大型GIS与数字地球的空间数学基础研究

将地图投影概念由传统的曲面到平面的变换扩展为曲面到曲面的变换,并提出一套适合于大型GIS和地球数字产品的实用\"地图投影\"模型。该模型的视图采用等距离切圆柱投影,度量空间是椭球面几何系统,符合计算机环境下GIS视图与度量空间分离的特性。鉴于全球多分辨率连续可视化,精密可视化量算、三维、多维地球数据统一,标准的空间定位框架和只有在统一的空间系统内,全球资源、生态环境数据才能进行精确的地理分析等4个方面的需要,从地图投影发展到本模型将是方向和技术趋势     

参考作物腾发量预测的径向基函数法

探讨了采用径向基函数网络模型进行参考作物腾发量预测方法的可行性,设计多组数字实验处理研究了输入因子间相关性对网络模型预测准确性的影响,预测结果与Penman-Montieth方法计算结果比较表明,所确定的模型与改进的Penman公式计算值有很高的一致性,具有一定精度。     

ON THE VERTICAL DISCRETIZATION OF ATMOSPHERIC MODELS-PART Ⅰ:EXISTING PROBLEMS,DATA PROCESSING AND CHOICE OF VERTICAL COORDINATE AND BASIS FUNCTIONS

Discussions are carried out on the vertical discretization of current atmospheric models.It is pointed out that there exist problems in the integration of the hydrostatic equation and the computation of vertical advection,vertical diffusion and so on.Then some possible ways for solving or alleviating them are suggested.Finally,the choice of vertical coordinate and basis functions is discussed.     

河湖水系连通理论基础与优先领域

河湖水系连通是中国正在推进的重大治水战略,是提高国家水资源统筹调配能力、水生态环境修复能力和河湖健康保障能力的重大举措,也是提高水旱灾害抵御能力的重要保障.目前河湖水系连通的知识比较零散,尚未形成系统的理论体系,河湖水系连通理论基础的研究,对理论体系的构建、战略的实施和持续推进具有重要意义.本文基于河湖水系连通的理论研...     

Gap Filling and Quality Assessment of CO2 and Water Vapour Fluxes above an Urban Area with Radial Basis Function Neural Networks

Vertical turbulent fluxes of water vapour, carbon dioxide, and sensible heat were measured from 16 August to the 28 September 2006 near the city centre of Münster in north-west Germany. In comparison to results of measurements above homogeneous ecosystem sites, the CO₂ fluxes above the urban investigation area showed more peaks and higher variances during the course of a day, probably caused by traffic and other varying, anthropogenic sources. The main goal of this study is the introduction and establishment of a new gap filling procedure using radial basis function (RBF) neural networks, which is also applicable under complex environmental conditions. We applied adapted RBF neural networks within a combined modular expert system of neural networks as an innovative approach to fill data gaps in micrometeorological flux time series. We found that RBF networks are superior to multi-layer perceptron (MLP) neural networks in the reproduction of the highly variable turbulent fluxes. In addition, we enhanced the methodology in the field of quality assessment for eddy covariance data. An RBF neural network mapping system was used to identify conditions of a turbulence regime that allows reliable quantification of turbulent fluxes through finding an acceptable minimum of the friction velocity. For the data analysed in this study, the minimum acceptable friction velocity was found to be 0.15 m s⁻¹. The obtained CO₂ fluxes, measured on a tower at 65 m a.g.l., reached average values of 12 μmol m⁻² s⁻¹ and fell to nighttime minimum values of 3 μmol m ⁻² s⁻¹. Mean daily CO₂ emissions of 21 g CO₂ m⁻²d ⁻¹ were obtained during our 6-week experiment. Hence, the city centre of Münster appeared to be a significant source of CO₂. The half-hourly average values of water vapour fluxes ranged between 0.062 and 0.989 mmol m⁻² s⁻¹and showed lower variances than the simultaneously measured fluxes of CO₂.     

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method

The optimal seismic design of structures requires that time history analyses （THA） be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function （RBF） neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization （BPSO） is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.     

Reservoir induced earthquakes analyzed via radial basis function networks

Estimation of the magnitude of reservoir induced seismicity is essential for seismic risk analysis of dam sites. Different geological and empirical methods dealing with the mechanism or magnitude of such earthquakes are available in the literature. In this study, a method based on an artificial neural network utilizing radial basis functions (RBF network) was employed to analyze the problem. The network has only two input neurons, one representing the maximum depth of the reservoir and the other being a comprehensive parameter representing reservoir geometry. Magnitudes of the induced earthquakes predicted using the RBF network were compared with the actual recorded data. Compared with the conventional statistical approach, the proposed method gives a better prediction, both in terms of coefficients of correlation and error rates.     

弥散地震评价的原则与方法研究

本文论述了弥散地震的概念及其由来,分析了当前对弥散地震存在的几种不同认识以及弥散地震对工程抗震设计基准影响的特点,在上述基础上研究和探讨了弥散地震评价的原则与方法。     

受涝甘蔗恢复生产措施的农业气象依据

对受洪涝危害的甘蔗提出恢复生产建议 ,并分析这些建议的农业气象依据 ,探讨其可行性和科学性 ,更好地为农业生产服务     

油藏地球化学的基础与展望

在系统综述国内外油藏地球化学研究现状和研究方法的基础上，着重介绍了其４个研究领域及地质意义，即：油藏地球化学描述、油藏原油混合作用原理、油藏中的有机隔层－沥青垫、油藏内有机－无机相互作用及储层润湿的改变。