基于知识推理的土地退化程度评价模型

引入知识推理技术构建了一种新的土地退化程度评价模型(KR_LDAM)。模型集成了可变精度粗集和模糊神经网络两种智能化推理方法进行土地退化程度评价因子提取和退化程度分级,系统地消除了土地退化程度评价过程中专家经验知识主观不一致性以及客观数据不确定性的影响,解决了退化程度划分的模糊性和渐进性问题。选取黄土高原风沙侵蚀区域陕西省横山县为研究区域进行实例研究和模型验证。     

Spatial optimization of rural settlement relocation by incorporating inter‐village social connections under future policy scenarios

Social connections between villages can represent farmers’ interests and thus benefit participatory rural relocation planning. With rural development, however, these connections will change and may weaken the adaptation of relocation plans to future rural systems. As yet, most studies still use empirical social connections to guide relocation planning, while a few have incorporated predicted connections in the context of rural development into relocation decisions. Meanwhile, spatial optimization approaches have seldom been adopted to solve this geographical decision issue. Accordingly, our study proposes a novel spatial relocation framework that incorporates changed inter‐village social connections under future rural development scenarios. Empirical inter‐village connections and their policy‐induced changes in central China were explored using social network analysis. An integration of particle swarm optimization and geographic information systems was adopted to identify the relocation solutions with maximum inter‐village connections and maximum spatial land use compactness, and to examine how connection changes under different policy scenarios influenced relocation outcomes. The results demonstrate the significance of incorporating policy‐induced social connections into relocation plans, and most importantly, show the negative relations between changed social connections and the migration distance/direction of relocated settlements. Our study is expected to improve the adaptation of relocation plans to future rural development.     

Earthworms’ Transcriptome and Gut Microbiota Response to Mineral Weathering

<正>1 Introduction Earthworms are an important component of soil macrofauna,dominating the biomass of soil invertebrates(Kodama et al.,2014;Plum and Filser,2005).They are known as the"engineers of ecosystem",and have a     

甘肃祁连山主动源重复探测项目建设及震源重复性分析

甘肃祁连山主动源重复探测激发源位于甘肃省张掖市祁连山西流水水库,2015年7月9日各系统开始正式运行,11月10日完成了为期40天的连续激发实验。观测资料处理结果表明,气枪震源具有良好的一致性和可重复性,整个观测系统能比较清晰地记录到震源激发的信号。祁连山主动源项目建设取得的结果可为主动源探测工作提供宝贵经验和科学借鉴,不断取得的观测数据为跟踪了解祁连山地区的深部结构时空变化提供了可能。     

内蒙古“一湖两海”水域动态监测体系:框架、指标与专题可视化

水资源动态监测是自然资源监测评价的核心内容。自然资源治理从单一要素治理向综合要素治理转型,水资源监测需要从系统化视角切入。基于此,以中国内蒙古自治区“一湖两海”水域为案例区,基于驱动-压力-状态-响应(driving-pressure-status-response,DPSR)模型构建了一种综合水域动态监测体系,形成了水域动态监测框架与指标体系,同时结合专题地图可视化技术对案例区水量、水温、水质和生物多样性的变化趋势进行动态分析和专题图表达,为“一湖两海”水域保护与治理工作提供决策支持。     

多目标微粒群算法用于土地利用空间优化配置

针对已有空间配置方法在协调土地利用多目标方面的不足,探讨构建基于多目标微粒群算法的土地利用空间优化配置模型。建立了土地利用空间配置方案与单个微粒的映射关系,以经济、社会、生态和综合效益为优化目标,以土地利用优化结构、土地利用现状、地类转换规则为约束条件进行最优配置方案的自组织、智能化搜索,并选取湖北省嘉鱼县作为试验区验证了其有效性。     

利用模拟退火算法的村镇土地利用空间优化调控模型

提出了一种顾及土地利用监测评价结果的村镇土地利用调控模型。该模型整合空间单元划分、调控单元判别、空间配置于一体,运用模拟退火算法,实现了多目标控制下的空间优化调控。实例验证了此模型的合理性。     

多目标微观邻域粒子群算法及其在土壤空间优化抽样中的应用

提出一种基于多目标微观邻域粒子群的土壤空间优化抽样方法。方法面向土壤空间调查的多目标特征,构建了基于最小克里金方差（MKV）和极大熵准则（ME）的粒子群多目标适应度函数,设计了最小样本量限制、样点可达性、采样成本限制和最小空间关联性四类粒子微观邻域操作策略,能高效协调土壤空间抽样精度、代表性、成本、样本量与样点布局等多目标冲突。实验结果表明,相比单目标粒子群算法和模拟退火算法,该方法的目标冲突协同能力强、收敛效率高,所设计抽样方案最优,为土壤质量精确调查与高效监测提供了技术支持。     

Regional land-use allocation using a coupled MAS and GA model: from local simulation to global optimization,a case study in Caidian District,Wuhan, China

China is facing the pressures of both rapid economic development and environmental protection, and land-use allocation optimization is an important way to manage the conflicts between these pressures and to achieve sustainable development. Optimization of land-use allocation is a nonlinear multiobjective spatial optimization problem, and a purely local simulation model or global optimization model is insufficient to solve it. It is essential to bridge the gap between the two models through the combination of top-down and bottom-up approaches. This study integrates a multiagent system (MAS) that simulates the behaviors of land-use stakeholders with regard to their choices of specific locations, with a genetic algorithm (GA) that simultaneously evaluates and optimizes land-use configurations to meet various regional development objectives. The model is expected to achieve the optimization of land use in terms of the composition and spatial configuration. Caidian District, Wuhan, China, was chosen as the study area to test the model in this paper. The results show that the performance of the coupled model is superior to a pure GA model or MAS model. The optimal configuration improves on the economic output, spatial compactness, and carbon storage of the current configuration and promotes sustainable regional land-use development from the local scale to the regional scale.     

甘肃祁连山主动源不同枪组激发效果对比

大容量气枪震源激发探测因具绿色环保、传播距离远和高度重复性等特点成为地下介质探测的新方法,利用其进行地下介质物性变化高精度探测,则对不同激发条件下气枪传播信号的一致性提出要求。本文选取了祁连山主动源不同枪组（4支和3支气枪）在激发环境一致情况下分别激发时各远台波形记录,通过信号叠加、互相关及频谱分析等方法,对比分析了各台站记录的不同枪组激发信号,及其与震中距的关系,并讨论了不同枪组激发对地下介质走时变化观测的影响。结果表明：（1）不同枪组的激发信号波形相关度随震中距增大而降低,信号震相随激发能量有微弱的变化,震中距超过50 km后其变化几乎分辨不出;（2）远台接收信号的能量大小随主动源激发能量变化,各台站能量变化主要体现在优势频率范围内;（3）接收台站台基越坚固不同激发能量均方根振幅比越小;（4）叠加一定数量的波形可以有效提高远台记录信号的信噪比,增加气枪激发能量则相对更容易提高信号信噪比。