改进灰狼算法优化GBDT在PM_(2.5)预测中的应用

针对灰狼算法易陷入局部最优解和全局搜索能力不足的问题,通过霍尔顿序列(Halton Sequence)搜索算法初始化狼群位置,避免灰狼算法陷入局部最优解和重复运算;引入莱维飞行和随机游动策略对灰狼算法的寻优过程进行优化,以增加算法的全局搜索能力;利用粒子群算法模拟灰狼种群得出的最佳适应度以用于惩罚项改进灰狼算法中的头狼更新策略。使用改进算法优化的梯度提升树(Gradient Boosting Decision Trees,GBDT)模型对北京市大气污染物监测数据中PM_(2.5)质量浓度进行预测,采用3种评估函数对各模型以及混合模型预测效果得分进行评估。结果显示,本文改进的灰狼算法对梯度提升树的优化效果优于其他算法,均方根误差E RMS为6.65μg/m^(3),平均绝对值误差E MA为3.20μg/m^(3),拟合优度(R^(2))为99%,比传统灰狼算法优化结果的均方根误差减少了19.19μg/m^(3),平均绝对值误差降低了10.03μg/m^(3),拟合优度增加了9百分点;与霍尔顿序列和莱维飞行改进的(Levy Flight-Halton Sequence,LHGWO)相比,改进的灰狼算法预测得分的均方根误差降低了10.39μg/m^(3),平均绝对值误差减小了6.71μg/m^(3),拟合优度提高了5百分点。研究表明了预测模型优化的有效性,为未来城市改善空气质量提供了科学依据和技术支持。

基于GBDT算法的基桩竖向承载力预测方法

目的为研究支撑-半刚接钢框架结构体系的抗震性能,方法设计了一榀由嵌套式单边螺栓与T型钢构成的半刚性梁柱节点的中心支撑钢框架,并进行了拟静力试验与有限元数值模拟,通过观测整个试验现象,分析了其滞回、承载力、刚度退化、耗能等抗震指标。结果结果表明:试件破坏过程明显经历了弹性段、塑性段、破坏段三个阶段,试件破坏模式主要为支撑受压失稳破坏,塑性变形主要累积在支撑体系上,整体呈现延性破坏特征;支撑断裂后,梁柱及T型钢节点无明显塑性变形,钢框架仍具有较高的安全储备,符合“强节点、弱构件”设计原则,表明了结构具有两道抗震防线;结论支撑与半刚接钢框架协同工作使得试件具有较高的抗侧刚度抵抗水平变形,且承载力较高、滞回性能稳定、耗能能力优良;单边螺栓在试验过程中的受力性能较普通高强螺栓并无较大差别,未出现严重的预紧力松弛现象,并能高效的保持螺栓预紧力。通过有限元数值模拟分析可知,减小支撑长细比,虽能有效提高结构的抗震性能,但长细比较小会导致支撑刚度增大,加速其余构件的损坏。故应以考虑结构的延性为前提,降低支撑的长细比,才能有效提高结构的抗震性能。

基于TrAdaBoost-GBDT模型的排土场边坡稳定状态判别

针对露天矿排土场失稳数据获取困难,样本数据量少等问题,提出基于迁移学习算法的露天矿排土场边坡稳定状态判别模型;结合陕西省F露天矿排土场边坡的实际地质条件和降雨情况,设计降雨条件下排土场不同土石混合比边坡的相似模拟试验方案,并采集和处理试验中边坡模型的含水率、土压力和孔隙水压力数据;考虑到小样本数据集对梯度提升回归树(GBDT)模型分类精度的影响,运用迁移学习思想,利用迁移自适应增强算法(TrAdaBoost)对源域数据集和目标域数据集样本权重进行迭代更新,以GBDT模型作为数据样本训练的弱学习器,最终根据弱学习器的分类结果,通过加权多数表决法生成一种基于迁移学习的TrAdaBoost-GBDT排土场边坡稳定状性判别模型,以提高小样本数据标签类别的判别准确率。结果表明:相对其他算法模型,提出的排土场边坡稳定状态判别模型在稳定状态判别上有更好的表现,准确率、精准率、召回率和曲线下面积值(AUC)分别达到93.3%、87.5%、100%和93.8%,能够作为边坡稳定状态判别的分类器。该模型相对其他算法模型可以提高小样本数据集的边坡稳定状态判别的准确性,弥补机器学习对小样本数据集分类结果精度较低的不足。

