Spatial distribution prediction and benefits assessment of green manure in the Pinggu District,Beijing,based on the CLUE-S model

Green manure use in China has declined rapidly since the 1980 s with the extensive use of chemical fertilizers.The deterioration of field environments and the demand for green agricultural products have resulted in more attention to green manure.Human intervention and policy-oriented behaviors likely have large impacts on promoting green manure planting.However,little information is available regarding on where,at what rates,and in which ways（i.e.,intercropping green manure in orchards or rotating green manure in cropland） to develop green manure and what benefits could be gained by incorporating green manure in fields at the county scale.This paper presents the conversion of land use and its effects at small region extent（CLUE-S） model,which is specifically developed for the simulation of land use changes originally,to predict spatial distribution of green manure in cropland and orchards in 2020 in Pinggu District located in Beijing,China.Four types of land use for planting or not planting green manure were classified and the future land use dynamics（mainly croplands and orchards） were considered in the prediction.Two scenarios were used to predict the spatial distribution of green manure based on data from 2011：The promotion of green manure planting in orchards（scenario 1） and the promotion of simultaneous green manure planting in orchards and croplands（scenario 2）.The predictions were generally accurate based on the receiver operating characteristic（ROC） and Kappa indices,which validated the effectiveness of the CLUE-S model in the prediction.In addition,the spatial distribution of the green manure was acquired,which indicated that green manure mainly located in the orchards of the middle and southern regions of Dahuashan,the western and southern regions of Wangxinzhuang,the middle region of Shandongzhuang,the eastern region of Pinggu and the middle region of Xiagezhuang under scenario 1.Green manure planting under scenario 2 occurred in orchards in the middle region of Wangxinzhuang,and croplands in most regions of Daxingzhuang,southern Pinggu,northern Xiagezhuang and most of Mafang.The spatially explicit results allowed for the assessment of the benefits of these changes based on different economic and ecological indicators.The economic and ecological gains of scenarios 1 and 2 were 175691 900 and143000 300 CNY,respectively,which indicated that the first scenario was more beneficial for promoting the same area of green manure.These results can facilitate policies of promoting green manure and guide the extensive use of green manure in local agricultural production in suitable ways.

Spatio-temporal Dynamic Simulation of Urban Land Use in Karst Areas Based on CLUE-S Model——A Case Study of Dahua Yao Nationality Autonomous County in Guangxi Zhuang Autonomous Region

This article uses TM images in 1999 and 2006 in Dahua County,selects the driving factors having great impact on urban land use change,and conducts data processing using GIS software.It then uses CLUE-S model to simulate land use change pattern in 2006,and uses land use map in 2006 to test the simulation results.The results show that the simulation achieves good effect,indicating that we can use CLUE-S model to simulate the future urban land use change in karst areas,to provide scientific decision-making support for sustainable development of land use.

Spatial structure optimization of mountainous abandoned mine land reuse based on system dynamics model and CLUE-S model

The mountainous abandoned mine land is often distributed in the fomi of fragmented patches. Therefore, it can greatly promote the reuse value of abandoned mine land and relieve the pressure of land demand to realize the rational reuse of abandoned mine land based on the future land use structure and spatial layout of mountainous area. In this paper, optimization of the spatial structure of mountainous abandoned mine land reuse is realized through the system dynamics model and CLUE-S model. Mentougou district, Beijing, China is selected as the research area. System dynamics model with feedback functions is constructed to simulate land use structure from 2011 to 2025, which is taken as the quanfiiative constraint on spatial structure optimization. CLUE-S model with neighborhood analysis function is applied to simulate future land use spatial structure. The simulation result layer is superimposed with the abandoned mine land distribution layer and the optimized spatial structure of abandoned mine land reuse then is determined, checked by reuse suitability evaluation. The result shows that abandoned mine land can be fully optimized as other land use types according to demand, and the reuse directions are water conservancy facilities land, urban land, rural residential land, tourism land, garden land, woodland and grassland. The trend of abandoned mine land reuse tend to be consistent with land use types of neighboring patches. This study can provide theoretical reference for the practices of mountainous abandoned mine land reuse.

