用聚类分析法划分山东省农业气象产量气候区

详细介绍了用聚类分析法划分山东省农业气象产量气候区的方法。用该方法制作产量分区预报，不仅考虑了分区内农业气候条件的影响，还考虑了分区内的生产力水平和行征区的划分，从而使农业气象产量预报更具体，更准确且比较简便。     

Fuzzy computing based rainfall–runoff model for real time flood forecasting

This paper analyses the skills of fuzzy computing based rainfall–runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time‐steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one‐step‐ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one‐step‐ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times. Copyright © 2004 John Wiley & Sons, Ltd.     

丽江市黑龙潭地区水文地质条件分析

以丽江市玉龙县境内的黑龙潭地区为主要研究对象,总结了已有的水文地质资料,分析了九子海-黑龙潭地区的岩性、构造等特征。通过对研究区内及周边泉的调查研究,认为北衙组中段、上段的灰岩、白云质灰岩是研究区的主要含水层,地形切割上述含水层使得地下水出露成泉。对水样进行了水化学聚类分析和氢氧同位素分析,发现含水层主要接受大气降水的渗透补给。按照水力联系对泉点进行了划分。根据研究区内及周边地区的泉点分布分析了地下空间的特点和地下水的联系。研究结果对泉群的利用、保护具有参考价值。     

基于模糊C均值聚类的云图样本修正与云类自动识别

基于云类样本的红外-可见光二维灰度空间投影,采用模糊聚类方法调整优化云类样本特征区域,消除采样误差。针对常规模糊C均值聚类(FCM)方法在处理上述问题时表现出的局限性,提出用样本特征均值替代FCM中随机初始中心的改进办法,既避免了常规FCM方法对初始中心敏感的缺陷,又可纠正其聚类结果对云类样本特征结构的歪曲。改进后的聚类结果既消除了采样误差,又保持了云类样本的基本特征属性。基于该判据的分类结果,可较为准确地分辨出陆地、水体、低云、中云、卷云、对流云和积雨云,分割判另9结果符合客观实际。     

中国区域经济增长集聚的空间统计分析

运用空间统计和计量经济学Moran I指数法及时空数据(Panel Data)模型分析了中国31个省级区域经济增长集聚及其影响因素.结果显示:①中国省域经济增长具有明显的空间依赖性,在地理空间上存在集聚现象,区域经济增长在时空上呈现出明显的空间效应,忽视空间效应将造成模型设定的偏差和计量结果的非科学性;②空间相关以及由此带来的国际国内贸易及外资等经济活动频繁程度,在很大程度上引起了31个省域区际经济增长的空间不均衡,空间集聚使得在经济增长过程中地理区位(距离)产生的空间成本降低,但地理特征将深刻作用于区域经济增长空间集聚的中心和外围关系;③外商直接投资、国际与区际贸易、人力资本、技术创新等因素对中国区域经济增长的贡献非常重要,但它却不能轻易改变经济地理的规则,经济增长因素在地理空间上的非均衡集聚导致了迥然不同的区域经济增长格局.     

坐标与属性一体化的空间聚类方法研究

通过空间坐标和属性特征的有机结合,定义了3种空间距离,给出了基于空间距离的K平均系统聚类算法,对山东省生态环境质量进行了聚类分析和类型分区。实践表明,空间聚类方法可以较好地反映出空间位置邻近和属性特征相似的空间聚类要求。     

广域旅游产业集群：区域旅游发展的地域分工与组织

区域旅游发展是我国区域发展总体战略的重要专题。区域旅游是同一区域范围内不同地区之间互为旅游客源地、互为旅游目的地的区内旅游活动。区内旅游供给由跨城市区划的食、住、行、游、购、娱等地方小产业集群环环相扣而成,这种分工模式在区域范围内形成一种基于地方产业集群组合的广域旅游产业集群。在这种集群形态内,存在着空间组织、产业组织及运营模式创新的空间。研究区内旅游流在城市节点间的空间演化特征和机制,以及区内旅游流所遵循的典型旅游线路在城市节点间的产业链演化特征和机制,在此基础上,探索基于广域集群组织的区域旅游发展创新,最后在理论上形成提升,形成涵盖结构与功能关系的广域旅游产业集群研究体系,对于区域旅游理论研究和发展实践而言,具有重要的价值。     

边坡稳定性分类评价的同伦模糊C-均值聚类算法

针对单一模糊C-均值聚类算法对初始聚类中心初值敏感性问题,引入同伦理论,提出了同伦模糊C-均值聚类算法。以三峡库岸研究程度较高的36个边坡为对象,采用同伦模糊C-均值聚类算法对边坡的稳定性进行分类,研究边坡最佳分类级数和算法的收敛性、可靠性。边坡聚类结果研究表明,同伦模糊C-均值聚类算法对初始聚类中心的选取没有明显的依赖性,是一个具有全局最优解的聚类方法,其结果明显好于单一模糊C-均值聚类算法。     

Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates

Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results.