基于GBDT的多时刻航班过站关键节点时间预测模型

为准确预测离港、起飞等航班过站关键节点时间,提高繁忙机场运行效率,本文提出一种基于梯度提升决策树(GBDT,gradient boosting decision tree)的多时刻航班过站关键节点时间预测模型。首先,按产生时刻划分航班信息数据项类别;其次,基于GBDT算法和Spark平台分别构建不同过站时刻的航班过站关键节点时间预测模型;最后,以实时计算方式获取和处理航班数据,实现在多个时刻对航班离港时间和起飞时间进行动态预测。实验结果表明,该模型具有良好的预测表现,并与其他算法进行对比,预测效果最优,±15 min内预测准确率达到95.6%。

基于聚类和GBDT的镀锌钢卷力学性能预测

热镀锌钢卷力学性能影响因素之间关系复杂,限制了模型精度的提升。采用k-means算法利用化学成分属性对镀锌钢卷数据集进行聚类,将数据聚成三种模式簇实现样本的优选。利用梯度提升树算法,开展各模式数据集与不划分模式的全数据集下的力学性能建模研究,最后结合网格搜索与交叉验证方法进行模型参数优化。研究结果表明,分模式下模型MAE误差相比于全数据集建模平均减小0.85 MPa。参数优化后,各模式下MAE误差平均减少5.19 MPa,RMSE误差平均减少3.63 MPa,提高了预测模型精度。

Parkland trees on smallholder farms ameliorate soil physical-chemical properties in the semi-arid area of Tigray,Ethiopia

Proposed agroforestry options should begin with the species that farmers are most familiar with,which would be the native multipurpose trees that have evolved under smallholder farms and socioeconomic conditions.The African birch(Anogeissus leiocarpa(DC.)Guill.&Perr.)and pink jacaranda(Stereospermum kunthianum Cham.)trees are the dominant species in the agroforestry parkland system in the drylands of Tigray,Ethiopia.Smallholder farmers highly value these trees for their multifunctional uses including timber,firewood,charcoal,medicine,etc.These trees also could improve soil fertility.However,the amount of soil physical and chemical properties enhanced by the two species must be determined to maintain the sustainable conservation of the species in the parklands and to scale up to similar agroecological systems.Hence,we selected twelve isolated trees,six from each species that had similar dendrometric characteristics and were growing in similar environmental conditions.We divided the canopy cover of each tree into three radial distances:mid-canopy,canopy edge,and canopy gap(control).At each distance,we took soil samples from three different depths.We collected 216 soil samples(half disturbed and the other half undisturbed)from each canopy position and soil depth.Bulk density(BD),soil moisture content(SMC),soil organic carbon(SOC),total nitrogen(TN),available phosphorus(AP),available potassium(AK),p H,electrical conductivity(EC),and cation exchange capacity(CEC)were analysed.Results revealed that soil physical and chemical properties significantly improved except for soil texture and EC under both species,CEC under A.leiocarpus,and soil p H under S.kunthianum,all the studied soils were improved under both species canopy as compared with canopy gap.SMC,TN,AP,and AK under canopy of these trees were respectively 24.1%,11.1%,55.0%,and 9.3% higher than those soils under control.The two parkland agroforestry species significantly enhanced soil fertility near the canopy of topsoil through improving soil physical and chemical properties.These two species were recommended in the drylands with similar agro-ecological systems.

