模糊理论在临沂城区生态地质环境评价中的应用

近年来,随着经济的快速发展和城市的大规模建设,临沂市城区出现了一系列环境地质问题,比如水土体污染、地质灾害加剧、生态环境恶化等等,严重破坏了自然生态环境。为综合评价该区生态地质环境,加强该区的生态地质环境保护与管理,该文在目前国内外尚无生态地质环境评价规范和标准的情况下,运用模糊理论对临沂生态地质环境进行了综合评价。评价结果把工作区分为良好区、较好区、较差区和差区4个区。     

基于二级模糊综合评判的废弃采石场对景观的影响分析——以泰山风景区及周边采石场为例

以泰山为例,根据风景区内及周边废弃采石场分布现状,选取地理位置、规模、植被恢复程度作为评价因子,利用二级模糊综合评判模型,量化了各个采石场相关指标对景区景观影响程度,并最终得出综合评判结果,为进一步对废弃采石场进行处理提供依据。     

基于双层模糊计算的工程测量质量评价模型

科学的对工程测量的成果质量进行评价是测绘生产过程中的关键工作,本文在总结以往测绘成果质量评价方法的同时,给出了工程测量的质量评价模型,即双层模糊评价模型,以高程控制测量为例,详述了模型的计算步骤和最终质量评价等级。     

三种开发区土地集约利用评价方法比较分析

BP神经网络是近年来用于开发区土地集约利用评价的新方法,自学习能力强且精度较高。模糊综合评价法和多因素综合评价法是开发区土地集约利用评价的常用方法。本文拟采用上述三种方法对广东省某高新技术产业开发区土地集约利用程度进行评价。通过评价过程和结果的对比分析,探讨三种方法在评价中的优缺点。     

基于模糊贴近度和改进Prim算法的高光谱图像波段分组排序

传统的基于Prim算法的高光谱图像波段分组排序需要计算所有波段之间的相关系数,并采用满秩相关系数矩阵作为邻接矩阵进行比较,计算复杂度较高。结合模糊数学的相似度衡量理论和高光谱图像的特点,首先提出使用计算简单的最大最小贴近度(maximum and minimum closeness,MMC)作为衡量高光谱图像波段间相关性的参数;然后将MMC的满秩邻接矩阵稀疏化,提取有效波段进行排序,明显降低了排序的波段数目和比较次数。实验结果表明,与传统的Prim算法相比,所提出的算法在保持原有压缩效率的同时,大大降低了波段排序的复杂度,平均波段排序运行时间减少了27%。     

Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery

Urban areas consist of spectrally and spatially heterogeneous features. Advanced information extraction techniques are needed to handle high resolution imageries in providing detailed information for urban planning applications. This study was conducted to identify a technique that accurately maps impervious and pervious surfaces from WorldView-2 (WV-2) imagery. Supervised per-pixel classification algorithms including Maximum Likelihood and Support Vector Machine (SVM) were utilized to evaluate the capability of spectral-based classifiers to classify urban features. Object-oriented classification was performed using supervised SVM and fuzzy rule-based approach to add spatial and texture attributes to spectral information. Supervised object-oriented SVM achieved 82.80% overall accuracy which was the better accuracy compared to supervised per-pixel classifiers. Classification based on the proposed fuzzy rule-based system revealed satisfactory output compared to other classification techniques with an overall accuracy of 87.10% for pervious surfaces and an overall accuracy of 85.19% for impervious surfaces.     

A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach

In this study, several types of adaptive network‐based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of MFs and if‐then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.     

Metropolitan dynamics in Israel: an emerging “metropolitan island state”?

This study analyzes metropolitan dynamics in a small country with an “island state” context of closed boundaries, using commuting data and mobile phone tracking data. We examine whether the Israeli context encourages the formation of a monocentric “metropolitan state,” characterized by increasing links between localities throughout the country and its principal metropolitan node (Tel Aviv)—rather than with secondary metropolitan areas—and by fuzzy, overlapping metropolitan boundaries. Commuting data from the 1995 and 2008 censuses show that metropolitan expansion processes in Israel are gradual. Mobile phone tracking data for 2013 reveal similar patterns, confirming the urban structure’s stability and the reliability of tracking data as a means of assessing metropolitan processes. The “island state” context supports growing monocentricity, but, when it comes to commuting and travel for other purposes, Israel is not yet a metropolitan state; metropolitan boundaries are not as fuzzy and rapidly changing as expected.     

Suspended load estimation using L1-fuzzy regression,L2-fuzzy regression and MARS-fuzzy regression models

ABSTRACT

The problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.

Editor D. Koutsoyiannis; Associate editor H. Aksoy     

Multi-criteria modelling and clustering of spatial information

To improve the formal integration of verbally given spatial information in a geographical information system, a methodology for converting such information to a geographical representation was developed. This was applied to examples of a spatial-related analysis of natural language text messages in the domain of disaster management. This article presents an approach for such a conversion by using a suitable knowledge representation as well as formal modelling structures. The structures are provided by an ontology-supported knowledge base. With respect to spatial uncertainty, a formal representation based on possibility theory was defined. Moreover, a cluster algorithm was developed for handling information of different sources and building a spatial context.