Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China
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TL;DR: Zhang et al. as discussed by the authors investigated the application of a class-weighted algorithm combined with conventional machine learning model (logistic regression (LR)) and ensemble machine learning models (LightGBM and random forest (RF)) to the landslide susceptibility evaluation.
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About: This article is published in Computers & Geosciences. The article was published on 01 Jan 2022. and is currently open access. The article focuses on the topics: Section (typography) & Landslide.
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Citations
Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
Moziihrii Ado,Khwairakpam Amitab,Arnab Kumar Maji,Elżbieta Jasińska,Radomir Gono,Zbigniew Leonowicz,Michal Jasinski +6 more
TL;DR: Machine learning models used for landslide susceptibility mapping are surveyed to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance.
Hybrid machine learning approach for landslide prediction, Uttarakhand, India
Poonam Kainthura,Neelam Sharma +1 more
TL;DR: In this paper , the prediction accuracy of five hybrid models for landslide occurrence in the Uttarkashi, Uttarakhand (India) was evaluated and compared and the results indicated that the constructed hybrid model HXGBRS (AUC = 0.937, Precision =0.946, F1-score = 0 .926 and Accuracy = 89.92%) is the most accurate model for predicting landslides when compared to other models (HBPNNRS, HBNRS, HBRS, and HRFRS).
Exploring the uncertainty of landslide susceptibility assessment caused by the number of non–landslides
TL;DR: Zhang et al. as mentioned in this paper investigated the evolution of landslide susceptibility under different numbers of non-landslides, and thus to answer the question of how many landslides are required for susceptibility modeling.
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Leo Breiman
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy
TL;DR: In this paper, the authors used geomorphological information to assess areas at high landslide hazard, and help mitigate the associated risk, and found that despite the operational and conceptual limitations, landslide hazard assessment may indeed constitute a suitable, cost-effective aid to land-use planning.
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The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan
TL;DR: In this paper, a landslide susceptibility map in the Kakuda-Yahiko Mountains of Central Japan is presented, where the authors use logistic regression to find the best fitting function to describe the relationship between the presence or absence of landslides (dependent variable) and a set of independent parameters such as slope angle and lithology.
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