Tree mycorrhizal associations determine how biodiversity,large trees,and environmental factors drive aboveground carbon stock in temperate forests

Biodiversity,large trees,and environmental conditions such as climate and soil have important effects on forest carbon stocks.However,recent studies in temperate forests suggest that the relative importance of these factors depends on tree mycorrhizal associations,whereby large-tree effects may be driven by ectomycorrhizal(EM)trees,diversity effects may be driven by arbuscular mycorrhizal(AM)trees,and environment effects may depend on differential climate and soil preferences of AM and EM trees.To test this hypothesis,we used forest-inventory data consisting of over 80,000 trees from 631 temperate-forest plots(30 m×30 m)across Northeast China to examine how biodiversity(species diversity and ecological uniqueness),large trees(top 1%of tree diameters),and environmental factors(climate and soil nutrients)differently regulate aboveground carbon stocks of AM trees,EM trees,and AM and EM trees combined(i.e.total aboveground carbon stock).We found that large trees had a positive effect on both AM and EM tree carbon stocks.However,biodiversity and environmental factors had opposite effects on AM vs.EM tree carbon stocks.Specifically,the two components of biodiversity had positive effects on AM tree carbon stocks,but negative effects on EM tree carbon stocks.Environmental heterogeneity(mean annual temperature and soil nutrients)also exhibited contrasting effects on AM and EM tree carbon stocks.Consequently,for the total carbon stock,the positive large-tree effect far surpasses the diversity and environment effect.This is mainly because when integrating AM and EM tree carbon stock into total carbon stock,the opposite diversity-effect(also environment-effect)on AM vs.EM tree carbon stock counteracts each other while the consistent positive large-tree effect on AM and EM tree carbon stock is amplified.In summary,this study emphasized a mycorrhizal viewpoint to better understand the determinants of overarching aboveground carbon profile across regional forests.

Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

基于GBDT的望奎县农田土壤有机碳主控因子研究

利用多源公开数据,针对于2012年搜集到的农田土壤有机碳(SOC)数据,通过梯度增强决策树(GBDT)探究SOC的主控因子,揭示SOC空间分异机理.选择国家商品粮食生产基地县和全国产量大县-望奎县作为研究区域,结果表明,GBDT模型相较于一元线性回归模型在所有的自变量上均具有更好的预测效果,总体的R^(2)为0.958,表明该模型能够很好地解释目标变量的变异;碱解氮、有效磷、气温、土壤微生物多样性、速效钾、土壤pH和降水量依次是影响有机碳含量最为显著的几个因素,其中碱解氮以33.85%的影响权重位居最高;7个环境协变量均观察到对SOC的阈值效应,且均具有两个阈值.研究发现任意两个变量都不是独立的,均存在相互作用的负值效应.表明环境变量对SOC含量的影响是一个复杂的交互作用,而不是简单的叠加.侧面证明了过度施肥并不会达到增加土壤肥力的作用,而会造成资源浪费和农田生态污染问题.

Enhanced Cooling Efficiency of Urban Trees on Hotter Summer Days in 70 Cities of China

Increasing the urban tree cover percentage(TCP) is widely recognized as an efficient way to mitigate the urban heat island effect. The cooling efficiency of urban trees can be either enhanced or attenuated on hotter days, depending on the physiological response of urban trees to rising ambient temperature. However, the response of urban trees' cooling efficiency to rising urban temperature remains poorly quantified for China's cities. In this study, we quantify the response of urban trees' cooling efficiency to rising urban temperature at noontime [~1330 LT(local time), LT=UTC+8] in 17summers(June, July, and August) from 2003–19 in 70 economically developed cities of China based on satellite observations. The results show that urban trees have stronger cooling efficiency with increasing temperature, suggesting additional cooling benefits provided by urban trees on hotter days. The enhanced cooling efficiency values of urban trees range from 0.002 to 0.055℃ %-1 per 1℃ increase in temperature across the selected cities, with larger values for the lowTCP-level cities. The response is also regulated by background temperature and precipitation, as the additional cooling benefit tends to be larger in warmer and wetter cities at the same TCP level. The positive response of urban trees' cooling efficiency to rising urban temperature is explained mainly by the stronger evapotranspiration of urban trees on hotter days.These results have important implications for alleviating urban heat risk by utilizing urban trees, particularly considering that extreme hot days are becoming more frequent in cities under global warming.

