Modelling algal blooms in the Dutch coastal waters by integrated numerical and fuzzy cellular automata approaches

In this paper, an integrated numerical and fuzzy cellular automata model was developed to predict possible algal blooms in Dutch coastal waters basing on the irradiance, nutrients and neighbourhood conditions. The numerical module used Delft3D-WAQ to compute the abiotic conditions, and fuzzy cellular automata approach was applied to predict the algal biomass that was indicated by chlorophyll a concentration. The simulated results of year 1995 were compared with that from BLOOM II model, and the advantages, disadvantages as well as future improvement were presented. In general, through this study, it is seen that the integrated modelling deserves more research inputs because: (1) the hydrodynamic processes and nutrients concentrations can be simulated in details by numerical method; (2) the irregular and sparse water quality and biological data, and the empirical knowledge from experts can be explored by the fuzzy logic technique; (3) the spatial heterogeneity, local interactions and the emerge of patchiness could be well captured through the cellular automata paradigm.     

Multistate models for monitoring individual trees in permanent observation plots

This interdisciplinary research on forest ecosystems begins with some characteristics of ecosystems which are the basis for the derivation of statistical models for the development and vitality of trees. Several ecological problems which could be solved by longitudinal studies are mentioned. Statistical methods for the evaluation of the crowns of spruce trees (Picea abies Karst) in three permanent observation plots in Switzerland are described. In particular, the time-dependent proportional odds model and a transitional model are used. Through application of these multistate models the data give information on the dependence of an ordered categorical response variable on covariates characterizing the ecosystem. The response variable is observed through infrared aerial photographs. This monitoring system gives insight into the dynamic behaviour of the forest ecosystem. The need for more eco-systematically motivated statistical research using longitudinal studies is identified.     

Assessment of herbaceous plant habitats in water-constrained environments: predicting indirect effects with fuzzy logic

Herbaceous plant production plays a key role in determining the function of rangeland ecosystems in the semi-arid and Mediterranean regions. Therefore, assessment of herbaceous plant habitats is important for understanding the ecosystem functioning in these regions and for applied purposes, such as range management and land evaluation. This paper presents a model to assess herbaceous plant habitats in a basaltic stony environment in a Mediterranean region. The model is based on geographic information systems (GIS), remote sensing and fuzzy logic, while four indirect variables, which represent major characteristics of herbaceous habitats, are modeled: rock cover fraction; wetness index (WI); soil depth; and slope orientation (aspect). A linear unmixing model was used to measure rock cover on a per pixel basis using a Landsat TM summer image. The wetness index and local aspect were determined from digital elevation data with 25 m × 25 m pixel resolution, while soil data were gathered in a field survey. The modeling approach adopted here is process-based and assumes that water availability plays a crucial role in determining herbaceous plant production in Mediterranean and semi-arid environments. The model rules are based on fuzzy logic and are written based on the hypothesized water requirements of the herbaceous vegetation. The results show that on a polygon basis there is positive agreement between the model proposed here and previous mapping of the herbaceous habitats carried out in the field using traditional methods. Intrapolygon tests show that the use of a continuous raster data model and fuzzy logic principles provide an added value to traditional mapping. Moreover, herbaceous biomass measurements at two time intervals—mid- and peak winter season—corresponded with the habitat assessment predictions achieved using a new scenario that is proposed in this research. This scenario suggests that rockiness increases herbaceous production on south-facing slopes, while in other slope aspects the rock cover has lower impact on herbaceous growth. Due to its simplicity, the model suggested here can be used by planners and managers, to adjust range activities over large areas. The process-based approach should allow adaptation of the model to other regions more effectively than models that were formulated on a purely empirical basis. The model could also be used to study the relationship between water availability and ecosystem productivity on a regional scale.     

计算机辅助确定外因火灾位置方法

系统介绍用计算机辅助确定矿井外因火灾位置方法的原理,给出该方法的具体步骤,剖析其定性分析、定量分析和定量分析结果评价三大步骤中的各技术关键,并借助一个实例验证了该方法实用的可行性     

斜坡稳定性的二级模糊推断

根据对斜坡稳定性影响的大小,选取了七个主要因于,引用了山西明水河流域几个斜坡样点实测数据．应用模糊近似推论方法及信息分配原理,对斜坡稳定性进行了单因子灰色隶属度分析及综合二级模糊近似推论,该方法克服了传统单因子评价的弊端,能比较真实地反映实际,为布设护坡工程提供了理论依据。     

基于机器学习的电热防除冰表面温度变化趋势预测

针对飞机部件周期控制律电加热防除冰的应用,提出基于机器学习以预测电加热防除冰表面温度的变化趋势.依靠大型结冰风洞及其电加热防除冰控制系统完成防除冰试验,获得有效的试验数据,以通、断电周期为分割单元,将试验数据划分成验证集和训练集.根据电热防除冰过程的换热情况,构建样本的特征参数,利用机器学习的有监督学习方式,选择KNN近邻回归算法和局部线性加权回归算法预测温度变化率,再转换为温度,得到的温度变化与测量数据的线性相关性达到80％以上的高相似度结果,表明使用机器学习可快速预测电热防除冰部件的表面温度变化趋势,且不同的回归算法针对模型不同测温点位置的预测效果存在差异.     

