Spatiotemporal evolution and prediction of habitat quality in Hohhot City of China based on the InVEST and CA-Markov models

With the acceleration of urbanization,changes in the urban ecological environment and landscape pattern have led to a series of prominent ecological environmental problems.In order to better coordinate the balanced relationship between city and ecological environment,we selected land use change data to evaluate the habitat quality in Hohhot City of China,which is of great practical significance for regional urban and economic development.Thus,the integrated valuation of ecosystem services and tradeoffs(InVEST)and Cellular Automata-Markov(CA-Markov)models were used to analyze,predict,and explore the Spatiotemporal evolution path and characteristics of urban land use,and forecast the typical evolution pattern of land use in 2030.The results showed that the land use types in Hohhot City changed significantly from 2000 to 2020,and the biggest change took place in cultivated land,grassland,shrub,and artificial surface.The decrease of cultivated land area and the increase of artificial surface area were the main impact trend of land use change.The average value of habitat quality had been decreasing continuously from 2000 to 2020,and the values of habitat degradation were 0.2605,0.2494,and 0.2934 in 2000,2010,and 2020,respectively,showing a decreasing trend.The decrease of habitat quality was caused by the needs of economic development and urban construction,as well as the impact of land occupation.During this evolution,many cultivated land and urban grassland had been converted into construction land.The simulated land use changes in 2030 are basically the same as those during 2000-2020,and the habitat quality will still be declining.The regional changes are influenced by the urban rapid development and industrial layout.These results can provide decision-making reference for regional urban planning and management as well as habitat quality evaluation.

The simulation of LUCC based on Logistic-CA-Markov model in Qilian Mountain area, China

The Qilian mountain area was examined for using the Logistic-CA-Markov coupling model combined with GIS spatial analyst technology to research the transformation of LUCC, driving force system and simulate future tendency of variation. Results show that： （1） Woodland area decreased by 12.55%, while grassland, cultivated land, and settlement areas increased by 0.22%, 7.92%, and 0.03%, respectively, from 1986 to 2014. During the period of 1986 to 2000, forest degradation in the middle section of the mountain area decreased by 1,501.69 km2. Vegetation cover area improved, with a net increase of grassland area of 38.12 km2 from 2000 to 2014. （2） For constructing the system driving force, the best simulation scale was 210m×210m. Based on logistic regression analysis, the contribution （weight） of composite driving forces to land use and cover change was obtained, and the weight value was more objectively compared with AHP and MCE method. （3） In the natural scenarios, it is predicted that land use and cover distribution maps of Qilian mountain area in 2028 and 2042, and the Lee-Sallee index test was adopted. Over the next 27 years （2015-2042）, farmland, woodland, grassland, settlement areas show an increasing trend, especially settlements with an obvious change of 0.56%. The area of bare land will decrease by 0.89%. Without environmental degradation, tremendous structural change of LUCC will not occur, and typical characteristic of the vertical zone of the mountain would remain. Farmland and settlement areas will increase, but only in the vicinity of Qilian and Sunan counties.

基于InVEST与CA-Markov模型的宝鸡市碳储量时空演变与预测

土地利用类型的变化是影响陆地生态系统碳储量变化的重要因素,分析区域未来土地利用格局对碳储量的影响、对维持碳平衡具有重要意义.基于宝鸡市2000—2020年土地利用数据,运用InVEST模型和CA-Markov模型,对2000—2020年宝鸡市不同土地利用类型碳储量的变化特征进行分析,并对2030年不同情景下宝鸡市土地利用变化及碳储量变化情况进行了预测.研究表明:①2000—2020年宝鸡市建设用地面积不断增加,耕地、林地和草地为主要土地利用类型.②2000—2020年,宝鸡市碳储量共损失0.42×10^(6)t,耕地和林地面积减少是其碳储量减少的主要原因.③2020—2030年自然发展情景下宝鸡市碳储量损失最多,耕地保护情景和生态保护情景下宝鸡市碳储量相差不大,控制城市扩张情景下宝鸡市碳储量损失最少、固碳能力最强.因此,未来进行国土空间规划时,可统筹考虑控制城市扩张情景、生态保护情景和耕地保护情景,以确保在粮食供给和生态安全的基础上,提高研究区的碳储量.