东北地区土地覆被格局变化模拟:基于CLUE-S和Markov-CA模型的对比分析

研究以遥感解译的东北地区2000年、2010年、2015年的土地覆被变化为基础,充分考虑自然和社会因素对土地覆被变化的影响,分别通过CLUE-S模型和Markov-CA模型对东北地区2015年和2030年土地覆被格局进行模拟,研究结果表明:通过与遥感解译的2015年实际土地覆被类型数据对比验证,CLUE-S模型和Markov-CA模型模拟结果的总体Kappa指数分别为0.9700和0.9649,结果表明2种模型的模拟结果较为理想,CLUE-S模型的模拟精度较Markov-CA模型更高。2015—2030年东北地区草地、耕地、湿地、其他用地和水体面积呈现下降趋势,林地、人工表面面积呈现增加趋势,人地关系越发紧张。东北地区作为生态环境相对脆弱的区域需警惕不可持续的土地覆被变化,需权衡生态保护、粮食增加与基础设施建设的用地需求和协调发展。

基于CLUE-S模型的浙江省土地利用结构变化趋势分析

土地利用格局变化及未来发展情景对省域土地合理规划和生物多样性保护具有重要意义。以东部典型区域浙江省为例,根据浙江省2013和2018年两期精度500 m×500 m土地利用数据、浙江省陆地国家公园、国家级自然保护区数据,分别设置了土地利用结构和经济社会发展速率维持现状的情景、自然保护地体系建立情景和城市扩张情景。选取高程、坡度、人口等11种影响因素作为驱动因子,采用CLUE-S模型拟合浙江省2018年的土地利用覆被格局并判断拟合精度,进而模拟出三种情景下研究区2030年的土地利用格局演变。结果表明:1)Logistic二元回归分析结果ROC值均大于0.699,表明所选驱动因子对土地利用类型的解释能力较为准确。2)Kappa系数高达0.9460,表明模型能够准确的模拟浙江省土地利用分布格局;3)与2018年浙江省土地利用情况相比,2030年在不同情景下土地利用面积与空间分布均发生了一定的变化,土地利用结构和经济社会发展维持现状情景下建设用地持续扩张,建设用地面积增加16575 hm^(2),显著增加的区域主要位于杭州市郊和温州市郊。自然保护地体系建立情景下,林地、水域分别增加了15625、1600 hm^(2),耕地、草地和建设用地分别减少了1550、1700和13975 hm^(2),空间上都呈现均匀变化。城市扩张情景下,建设用地面积迅速增加145800 hm^(2),增加部分多数位于原有建设用地的外围。三种情景对比下,自然保护地体系建立情景是浙江省2030年最适宜的土地利用土地覆被格局。2030年浙江省在自然保护地体系建立情景下生态空间的面积最大,为6741925 hm^(2),年均增加速度为0.0192%。

基于CLUE-S模型的吴忠市土地利用时空演变及多情景模拟预测

【目的】研究不同情景下的未来土地利用时空演变格局,为合理利用土地资源、科学规划国土空间开发格局和区域高质量发展提供助力。【方法】研究团队以宁夏回族自治区吴忠市为研究对象,选取2000年、2010年和2020年3期土地利用数据,运用土地利用转移矩阵、CLUE-S模型,分析研究区2000—2020年土地利用变化特征,模拟自然增长情景、生态保护情景和经济增长情景下2030年吴忠市土地利用演变特征。【结果】1)2000—2020年吴忠市草地面积占比最大,其次是耕地和未利用地,建设用地面积不断增长;建设用地面积变化最快,并以耕地转化为建设用地为主。2)2030年建设用地面积在自然增长情景下将会减少,生态保护情景下林地面积增幅较大,经济增长情景下各土地利用类型变化较小。3)在3种情景下,吴忠市2030年土地利用结构仍然以耕地、林地、草地为主,建设用地面积在生态保护情景和经济增长情景下将呈增长的趋势,在自然增长情景下将呈减少趋势。【结论】生态保护情景下的预测结果更符合国土空间规划要求和经济社会发展的需求。该研究成果对推动吴忠市未来土地利用格局规划具有重要意义。