Exploring Trees Diversity and Ecological Value of Mountain Forests in the Blue Nile Region of Sudan

We carried out this research at Abu-Gaddaf Natural Forest Reserve (ANFR) which is located east of Blue Nile River, in Blue Nile State, Sudan. It aims at exploring tree composition assessing their diversity indices, and ecological importance values. For survey of types and estimation of density of tree species in the forest, we randomly distributed 97 circular sample plots (0.1 hectare (ha)). In each sample plot we identified all trees to the species level, recorded their frequencies and computed species diversity and importance value indices (IVI). A total of 13 tree species, which belong to 12 genera and nine families, were identified in the tree layer of the forest. Mean tree density in ANFR was 116 trees/ha, composed mainly of Boswellia papyrifera (Del.) Hochst. (48), followed by Combretum hartmannianu (19) and Lannea fruticosa (18). Fabaceae was the most common family followed by Combretaceae and Malvaceae. B. papyrifera Delile Hochst was the most abundant while Acacia seyal was the least abundant species. Species richness (R = 1.71), evenness (E = 0.69), dominance (0.24) indices and Simpson’s Index of Diversity (D' = 0.76) suggest a moderate diversity, moderate numbers of individuals in each species and a moderate community stability. The research provides empirical results on diversity and ecological importance value of species, signifies the urging need to safeguard threatened species and to prioritize them for conservation, as well as the need to promote management of abundant species to provide multiple forest ecosystem services.

Enhancing PDF Malware Detection through Logistic Model Trees

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

Seedling potential of trees species along the elevational gradient in temperate hill forest of central Nepal

Regeneration status of tree species along elevation gradient in temperate hill forest was not understood greatly.Present research examined the tree diversity and its regeneration patterns along an elevation gradient in temperate hill forest,central Nepal.Data were collected from 300 sample plots within vertical elevation bands of 10,ranging from 1365 to 2450 m asl.A random sampling method was used for data collection in three seasons,winter,pre-monsoon and post monsoon seasons.Diameter at breast height(DBH)was used to broadly categorize the plant individual into trees,saplings and seedlings.The tree species richness ranged from 12 to 25 with density of 350 to 1200 individuals per hectare.Species richness of tree and sapling showed statistically significant unimodal pattern,which peaked at mid-elevation.Elevation showed a strong and positive linear correlation with the seedling density(Deviance=0.99,p<0.001)and a significant hump-shaped relationship with sapling density(Deviance=0.95,p<0.001).Similarly,elevations showed a statistically significant negative hump-shaped relationship with all trees,saplings and seedling stages(Deviances=0.89,0.87 and 0.57).The highest values of the Shannon-Wiener index and the lowest value of the Simpson index were found at mid-elevation for all growth forms.Nearly 92%of tree species were found at regenerating stage;49%in a good renewal regeneration status,32%in fair renewal regeneration,and 11%at a poor regenerating condition.Nevertheless,4%of tree species were reported as non-regenerating stages and 4%were newly introduced species.Hence,the regeneration status of the study area was considered fairly well since sapling(78.5%)>seedling(10.6%)≤mature(10.9%).Among tested environmental variables,elevation and annual mean rainfall were the most influential factors in the regeneration of tree species.

