Results 21 to 30 of about 5,416,756 (285)
Prediction of rockhead using a hybrid N-XGBoost machine learning framework
Journal of Rock Mechanics and Geotechnical Engineering, 2021 The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation. Although the conventional site investigation methods (i.e.
Xing Zhu +5 more
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Wildfire Susceptibility Mapping in Karabuk Province, Türkiye Using Machine Learning Algorithms [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesWildfires are among the most devastating natural hazards, increasingly intensified by climate change and anthropogenic pressures. Accurate susceptibility mapping is essential for disaster preparedness, risk mitigation, and sustainable land management ...
S. K. M. Abujayyab +6 more
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Flexible Boosting of Accelerated Failure Time Models [PDF]
, 2008 When boosting algorithms are used for building survival models from high-dimensional data, it is common to fit a Cox proportional hazards model or to apply semiparametric least squares techniques.
Torsten Hothorn +5 more
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Comparative Assessment of Machine Learning Algorithms for Wildfire Susceptibility Mapping in South Wales, Australia [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesWildfire susceptibility mapping is an essential tool for proactive fire management in regions prone to wildfires. This study aims to determine areas of South Wales, Australia, that are susceptible to wildfires using a comprehensive set of environmental ...
A. Rostami +2 more
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International Journal of Industrial Engineering, 2023 Annually, natural catastrophes inflict heavy tolls in terms of human lives lost, property destroyed, and economies hit hard. Information and communications technology (ICT) enables the executions of diverse models that handle different parts of disaster management, while geospatial scientists work to reduce or control these risks via computer modelling
MS Srikanth +3 more
openaire +1 more source
Boosting Additive Models using Component-wise P-Splines [PDF]
, 2007 We consider an efficient approximation of Bühlmann & Yu’s L2Boosting algorithm with component-wise smoothing splines. Smoothing spline base-learners are replaced by P-spline base-learners which yield similar prediction errors but are more advantageous ...
Hothorn, Torsten, Schmid, Matthias
core +1 more source
Gradient boosting decision tree algorithm.
, 2021 Gradient boosting decision tree algorithm.
Wenjing Xu (183446) +5 more
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Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping
Remote Sensing, 2022 Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects.
Seyd Teymoor Seydi +5 more
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