基于CLUE-S和Markov模型的土地利用变化模拟预测

土地利用变化过程是极其复杂的,主要是因为其在变化的过程中不仅仅是单一因素作用的结果,而是多种因素掺杂在一起形成的复杂过程。在经济快速发展的今天,一旦区域土地利用存在不合理的情况,极容易影响区域资源可持续利用以及和谐稳定的状态,土地利用变化模拟预测可以改善土地利用结构、提高土地利用效率,帮助相关部门规划国土空间布局,缓解人地矛盾,实现自然资源可持续发展。本文是基于高分辨率遥感影像解译得到靖远县2期土地利用数据,并利用CLUE-S与Markov模型相结合的方法对靖远县未来土地利用变化从自然发展、耕地保护和生态安全三种情景进行预测。结果表明,(1)CLUE-S模型相对于小尺度的土地利用变化模拟预测的效果较好,精度较高。(2)建设用地在自然发展情景下的增加是最为显著的,在其他两种情景下受到限制因素后在减少,草地和耕地只有在自然发展情景下是减少,林地只在耕地保护情景下是减少,水域用地和未利用地都在自然发展情景下是增加的。(3)生态安全情景与耕地保护情景对未来土地利用变化的调控效果较好,林地、草地和耕地受到了更好的保护。研究表明,区域土地利用模拟预测可以更好地为土地利用规划编制提供参考。

Simulation of Land-use Scenarios for Beijing Using CLUE-S and Markov Composite Models

This study investigated and simulated land use patterns in Beijing for the year 2000 and the year 2005 from the actual land use data for the year 1995 and the year 2000,respectively,by combining spatial land allocation simulation using the CLUE-S model,and numerical land demand prediction using the Markov model.The simulations for 2000 and 2005 were confirmed to be generally accurate using Kappa indices.Then the land-use scenarios for Beijing in 2015 were simulated assuming two modes of development:1) urban development following existing trends;and 2) under a strict farmland control.The simulations suggested that under either mode,urbanized areas would expand at the expense of land for other uses.This expansion was predicted to dominate the land-use conversions between 2005 and 2015,and was expected to be accompanied by an extensive loss of farmland.The key susceptible to land-use changes were found to be located at the central urban Beijing and the surrounding regions including Yanqing County,Changping District and Fangshan District.Also,the simulations predicted a considerable expansion of urban/suburban areas in the mountainous regions of Beijing,suggesting a need for priority monitoring and protection.

基于CLUE-S和InVEST模型的苏州市生境质量评估及预测

评估城市化进程中土地利用变化下的生境质量演变,对于构建综合生态规划体系、应对全球可持续发展的重大挑战具有重要意义。基于苏州市2010年和2018年遥感影像解译的土地利用数据和社会经济等数据,选取高程、坡度、到公路的距离、人口密度等驱动因子,采用CLUE-S模型模拟多情景下2030年土地利用变化,并通过生态系统服务评估与权衡(InVEST)模型评估过去并预测未来的生境质量时空演变,探讨土地利用变化对生境质量的影响。结果表明:最优拟合尺度为400 m×400 m,Kappa系数达到0.8545,模型能够很好地模拟研究区未来的土地利用格局;苏州市2030年生态保护情景下的生境质量优于2018年,而2030年自然增长情景下的生境质量差于2010年和2018年;从空间分布看,苏州市中心城区、工业较集中和人口活动强度大的地区生境质量差,太湖周边等水系发达地区生境质量较好。因此,未来研究区在发展经济的同时,更要注重生态保护,实现高质量发展。

CLUE-S模型在生态景观空间规划仿真中的应用

生态景观是城市建设中不可或缺的一部分,如果规划不合理会导致土地面积利用率不高。为此提出基于CLUE-S模型的生态景观空间规划优化方法。通过土地利用转移网络分析景观土地转移方向,并通过景观土地动态度分析模型描述景观土地在规划过程中的变化速度。分析生态景观空间内各个景观规划要素对应的属性及其属性的空间相关特征,从斑块识别和廊道规划角度出发规划生态景观空间,运用CLUE-S模型模拟空间土地布局,求解空间规划中土地的面积,实现对生态景观空间规划结果的优化。实验结果表明,所提方法的Kappa指数高,说明其规划结果和实际景观之间具有一致性,上述方法的规划效果较好。