Efficiency of Bio-Fertilizing as One of the Natural Alternatives to Improve the Growth of Khaya senegalensis and Swietenia mahagoni Trees and for Sustainability

A field experiment was carried out at Ismailia Research Station, Ismailia Governorate from 2020-2022 to improve the growth of Khaya senegalensis and Swietenia mahagoni by using a combination of mineral fertilizer (NPK) and biological fertilizer (Azotobacter chroococcum, Bacillus megatherium, and Bacillus circulant) as recommended dose under new sandy soils conditions. Split plot designed with four treatments (Control, (50% Mineral fertilizer (M.) + 50% Biological fertilizer (Bio.)), 100% M. and 100% Bio.) of each species. Vegetative growth, leaf area, tree biomass, stored carbon, basal area, tree volume, and in the soil both of microbial account and mineral content were determined. The experimental results showed no significant differences between studied species among the most studied parameters except for Khaya senegalensis which gave the highest significant difference in root biomass and below-stored carbon than Swietenia mahagoni. Evidently, the highest significant growth parameters were 100% mineral fertilizer followed by (50% M. + 50% Bio.) as compared with control. No significant difference between 100% M. and (50% M. + 50% Bio.) of shoot dry biomass (15.19 and 12.02 kg, respectively) and above-stored carbon (0.28 and 0.22 Mt, respectively). Microbial account and mineral content in soil were improved after cultivation of tree species compared to before planting and control, especially with 50% mineral fertilizer and 50% bio-fertilizer treatment. In conclusion, a treatment containing 50% mineral fertilizer and 50% bio-fertilizer has led to the ideal Khaya senegalensis and Swietenia mahagoni growth in sandy soil for cheaper and sustainable.

Long-term changes in radial growth of seven tree species in the mixed broadleaf-Korean pine forest in Northeast China:Are deciduous trees favored by climate change?

The role of the temperate mixed broadleaf-Korean pine forest(BKF)in global biogeochemical cycles will depend on how the tree species community responds to climate;however,species-specific responses and vulner-abilities of common trees in BKF to extreme climates are poorly understood.Here we used dendrochronological meth-ods to assess radial growth of seven main tree species(Pinus koraiensis,Picea jezoensis,Abies nephrolepis,Fraxinus mandshurica,Phellodendron amurense,Quercus mongolica,and Ulmus davidiana)in an old-growth BKF in response to climate changes in the Xiaoxing’an Mountains and to improve predictions of changes in the tree species compo-sition.Temperature in most months and winter precipita-tion significantly negatively affected growth of P.jezoensis and A.nephrolepis,but positively impacted growth of P.koraiensis and the broadleaf species,especially F.mandshu-rica and U.davidiana.Precipitation and relative humidity in June significantly positively impacted the growth of most tree species.The positive effect of the temperature during the previous non-growing season(PNG)on growth of F.mandshurica and Q.mongolica strengthened significantly with rapid warming around 1981,while the impact of PNG temperature on the growth of P.jezoensis and A.nephrolepis changed from significantly negative to weakly negative or positive at this time.The negative response of radial growth of P.jezoensis and A.nephrolepis to precipitation during the growing season gradually weakened,and the negative response to PNG precipitation was enhanced.Among the studied species,P.koraiensis was the most resistant to drought,and U.davidiana recovered the best after extreme drought.Ulmus davidiana,P.jezoensis and A.nephrolepis were more resistant to extreme cold than the other species.Climate warming generally exacerbated the opposite growth patterns of conifer(decline)and broadleaf(increase)spe-cies.Deciduous broadleaf tree species in the old-growth BKF probably will gradually become dominant as warming continues.Species-specific growth-climate relationships should be considered in future models of biogeochemical cycles and in forestry management practices.

