海岸带土地发展潜力评价——以杭州湾南岸为例

土地潜力评价是编制土地利用规划的前提,把握土地的潜在经济效能是城镇化、工业化快速推进背景下土地资源合理配置的重要命题。本文甄选自然条件、生态敏感性、经济发展和空间结构指标建立了土地发展潜力评价体系,分别采用模糊综合评价法与网络层次分析法赋权指标体系与计算权重,测度发展潜力值并划分了杭州湾南岸余姚、慈溪和镇海的海岸带土地发展潜力区。结果显示:(1)低发展潜力区面积为237.14 km²,占比为17.73%;中发展潜力区面积为323.08 km²,占比为24.15%;高发展潜力区面积为473.34 km²,占比为35.38%;极高发展潜力区面积为304.27 km²,比例为22.74%。(2)按照地类的级别面积分析,交通运输用地和自然保留地面积在极高潜力区中达到峰值,呈由高向低等级面积递减态势;耕地、其他农用地、城乡建设用地和其他建设用地在高潜力区中占比最大,且基本布局于极高、高潜力区;园地、河流水面、水利建设用地集中于中潜力区,而沿海滩涂与林地基本分布于低、中潜力区。(3)余姚、慈溪和镇海海岸带土地发展潜力分区呈现不同特点,余姚属于待提升型,慈溪为均衡发展型,镇海是高质发展潜力型。     

贵州省六盘水市钟山区地质灾害危险性模糊评判

贵州省六盘水市钟山区为地质灾害典型区,但以往地质灾害研究方面工作程度相对较低,影响了该区地质灾害的有效防治和社会发展规划的有序进行。本文在钟山区地质灾害规划现状调查的基础上,总结了该区地质灾害形成原因和影响因素,最终选取地形坡度、岩组结构、植被覆盖率、人类工程活动、降雨量、地震共六个主要地质灾害影响因素作为评价因素集,利用灰色关联度法确定各影响因子的权重,运用模糊综合评判方法对钟山区内每个网格单元进行模糊综合评判,根据最大隶属度原则得出每个网格单元的隶属度值作为危险性评价结果,按照地质灾害危险性等级对该区进行危险性定量区划。此区划评价结果可供今后该区地质灾害防治、发展规划参考。     

利用人工神经网络方法对牙形石鉴定中若干“模糊特征”的数字化研究

牙形石是一类在地层学研究中具有重要意义的古生物化石。但由于牙形石鉴定中存在诸多重要、却难以定量分析的“模糊特征”,使得牙形石的鉴定和分类工作长期依赖于古生物工作者的经验。利用数学中的非线性方程将这些“模糊特征”进行数学描述,并利用人工神经网络中的反向传播算法设计出一套多隐含层的前馈网络分类器,对这些“模糊特征”进行分类尝试,取得了良好的效果,为牙形石的进一步数值化研究奠定了基础。     

基于模糊综合评判的选址空间决策支持系统

传统的选址空间决策支持系统的数学模型难以全面考虑复杂、抽象的选址影响因素,难以把一些模糊的约束条件纳入数学模型,因而模型存在一定缺陷。利用模糊综合评判进行选址空间决策支持系统的研究,可以充分利用GIS的空间和非空间信息,且考虑的因素更为全面,极大地避免了人为因素的影响,是对选址决策支持系统数学模型的改进。     

A GEOGRAPHICAL ANALYSIS OF IMAGE INTERPRETATION METHODS

Image interpretation methods, procedures for relating image pattern to ground conditions, are essential to our use of remote sensing imagery. These methods can be analyzed in respect to the role of ancillary information in the image interpretation process. In general, those procedures that are comparatively independent of ancillary information can be applied in varied geographic settings. Because almost all interpretation procedures depend to some extent upon ancillary information, a detailed and integrated knowledge of the cultural and physical landscape is a prerequisite for both manual and automated interpretation procedures.     

Modelling sustainable urban growth in a rapidly urbanising region using a fuzzy-constrained cellular automata approach

This article presents an application of a fuzzy-constrained cellular automata model to simulate the spatio-temporal processes of urban growth in the rapidly growing Gold Coast City in Southeast Queensland, Australia. Urban growth has been captured in the model as a continuous process in space and over time, which has been affected by a set of primary and secondary transition rules. The primary transition rules deal with the propensity of a local area for development and the impact of its neighbouring cells on such development, while the secondary transition rules reflect the influences of environmental and institutional factors on urban growth. Application of the model demonstrates its re-applicability to different regions and the effectiveness of the cellular automata technique in studying urban dynamics. It also provides tools to explore sustainable urban growth options under different socio-environmental and planning control factors. A sustainable urban future of the region is achievable if development is managed to maintain a balance amongst ecological conservation, economic growth and the contemporary Australian lifestyle.     

Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

Abstract

The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.

Editor Z.W. Kundzewicz     

Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series

Abstract

The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS.     

一种基于紫外极光图像的亚暴膨胀期起始时刻的自动检测方法

准确界定亚暴起始时刻是理解亚暴相关问题的关键.已有研究主要集中在两方面:一是从极光图像中人工挑选亚暴事件进行案例分析或统计分析来研究亚暴发生机制及亚暴期间的地磁环境;二是基于一些空间物理参数,如AE指数、SME(SuperMAG electrojet)指数、Pi2、正弯扰等,采用人眼判断或是模式识别的方法从中找出亚暴起始时刻.本文尝试采用模式识别的方法从紫外极光图像中自动地检测出亚暴膨胀期起始时刻.首先,将紫外极光图像通过网格化处理转换到磁地方时-地磁纬度(MLT-MLAT)直角坐标下,然后通过模糊c均值聚类方法提取亮斑,再考察亮斑强度是否增强、面积是否极向膨胀来判断是不是亚暴事件.本文方法在1996年12月-1997年2月这三个月的Polar卫星紫外极光图像上进行了实验验证.我们将检测到的亚暴起始时刻与Liou(J. Geophys. Res., 2010, 115: A12219)的人工标记进行了对比,并详细分析了与标记不一致的多检和漏检事件.本文提出的自动检测方法可以快速地从海量紫外极光图像中完成亚暴事件的初步筛选,方便研究人员进一步深入研究极光亚暴.     

Double vagueness: uncertainty in multi-scale fuzzy assignment of duneness

In the automation of identification of landscape features the vagueness arises from the fact that the attributes and parameters that make up a landscape vary over space and scale. In most of existing studies, these two kinds of vagueness are studied separately. This paper investigates their combination in identification of coast landscape units. Fuzzy set theory is used to describe the vagueness of geomorphic features due to the continuity in space. The vagueness resulted from the scale of measurement is evaluated by statistic indicators. The differences of fuzzy objects derived from data at differing resolutions (in size from 3×3 cells to 25×25 cells) are studied in order to examine these higher-order uncertainties.