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

Fang, Zhice; Wang, Yi; Peng, Ling; Hong, Haoyuan

doi:10.1080/13658816.2020.1808897

Cited by 181 publications

(73 citation statements)

References 87 publications

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“…The present study analyzed and compared six heterogeneous models for LSI. For each model, the training and testing datasets were constructed using the common sampling strategy (Ali et al, 2021;Fang et al, 2021). Note that the estimated weight of each landslide model is significant for final LSI.…”

Section: Landslide Susceptibility Mapping Via Xgboostmentioning

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

109

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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: Landslide Susceptibility Mapping Via Xgboostmentioning

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

109

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“…However, only one identified study had used a model based on extremely randomized trees (extra trees) [25]. Furthermore, being able to know which models achieve the best performance can be useful to carry out advanced techniques of landslide susceptibility mapping based on "blending" or "ensembling" different models [86][87][88].…”

Section: Discussionmentioning

confidence: 99%

Landslide Susceptibility Assessment Using an AutoML Framework

Bruzón

Arrogante-Funes

et al. 2021

IJERPH

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The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.

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“…The approach works in three steps: one is the spatial database, the second is splitting training and test data, and the third one is applying different ensemble learning approaches. The outcome captured reveals that ensemble learning achieves superior results compared to the individual method [10]. The use of Deep Neural Networks (DNN) in various fields like speech, text, and visual has achieved good performance and success.…”

Section: Literature Surveymentioning

confidence: 99%

Ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis

SivaSubramanian¹,

Prabha²

2022

Computer Systems Science and Engineering

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Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner. Further investigation of customer patterns helps the firm to develop efficient decisions and in turn, helps to optimize the enterprise's business and maximizes consumer satisfaction correspondingly. To conduct an effective assessment about the customers, Naive Bayes(also called Simple Bayes), a machine learning model is utilized. However, the efficacious of the simple Bayes model is utterly relying on the consumer data used, and the existence of uncertain and redundant attributes in the consumer data enables the simple Bayes model to attain the worst prediction in consumer data because of its presumption regarding the attributes applied. However, in practice, the NB premise is not true in consumer data, and the analysis of these redundant attributes enables simple Bayes model to get poor prediction results. In this work, an ensemble attribute selection methodology is performed to overcome the problem with consumer data and to pick a steady uncorrelated attribute set to model with the NB classifier. In ensemble variable selection, two different strategies are applied: one is based upon data perturbation (or homogeneous ensemble, same feature selector is applied to a different subsamples derived from the same learning set) and the other one is based upon function perturbation (or heterogeneous ensemble different feature selector is utilized to the same learning set). Furthermore, the feature set captured from both ensemble strategies is applied to NB individually and the outcome obtained is computed. Finally, the experimental outcomes show that the proposed ensemble strategies perform efficiently in choosing a steady attribute set and increasing NB classification performance efficiently.

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

Cited by 181 publications

References 87 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

Landslide Susceptibility Assessment Using an AutoML Framework

Ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis

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