基于贝叶斯优化GBDT的转炉炼钢终点预测

为提高转炉炼钢终点碳含量和温度预报精度,提出基于贝叶斯优化梯度提升决策树(BOA_GBDT)的转炉炼钢终点碳含量和温度预测模型,将其与基础模型径向基函数(RBF)、支持向量机(SVM)、梯度提升决策树(GBDT)以及贝叶斯优化的径向基函数(BOA_RBF)、支持向量机(BOA_SVM)终点碳温预测模型对比分析。实验结果表明:BOA_GBDT各项误差指标最小,命中率最高,终点时刻碳含量在±0.01%误差区间内命中率为96.2%;终点温度在±10℃误差区间内命中率为92.1%。贝叶斯优化算法能够显著提升模型性能,更准确地判断转炉炼钢终点碳含量和温度,为吹炼出符合要求的钢水提供较为可靠的依据。

基于GBDT特征提取与Tent-ASO-BP网络的铣刀磨损量预测

为了提高机械加工过程中小样本刀具磨损量监测的准确性,提出一种基于Pearson+GBDT特征提取、Tent混沌映射和原子搜索算法(ASO)优化BP神经网络(Tent-ASO-BP)的刀具磨损量预测模型。针对BP神经网络特征选择及参数选择难题,提出了基于Pearson+GBDT的双层过滤式特征筛选方式求取网络输入特征,并使用Tent混沌映射改进原子搜索算法(ASO)对BP神经网络最优权值和阈值进行求解。通过实验证明:Tent混沌映射改善了ASO,避免ASO陷入局部极值和过早收敛,即通过交叉验证证明Tent-ASO优化BP神经网络训练模型精度较ASO高。同时,验证了梯度提升决策树(GBDT)能够筛选出用于刀具磨损值映射的一组特征,且特征筛选能力强于同类算法Light GBM、Catboost、决策树、随机森林。

Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees

This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from NCHRP Report-547,the model was trained and rigorously tested.Performance metrics,specifically RMSE,MAE,and R2,were employed to assess the model's predictive accuracy,robustness,and generalisability.When benchmarked against well-established models like support vector machines(SVM)and gaussian process regression(GPR),the AHA-boosted model demonstrated enhanced performance.It achieved R2 values of 0.997 in training and 0.974 in testing,using the traditional Witczak NCHRP 1-40D model inputs.Incorporating features such as test temperature,frequency,and asphalt content led to a 1.23%increase in the test R2,signifying an improvement in the model's accuracy.The study also explored feature importance and sensitivity through SHAP and permutation importance plots,highlighting binder complex modulus|G*|as a key predictor.Although the AHA-boosted model shows promise,a slight decrease in R2 from training to testing indicates a need for further validation.Overall,this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete,making it a valuable asset for pavement engineering.

A Light and Simplified Branch Bending Method for Young Pear Trees

Aiming at high cost and low efficiency of conventional branch bending method in the modern intensive planting and labor-saving cultivation mode of young pear trees,this paper provides a new branch bending method with wide source of raw materials,cheap price and simple operation,which is also suitable for the management of low-age branches in the process of high grafting and upgrading of traditional big trees.

Insights from i-Tree Eco-efficiency Assessment Management of Urban Trees in Oxford,UK

In the era of stock development following the acceleration of urbanization,the revitalization of urban green space has assumed an increasingly significant role.Consequently,the management of urban trees has emerged as a critical focus of urban governance,contributing to the enhancement of livability in human settlements.This study offers a comprehensive analysis of the urban tree management system in Oxford,UK,identifying that its primary objective is to optimize and maintain a harmonious balance between human activities and the natural environment through the implementation of high-quality planting practices.The system emphasizes enhanced management practices and establishes a robust framework for the development of targeted policies and management regulations,utilizing i-Tree eco-efficiency assessment and real-time feedback mechanisms.China’s urban tree management is in its nascent stages,and there is an urgent need for the development of urban green space.By adopting the refined management assessment methodologies employed for urban trees in Oxford,UK,it is possible to enhance the ecological value of urban trees,which represent a significant green resource within cities,and contribute to the creation of more livable urban spaces.