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

基于CLUE-S情景模拟的挠力河流域耕地适宜性评价研究

以挠力河流域为研究区,选择2000、2010、2020年3期土地利用数据,从土地利用动态度和转移矩阵两方面分析土地利用时空变化,运用CLUE-S模型对研究区2030年土地利用变化格局进行模拟验证,设基线、农业发展和生态保护3种情景预测2030年土地利用变化格局,最后对3种情景下耕地适宜性进行评价。结果表明:研究区土地利用类型以耕地为主,2000—2020年森林、湿地、水体和人造地表呈增加趋势,耕地和草地呈减少趋势。2000—2020年土地利用转移主要发生在耕地、草地和森林之间,2000—2010年草地面积转入最多,耕地次之,森林最少,转出面积相反;2010—2020年森林转入转出面积最多,草地次之,耕地最少。CLUE-S模型对挠力河流域土地利用变化具有良好的模拟能力,Kappa系数为0.894,模拟总体精度为91.18%;基线情景下耕地、草地和水体地类面积减少,其他地类面积增加;农业发展情景下耕地面积增加23.68%,人造地表面积未发生变化,其他地类面积均较少;生态保护情景下与农业发展情景正好相反。依评价指标体系和评价模型,分析计算出3种情景下耕地位于适宜区的面积占研究区总面积的96%以上,不适宜区占比低于4%。研究结果可为加强未来耕地质量建设与管理、落实耕地保护目标和科学管理土地资源提供科学决策。

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model

The Conversion of Land Use and its Effects at Small regional extent （CLUE-S） model is a widely used method to simulate land use change. An ordinary logistic regression model was integrated into the CLUE-S model to identify explanatory variables without considering the spatial autocorrelation effect. Using image-derived maps of the Changsha- Zhuzhou-Xiangtan urban agglomeration, the CLUE-S model was integrated with the ordinary logistic regression and autologistic regression models in this paper to simulate land use change in 2000, 2005 and 2009 based on an observation map from 1995. Significant positive spatial autocorrelation was detected in residuals of ordinary logistic models. Some variables that were much more significant than they should be were selected. Autologistic regression models, which used autocovariate incorporation, were better able to identify driving factors. The Receiver Operating Characteristic Curve （ROC） values of autologistic regression models were larger than 0.8 and the pseudo R^2 values were improved, compared with results of logistic regression model. By overlapping the observation maps, the Kappa values of the ordinary logistic regression model （OL）-CLUE-S and autologistic regression model （AL）-CLUE-S models were larger than 0.75. The results showed that the simulation results were indeed accurate. The Kappa fuzzy （Kfuzzy） values of the AL-CLUE-S models （0.780, 0.773, 0.606） were larger than the values of the OL-CLUE-S models （0.759, 0.760, 0.599） during the three periods. The AL-CLUE-S models performed better than the OL-CLUE-S models in the simulation of land use change. The results showed that it is reasonable to integrate autocovariates into CLUE-S models. However, the Kfuzzy values decreased with prolonged duration of simulation and the maximum range of time was not discussed in this paper.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Anisotropic time-dependent behaviors of shale under direct shearing and associated empirical creep models

Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,30°,45°,60°,and 90°),under multiple levels of direct shearing for the first time.The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior.Under a low shear stress,the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations,and the increase depends on the bedding orientation and creep time.Under high shear stress conditions,the creep compliance of shale is minimal when the bedding orientation is 0°,and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°,45°,60°,and 90°.The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation.A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed.The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate.The applicability of the five classical empirical creep models is quantitatively evaluated.It shows that the logarithmic model can well explain the experimental creep strain and creep rate,and it can accurately predict long-term shear creep deformation.Based on an improved logarithmic model,the variations in creep parameters with shear stress and bedding orientations are discussed.With abovementioned findings,a mathematical method for constructing an anisotropic shear creep model of shale is proposed,which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.