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基于Orange集成多机器学习模型的城市周边农地流转价格评估研究——以武汉市为例
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作者 张祚 张跻耀 +1 位作者 晏学丽 廖慧 《中国土地科学》 北大核心 2025年第5期92-106,共15页
研究目的:探索一种新型的精准、高效且易操作的农地流转价格估价方法,对城市周边农地流转价格实现全局性快速评估,并解析影响因素,为农地流转的科学管理提供技术支撑。研究方法:以武汉市为例,选取多源数据指标为预测因子,基于Orange平台... 研究目的:探索一种新型的精准、高效且易操作的农地流转价格估价方法,对城市周边农地流转价格实现全局性快速评估,并解析影响因素,为农地流转的科学管理提供技术支撑。研究方法:以武汉市为例,选取多源数据指标为预测因子,基于Orange平台,构建多机器学习模型并融合堆叠(Stacking)集成学习的方式,分析城市周边农地流转价格的影响因素并进行价格预测。研究结果:(1)融合多源数据和机器学习的Orange工作流能高效评估精细尺度下的农地流转价格,并具有很好的可解释性和泛化能力。旱地最优模型为自适应提升(AdaBoost)模型,水田最优模型为AdaBoost与随机森林(RF)、极端梯度提升(XGBoost)、K近邻(KNN)模型堆叠的集成学习模型。(2)武汉市农地流转价格的主导影响因素为中心城镇影响度、景观生态多样性指数、农用地经济等指数等,且前两者分别具有显著正向影响和负向影响。(3)武汉市跨城区农地流转价格差异明显,但整体呈现“中心—边缘”递减的空间特征。旱地价格以中高值为主,水田价格以中低值为主,且在局部表现出异质性。研究结论:多源数据预测因子和多机器学习模型的堆叠可以实现武汉市周边农地流转价格的高效评估,而Orange作为可视化编程平台,不但很好支撑了建模过程,并有效降低了建模门槛。未来,积极推动农地流转市场发育的同时,应进一步更新、完善城市农地基准地价评估方法体系。 展开更多
关键词 农地流转价格 地价评估 多机器学习 集成学习模型 ORANGE
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Urban tree species classification based on multispectral airborne LiDAR
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作者 HU Pei-Lun CHEN Yu-Wei +3 位作者 Mohammad Imangholiloo Markus Holopainen WANG Yi-Cheng Juha Hyyppä 《红外与毫米波学报》 北大核心 2025年第2期211-216,共6页
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services... Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy. 展开更多
关键词 multispectral airborne LiDAR machine learning tree species classification
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Modifying the pore structure of biomass-derived porous carbon for use in energy storage systems
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作者 XIE Bin ZHAO Xin-ya +5 位作者 MA Zheng-dong ZHANG Yi-jian DONG Jia-rong WANG Yan BAI Qiu-hong SHEN Ye-hua 《新型炭材料(中英文)》 北大核心 2025年第4期870-888,共19页
The development of sustainable electrode materials for energy storage systems has become very important and porous carbons derived from biomass have become an important candidate because of their tunable pore structur... The development of sustainable electrode materials for energy storage systems has become very important and porous carbons derived from biomass have become an important candidate because of their tunable pore structure,environmental friendliness,and cost-effectiveness.Recent advances in controlling the pore structure of these carbons and its relationship between to is energy storage performance are discussed,emphasizing the critical role of a balanced distribution of micropores,mesopores and macropores in determining electrochemical behavior.Particular attention is given to how the intrinsic components of biomass precursors(lignin,cellulose,and hemicellulose)influence pore formation during carbonization.Carbonization and activation strategies to precisely control the pore structure are introduced.Finally,key challenges in the industrial production of these carbons are outlined,and future research directions are proposed.These include the establishment of a database of biomass intrinsic structures and machine learning-assisted pore structure engineering,aimed at providing guidance for the design of high-performance carbon materials for next-generation energy storage devices. 展开更多
关键词 Energy storage systems Porous carbon Biomass precursors Pore structure Machine learning-assisted
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