华北地区大气细颗粒物(PM2.5)年际变化及其对土地利用/覆被变化的响应

大气细颗粒物(PM_(2.5))是大气污染的重要组成成分,对其影响因素进度探讨具有重要的意义.但目前来看,多数的研究都聚焦于PM_(2.5)与气象要素以及经济因素之间的关系,分析土地利用/覆被变化对PM_(2.5)影响的研究相对较少,需要进一步的深入探讨.基于PM_(2.5)空间分布数据及土地利用/覆被数据,对华北地区PM_(2.5)变化特征及土地利用/覆被变化特征进行了系统分析,并利用地理加权回归、GIS空间分析等手段探讨了PM_(2.5)变化与土地利用/覆被变化的响应关系,结果表明:①华北地区PM_(2.5)浓度整体呈现东南高、西北低的空间格局,且18a均保持这一态势没有变化.时间上来看,在2006年达到污染最大值,之后虽有波动但一直居高不下.多数城市PM_(2.5)浓度超标,整体环境污染形势严峻;②2000～2015年研究区土地利用类型/覆被以耕地、林地和草地为主,土地利用/覆被变化趋势主要表现为耕地的大量减少以及建设用地的持续增加,水域和未利用地面积略有减少,林地和草地转入转出面积接近,因而总量变化不大;③地理加权回归模型计算结果表明,Local R~2(衡量局部拟合度)较低的区域为土地利用/覆被未发生变化的区域,而在土地利用/覆被变化明显的区域,Local R~2较高,说明PM_(2.5)变化对土地利用/覆被变化有着显著的响应作用;④对于不同土地利用/覆被状况而言,PM_(2.5)分布特征表现出建设用地耕地水域草地林地未利用地的趋势.对于不同土地利用/覆被转换方式而言,当自然用地向人工用地转换时PM_(2.5)浓度上升,而人工用地向自然用地转变时PM_(2.5)浓度降低.     

一种HSI空间基于模糊聚类的火灾彩色图像分割算法

针对通用模糊聚类算法进行彩色图像分割存在对初值敏感,迭代过程耗时等问题,在HSI空间结合火焰图像分布特征,采用平均值法进行初值优选,构造抑制算子和抑制因数对火焰无关区域S和I分量进行有效抑制,采用直方图聚类后进行数据融合等方式,最终实现彩色火灾图像分割。实验表明,该算法提高了彩色火灾图像分割的准确性和收敛速度。     

基于矢量空间数据的陵水海滨风景名胜规划区生态敏感性评价

以陵水海滨风景名胜规划区为研究区域,通过调查主要生态敏感问题,选取基本农田、生态保护林、自然保护区、河流水库、道路等主要评价指标,建立评价指标体系,并在此基础上,以Are／info为平台,以矢量数据为数据环境,创建了生态敏感性评价模型。结果显示,20．28％和29．37％的规划区处于较敏感区和敏感区,8．85％旅游设施用地和9．54％旅游服务用地处于敏感区。最后提出了在旅游开发过程中,保护生态敏感目标的合理性建议。     

pUCD-recA重组发光菌构建及对遗传毒性污染物响应作用

基因重组发光菌在水质毒性的评价中具有重要的作用,本研究从分析污染物毒性损伤的机制出发,构建新型pUCD-recA基因重组发光菌.用PCR法从大肠杆菌W3110中扩增recA基因,将其与pGEM-T easy载体连接后测序.测序正确的recA片段及pUCD615载体均用BamHⅠ、EcoRⅠ双酶切,连接后电转化导入宿主菌JM109.挑取克隆,提取质粒用PCR鉴定,阳性克隆再进行测序.将构建成功的pUCD-recA载体转化入大肠杆菌RFM443,加入相应的遗传毒性污染物,观察发光响应作用.结果表明,recA基因PCR扩增出的片段为293 bp,测序结果与GenBank中的recA序列进行BLAST比对,同源性为99%,表明扩增序列正确.与pUCD615载体连接后的测序结果表明,recA基因已正确地插入到pUCD615的多克隆位点,方向和读码框正确,重组发光菌载体构建成功.将构建好的重组载体转化入RFM443宿主菌,加入遗传毒性污染物观察响应效果.丝裂霉素C(MMC)对pUCD-recA重组发光菌诱导效果最好,0.01 mg/L即可有很好的响应曲线;N′-甲基-N′-硝基亚硝基胍(MNNG)则在50~100 mg/...