基于动态稳定性验证和CA-Markov预测的耕地时空演变特征--以赣榆区为例

为阐明耕地变化动态稳定性验证的重要性,预测“十四五”目标年(2025年)与远景目标年(2035年)的耕地变化,本研究运用耕地面积变异系数、地理集中指数、不平衡指数及趋势分析法,从耕地数量、空间分布与空间发展趋势三个方面,对江苏省连云港市赣榆区耕地变化的动态稳定性进行验证,并运用CA-Markov模型对未来不同政策情境下的耕地变化情况进行模拟。结果表明,2009-2016年赣榆区耕地总量持续下降,未存在较大反复与波动,耕地空间分布较为均衡,耕地占用与补充的空间发展趋势较为连续,总体上具有较高的动态稳定性,符合运用CA-Markov模型的前提条件。预测在各种政策情境下,2025年之前赣榆区将经历快速的耕地非农化过程,至2035年耕地减少的速率有所放缓,具有区位优势的赣榆北部地区将是未来耕地非农化的重点区域,耕地将逐渐向南部内陆地区转移。研究表明,对耕地变化的动态稳定性进行验证可以有效保证CA-Markov模型的模拟精度,赣榆区未来的耕地保护形势不容乐观,应合理协调经济发展与耕地保护之间的关系,保障社会、生态与粮食安全。

改进CA-Markov模型的城市土地空间结构演变预测

城市土地空间结构演变预测需要大量的数据支持,现阶段预测手段存在预测精度不佳的问题,为此提出GIS技术下城市土地空间结构演变预测方法。通过GIS技术获取城市土地空间结构数据,并对其进行结构演变分析;通过人工神经网络模型、轮盘竞争机制和惯性系数对CA模型实行优化处理;将改进CA模型与Markov结构结合,建立改进CA-Markov模型,根据城市土地空间结构演变分析结果实现城市土地空间结构演变预测。实验结果表明,所提方法的城市土地空间结构演变预测精度更高,整体应用效果更好。

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。

互花米草治理对黄河三角洲湿地利用及景观格局的影响——利用随机森林和改进的CA-Markov模型

随着2022年《互花米草防治专项行动计划(2022—2025年)》发布,黄河三角洲湿地互花米草清理工作迅速展开。通过对比模拟的自然发展情景下与互花米草大规模清理后的真实情景下黄河三角洲湿地利用及景观格局指数,分析清理工作生态效益及湿地未来潜在风险。基于时序Sentinel-1 SAR影像和随机森林分类器获取了2020—2023年黄河三角洲湿地利用/覆盖变化结果;针对短期湿地模拟预测驱动力因素缺乏定量化设定的问题,构建了一种联合随机森林与CA-Markov模型的湿地模拟预测方法,实现2023年湿地自然发展情景下更精准的模拟预测;最后计算了2023年湿地在模拟和真实情景下的景观格局指数。结果表明:1)2023年互花米草清理工作效果显著,其面积锐减至5.53km^(2),与2022年相比减少了55.36km^(2),为芦苇等本土植被释放了生存空间;2)与自然发展情景相比,互花米草“围栏”不复存在,本土植被连通性上升,芦苇分布更加琐碎且面积比自然发展情景下增加了16.37km^(2),湿地的优势植被类型已逐渐向芦苇转变;3)由于互花米草对新生滩涂的入侵能力强于其它本土植被以及沿岸受海浪冲击等问题,互花米草清除后的黄河三角洲湿地面临互花米草复萌、二次蔓延及海岸线侵蚀的新风险。

基于CA-Markov和InVEST模型的昆明市土地利用与生境质量时空变化及预测

基于2000年、2010年和2020年3期GlobeLand30土地利用数据,采用CA-Markov和InVEST模型,通过预测2030年土地利用以及生境变化特征,揭示昆明市土地利用变化趋势及对其生境质量时空演变的影响,为生态环境可持续性发展提供参考。结果表明:①研究区以耕地、林地和草地为主,2000—2020年由于退耕还林还草政策的实施和社会经济快速发展,耕地面积比例持续降低,人造地表面积比例增加,其他类型变化不大。②预计2030年人造地表扩张速度加快,主要是来自耕地、林地和草地的转换,除人造地表和草地外,其他用地类型面积均为下降趋势。③研究区于2010年景观破碎化程度达到最高,2020—2030年破碎化程度持续降低,各类型景观趋于简单化和规则化。④研究区平均生境质量逐渐降低,低生境质量区域与高生境质量区域面积逐渐增加,两极分化态势逐渐明显。

