检索结果-南通市图书馆

Predicting plant disease epidemics using boosted regression trees

Infectious Disease Modelling 2024年第4期9卷 1138-1146页

作者： Chun Peng Xingyue Zhang Weiming Wang School of Mathematics and Statistics Huaiyin Normal UniversityHuaian223300PR China Ecole Polytechnique Federale de Lausanne Rte Cantonale1015LausanneSwitzerland

Plant epidemics are often associated with weather-related *** is difficult to identify weather-related predictors for models predicting plant *** the article by Shah et al.,to predict Fusarium head blight(FHB)epidemics of wheat,they explored a functional approach using scalar-on-function regression to model a binary outcome(FHB epidemic or non-epidemic)with respect to weather time series spanning 140 days relative to *** scalar-on-function models fit the data better than previously described logistic regression *** this work,given the same dataset and models,we attempt to reproduce the article by Shah et *** a different approach,boosted regression *** fitting,the classification accuracy and model statistics are surprisingly good.

关键词： Plant disease epidemics Scalar-on-function model boosted regression trees

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau

引用

Pedosphere 2018年第5期28卷 739-750页

作者： ZHI Junjun ZHANG Ganlin YANG Renmin YANG Fei JIN Chengwei LIU Feng SONG Xiaodong ZHAO Yuguo LI Decheng State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing 210008(China) University of Chinese Academy of Sciences Beijing 100049 (China)

Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely ***, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.

关键词： boosted regression trees classification and regression tree digital soil mapping random forest soil diagnostic horizons,support vector machine

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Patterns of forest composition and their long term environmental drivers in the tropical dry forest transition zone of southern Africa

引用

Forest Ecosystems 2017年第1期4卷 33-44页

作者： Vera De Cauwer Coert J.Geldenhuys Raf Aerts Miya Kabajani Bart Muys Namibia University of Science and Technology Faculty of Natural Resources and Spatial SciencesPrivate Bag 13388WindhoekNamibia University of Leuven Division ForestNature and LandscapeCelestijnenlaan 200E box 2411BE-3001 LeuvenBelgium. Forest&Wood Science Stellenbosch Universityc/o Forestwood ccMurrayfieldPretoriaSouth Africa. University of Leuven Division EcologyEvolution and Biodiversity ConservationKasteelpark Arenberg 31-2435BE-3001 LeuvenBelgium.

Background： Tropical dry forests cover less than 13 % of the world＇s tropical forests and their area and biodiversity are declining. In southern Africa, the major threat is increasing population pressure, while drought caused by climate change is a potential threat in the drier transition zones to shrub land. Monitoring climate change impacts in these transition zones is difficult as there is inadequate information on forest composition to allow disentanglement from other environmental drivers. Methods： This study combined historical and modern forest inventories covering an area of 21,000 km2 in a transition zone in Namibia and Angola to distinguish late succession tree communities, to understand their dependence on site factors, and to detect trends in the forest composition over the last 40 years. Results： The woodlands were dominated by six tree species that represented 84 % of the total basal area and can be referred to as Bdikioea - Pterocarpus woodlands. A boosted regression tree analysis revealed that late succession tree communities are primarily determined by climate and topography. The Schinziophyton rautanenfi and Baikiaea plurijuga communities are common on slightly inclined dune or valley slopes and had the highest basal area （5.5 - 6.2 m^2 ha＆-1）. The Burkea africana - Guibourtia coleosperma and Pterocarpus angolensis - Diafium englerianum communities are typical for the sandy plateaux and have a higher proportion of smaller stems caused by a higher fire frequency. A decrease in overall basal area or a trend of increasing domination by the more drought and cold resilient B. africana community was not confirmed by the historical data, but there were significant decreases in basal area for Ochna pulchra and the valuable fruit tree D. englerianum. Conclusions： The slope communities are more sheltered from fire, frost and drought but are more susceptible to human expansion. The community with the important timber tree P. angolensis can best withstand high

关键词： Baikiaea woodland Tree community Namibia boosted regression trees Pterocarpus ango/ensis Disturbance Miombo Ecoregion Climate change

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China

引用

Journal of Forestry Research 2018年第5期29卷 1263-1276页

作者： Quanyi Qiu Guoliang Yun Shudi Zuo Jing Yan Lizhong Hua Yin Ren Jianfeng Tang Yaying Li Qi Chen Key Laboratory of Urban Environment and Health Key Laboratory of Urban Metabolism of Xiamen Institute of Urban Environment Chinese Academy of Sciences Jimei Avenue 1799 Xiamen People's Republic of China Ningbo Urban Environment Observation and Research Station-NUEORS Chinese Academy of SciencesNingbo 315800 People's Republic of China University of Chinese Academy of Sciences Beijing 100049 People's Republic of China Department of Spatial Information Science and Engineering Xiamen University of Technology Xiamen 361024People's Republic of China Department of Geography University of Hawai'i at Manoa422 Saunders Hall 2424 Maile Way Honolulu Hawai'iUSA

We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling *** sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees *** to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure *** age and diameter at breast height are the major *** with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic *** deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local *** minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.

关键词： BEF boosted regression trees Eucalyptus plantations Local biomass model Regional biomass estimation Biotic versus abiotic factors Uncertainty analysis

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：