotation and random forest models of ensemble learning to predict landslide susceptibility

Zhao, Lingran; Wu, Xueling; Niu, Ruiqing; Wang, Ying; Zhang, Kaixiang

doi:10.1080/19475705.2020.1803421

Cited by 37 publications

(18 citation statements)

References 39 publications

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“…Regarding the machine learning models, the random forest classifier (RFC) is based on a combination of decision tree classifiers and, therefore, it is considered a powerful supervised algorithm for solving binary classification tasks (Breiman 2001). The RFC model has been used in several LS studies, such as Chen et al (2018aChen et al ( , 2018b, Sevgen et al (2019), Nsengiyumva and Valentino (2020), Kocaman et al (2020) or Zhao et al (2020). Similarly to RFC model, Naïve Bayes classifier (NBC), a supervised probabilistic algorithm built on Bayes theorem, have been applied in several studies in recent years (Tsangaratos and Ilia 2016;He et al 2019;Chen et al 2020aChen et al , 2020bLee et al 2020;Lei et al 2020aLei et al , 2020b.…”

Section: Introductionmentioning

confidence: 99%

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Pham

Achour

Ali

et al. 2021

Geomatics, Natural Hazards and Risk

108

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Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Pham

Achour

Ali

et al. 2021

Geomatics, Natural Hazards and Risk

108

View full text Add to dashboard Cite

show abstract

“…Stumpf et al 48 proposed a machine learning‐based analysis framework with a repetitive strategy to handle class imbalance. Zhao et al 49 tackled minor data imbalance issues in predicting landslide susceptibility by utilizing a voting system and the random processing of samples with a random forest algorithm.…”

Section: Related Workmentioning

confidence: 99%

An intelligent framework for end‐to‐end rockfall detection

Zoumpekas

Puig

Salamó

et al. 2021

Int J of Intelligent Sys

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Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner (TLS) or digital cameras. Multitemporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertize and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework presents major research challenges and justifies exhaustive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state‐of‐the‐art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been examined and benchmarked applying well‐known metrics to be incorporated into our system. Specifically, we developed machine learning techniques and applied them to analyze point cloud data extracted from TLS in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental results indicate that some of the above‐mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally validated accuracy.

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“…Stumpf & Kerle (2011) proposed a machine learning based analysis framework with an iterative scheme to handle class imbalance. Zhao et al (2020) tackled minor data imbalance issues in predicting landslide susceptibility by utilizing a voting system and the random processing of samples with a random forest algorithm.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Intelligent Framework for Rockfall Detection

Zoumpekas,

Puig,

Salamó

et al. 2021

Preprint

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Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner or digital cameras. Multi-temporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertise and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework poses significant research challenges and justifies intensive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state-of-the-art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been benchmarked and compared applying well-known metrics to be incorporated into our system. Specifically, we developed statistical and machine learning techniques and applied them to analyze point cloud data extracted from Terrestrial Laser Scanner in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental data suggest that some of the above-mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally proven accuracy.

show abstract

Using the rotation and random forest models of ensemble learning to predict landslide susceptibility

Cited by 37 publications

References 39 publications

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

An intelligent framework for end‐to‐end rockfall detection

End-to-End Intelligent Framework for Rockfall Detection

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