基于CA-Markov模型的淮河流域土地利用变化研究

【目的】研究淮河流域土地利用变化特征并预测淮河流域2030年土地利用变化,实现流域土地资源的合理开发利用。【方法】基于淮河流域1990—2020年共7期的土地利用数据,采用土地利用转移矩阵和土地利用动态度分析淮河流域土地利用变化特征。基于元胞自动机-马尔科夫(CA-Markov)模型模拟淮河流域2010和2015年的土地利用格局,并在满足一定的精度条件下,预测淮河流域2030年的土地利用变化趋势。【结果】(1)耕地和建设用地面积占淮河流域土地利用面积的80%以上,是淮河流域最主要的两种土地利用类型;(2)耕地的减少和建设用地的持续扩张是淮河流域1990—2020年土地利用最为明显的变化特征;(3)基于CA-Markov模型模拟的2010年和2015年土地利用的Kappa系数分别为0.937和0.944,模拟精度较高;(4)预测的淮河流域2030年土地利用变化显示:耕地的减少和建设用地的扩张仍然是主要趋势,但二者变化幅度放缓,林地和草地的变化不显著,水域面积继续增加。【结论】淮河流域建设用地大幅度扩张以及耕地面积持续减少应引起重视,基于CA-Markov模型模拟的2030年土地利用变化可为流域未来土地利用开发提供参考。

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。

基于CA-Markov模型的云南自由贸易试验区土地利用和景观格局时空演变分析及模拟预测

为探究云南自由贸易实验区土地利用和景观格局变化特征,协调经济发展和生态保护,促进土地资源高效可持续利用。本文通过Arc GIS 10.6、Fragstats 4.0、IDRISI 17.0以自贸区2010、2015、2020年三期遥感影像为数据源,研究分析其土地利用变化和景观格局时空演变特征,并利用CA-Markov模型,模拟预测2025年土地利用类型和景观格局。研究表明,(1)三个片区土地利用结构变化为建设用地增加,林地、耕地和未利用地减少,草地和水体基本保持不变。(2)在研究期前期,建设用地主要来源于林地和耕地,后期为未利用地,并加大对生态环境的保护力度。(3)在发展过程中,其整体景观破碎度逐渐降低,但连通性和集聚度逐渐加强,生物多样性减少;耕地、林地破碎化程度加重,聚集度和景观优势不断下降,并转换为建设用地;随着人类干扰因素不断增强,建设用地优势表现明显,聚集度高,区域景观自我调节能力增强,景观空间结构更加稳定。

基于InVEST和CA-Markov模型的“十四五”碳储量时空演变与预测——以张家界市为例

基于多情景模拟不同土地利用类型的碳储量,为张家界市实现旅游与生态发展提供建议。以张家界市2000年、2010年、2020年土地利用变化作为切入点,基于InVEST模型与CA-Markov模型,对不同时空的土地利用类型下的碳储量进行空间情景模拟和量化,探讨不同情景下的土地利用及其碳储量变化模式。结果表明:张家界市主要以林地、耕地为主,建设用地的增加主要来自耕地与草地;碳储量在空间分布上呈明显聚集性;碳储量呈逐年减少的趋势,共减少了0.35×10^(6)t。在不同的情景模拟下,相对于2020年,碳储量流失总量分别为3.44×10^(6)、3.04×10^(6)、3.99×10^(6)、4.93×10^(6) t。综合发展情景下的土地利用现状最为合理,能够为区域碳平衡和土地利用结构优化提供依据,助力张界市实现“双碳”目标与旅游高质量发展。

基于CA-Markov模型的公园绿地土地利用变化及预测——以武汉市江岸区为例

研究城市内公园绿地的土地利用变化,对于日后城市公园的发展及改造、城市的经济发展、空间安排都具有一定的参考意义。以武汉市江岸区为研究对象,运用ArcGIS对Landsat 2005年、2010年、2015年和2020年4期土地遥感数据进行目视解译,并通过CA-Markov模型对2030年土地利用情况以及城市公园绿地变化进行预测,在此基础上分析了2005-2030年城市公园绿地的变化情况。结果显示:①武汉市江岸区的主要用地类型为建设用地,面积占总用地面积的60%以上;②耕地和林地的减少和建设用地的持续增加是武汉市江岸区2005-2010年土地利用最为明显的变化特征;③运用CA-Markov模型模拟2015年和2020年土地利用变化,其Kappa系数分别为0.772和0.809,均大于0.75,模拟精度合格;④预测的武汉市江岸区2030年土地利用变化显示:耕地和林地的减少以及建设用地的增加仍然是主要趋势,但二者变化幅度减缓,城市公园绿地处于持续增加的状态。指出了武汉市江岸区建设用地的持续增加,耕地林地的持续减少需要引起重视,基于CA-Markov模型模拟的2030年土地利用变化也可为武汉市江岸区未来土地利用开发提供参考,同时城市公园绿地的建设可以此为参考依据,发展原有的城市公园绿地或开发新的城市公园绿地。

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.

Geostatistical seismic inversion and 3D modelling of metric flow units,porosity and permeability in Brazilian presalt reservoir

Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.