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在“自然资源管理”中，命中：58条，耗时：小于0.01 秒

1.GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

作者：Seyed Amir Naghibi ; Hamid Reza Pourghasemi…

关键词：Spring potential mapping ; Boosted regression tree ; Classification and regression tree ; Random forest ; GIS ; Iran

刊名：Environmental Monitoring and Assessment

出版者：Springer Netherlands

年：2016

2.Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)

作者：Emmanuel John M. Carranza ; Alice G. Laborte

关键词：Regression trees ; Missing data ; Hydrothermal Au–Cu deposits ; Catanduanes (Philippines) ; GIS

刊名：Natural Resources Research

出版者：Springer Netherlands

年：2016

3.A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

作者：Seyed Amir Naghibi ; Hamid Reza Pourghasemi

关键词：Groundwater potential mapping ; Boosted regression tree ; Classification and regression tree ; Random forest ; General linear model ; Evidential belief function ; GIS

刊名：Water Resources Management

出版者：Springer Netherlands

年：2015

4.Influence of prior distributions and random effects on count regression models: implications for estimating standing dead tree abundance

作者：Matthew B. Russell (1) 1. Department of Forest Resources ; University of Minnesota ; St. Paul ; MN聽 ; 55108 ; USA

关键词：Biomass ; Carbon ; Coarse woody debris ; Forest inventory ; Markov chain Monte Carlo (MCMC) ; Zero ; inflation

刊名：Environmental and Ecological Statistics

出版者：Springer Netherlands

年：2015

5.Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models

作者：Lei Chen ; Chen Chu ; Tao Huang ; Xiangyin Kong ; Yu-Dong Cai

关键词：Cell ; penetrating peptide ; Pseudo ; amino acid composition ; Minimum redundancy maximum relevance ; Incremental feature selection ; Random forest

刊名：Amino Acids

出版者：Springer Wien

年：2015

6.Modeling Canada yew (Taxus canadensis Marsh.) distribution and abundance in the boreal forest of northeastern Ontario, Canada

作者：Jeffery P. Dech ; Shawn Mayhew-Hammond ; April L. James ; Bharat Pokharel

关键词：Canada yew ; Spatial modeling ; Regression tree ; Random Forests ; Disturbance ; Boreal forest

刊名：Ecological Indicators

年：January, 2014

7.Estimation of coal gross calorific value based on various analyses by random forest method

作者：S.S. Matina ; S. Chehreh Chelganib ; schehreh@umich.edu" class="auth_mail" title="E-mail the corresponding author

关键词：Gross calorific value ; Random forest ; Proximate analysis ; Ultimate analysis ; Regression

刊名：Fuel

年：2016

8.Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada

作者：G. McKay ; J. R. Harris

刊名：Natural Resources Research

出版者：Springer Netherlands

年：2016

9.Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain)

作者：Victor Rodriguez-Galianoa ; vrgaliano@ugr.es" class="auth_mail ; Maria Paula Mendesb ; Maria Jose Garcia-Soldadoc ; Mario Chica-Olmoc ; Luis Ribeirob

关键词：Random Forest ; Groundwater ; Vulnerability assessment ; Machine learning techniques ; Nitrates

刊名：Science of the Total Environment

年：1 April, 2014

10.Prehospital triage of trauma patients using the Random Forest computer algorithm

作者：Michelle Scerbo ; MD ; Hari Radhakrishnan ; MD ; Bryan Cotton ; MD ; MPH ; Anahita Dua ; MD ; Deborah Del Junco ; PhD ; Charles Wade ; PhD ; John B. Holcomb ; MD ; john.holcomb@uth.tmc.edu" class="auth_mail

关键词：Random Forest model ; Triage ; Trauma ; Overtriage ; Undertriage ; Prehospital care

刊名：Journal of Surgical Research

年：April, 2014

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