Fuzzy set approaches to classification of rock masses   总被引：6，自引：0，他引：6  

A. Aydin   《Engineering Geology》2004,74(3-4):227-245

Rock mass classification is analogous to multi-feature pattern recognition problem. The objective is to assign a rock mass to one of the pre-defined classes using a given set of criteria. This process involves a number of subjective uncertainties stemming from: (a) qualitative (linguistic) criteria; (b) sharp class boundaries; (c) fixed rating (or weight) scales; and (d) variable input reliability. Fuzzy set theory enables a soft approach to account for these uncertainties by allowing the expert to participate in this process in several ways. Hence, this study was designed to investigate the earlier fuzzy rock mass classification attempts and to devise improved methodologies to utilize the theory more accurately and efficiently. As in the earlier studies, the Rock Mass Rating (RMR) system was adopted as a reference conventional classification system because of its simple linear aggregation.

The proposed classification approach is based on the concept of partial fuzzy sets representing the variable importance or recognition power of each criterion in the universal domain of rock mass quality. The method enables one to evaluate rock mass quality using any set of criteria, and it is easy to implement. To reduce uncertainties due to project- and lithology-dependent variations, partial membership functions were formulated considering shallow (<200 m) tunneling in granitic rock masses. This facilitated a detailed expression of the variations in the classification power of each criterion along the corresponding universal domains. The binary relationship tables generated using these functions were processed not to derive a single class but rather to plot criterion contribution trends (stacked area graphs) and belief surface contours, which proved to be very satisfactory in difficult decision situations. Four input scenarios were selected to demonstrate the efficiency of the proposed approach in different situations and with reference to the earlier approaches.  相似文献   

基于模糊算子理论的道路半自动提取   总被引：2，自引：0，他引：2  

王天鹏  姜挺 《测绘学院学报》2004,21(3):187-189

提出了一种基于模糊算子理论的道路半自动提取方法。该方法在对小比例尺影像进行Sobel边缘检测的基础上，定义了12种模糊算子表示2维道路的各种可能的结构，然后在给定道路种子点附近形成的一定范围内进行搜索，提取出道路的中心线。实验结果表明，该算法速度较快并且具有较强的鲁棒性。  相似文献   

集成高度和彩色纹理特征的影像目标模糊聚类识别方法     

潘励  郑宏  张祖勋  张剑清 《武汉大学学报(信息科学版)》2004,29(4):311-314

提出一种基于高度和彩色纹理信息的目标识别方法 ,其目的是提取具有相对高度的地物。由于传统二维纹理分析的方法往往不能有效地提取这类目标 ,本文把高度、色彩和纹理信息有机地结合起来 ,提高了识别率。依据影像提取结果 ,探讨了树林对森林覆盖地区自动空中三角测量选点的负面影响 ,同时建议采用数据分组的方案来改善森林覆盖地区自动空中三角测量的精度 ,并用对比实验证明了其有效性。  相似文献   

Groundwater vulnerability mapping: A GIS and fuzzy rule based integrated tool   总被引：8，自引：0，他引：8  

B. Dixon   《Applied geography (Sevenoaks, England)》2005,25(4):327-347

Contamination of groundwater has become a major concern in recent years. Since testing of water quality of all domestic and irrigation wells within large watersheds is not economically feasible, one frequently used monitoring strategy is to develop contamination potential maps of groundwater, and then prioritize those wells located in the potentially highly contaminated areas for testing of contaminants. However, generation of contamination potential maps based on groundwater sensitivity and vulnerability is not an easy task due inherent uncertainty. Therefore, the overall goal of this research is to improve the methodology for the generation of contamination potential maps by using detailed landuse/pesticide and soil structure information in conjunction with selected parameters from the DRASTIC model. The specific objectives of this study are (i) to incorporate GIS, GPS, remote sensing and the fuzzy rule-based model to generate groundwater sensitivity maps, and (ii) compare the results of our new methodologies with the modified DRASTIC Index (DI) and field water quality data. In this study, three different models were developed (viz. DI_fuzz, VI_fuzz and VI_{fuzz_ped}) and were compared to the DI. Once the preliminary fuzzy logic-based (DI_fuzz) was generated using selected parameters from DI, the methodology was further refined through VI_fuzz and VI_{fuzz_ped} models that incorporated landuse/pesticide application and soil structure information, respectively. This study was conducted in Woodruff County of the Mississippi Delta region of Arkansas. Water quality data for 55 wells were used to evaluate the contamination potential maps. The sensitivity map generated by VI_{fuzz_ped} with soil structure showed significantly better coincidence results when compared with the field data.  相似文献   

基于地名首字母模糊查询的关键技术研究     

徐齐  陈杨 《测绘与空间地理信息》2016,(3):184-186

根据地名信息的特点,提出一种基于地名首字母的模糊查询方法。采用Access数据库,结合微软自带的字库实现了地名首字母的提取与入库,特别是解决了多音字在首字母提取及入库时的处理,并提供了一种文件批量输入的方法,实现了基于地名首字母的模糊查询。设计开发的地名查询系统,使用简单,效率也得到了极大的提高,可以获取对应的地理位置,为后继GIS系统开发提供方便。  相似文献   

基于 DTW 距离的时序 NDVI 数据植被信息提取--以秦巴山区为例     

韩晓勇  韩玲  陈鲁皖 《测绘工程》2016,25(3):11-16

秦巴山区是我国重要的生态屏障,对该区的植被信息提取开展研究,可为区内生态服务功能及自然资源开发利用提供基础数据。通过加窗处理改进DTW距离相似性算法,结合临近度模糊分类方法对2005—2014年的MODIS NDVI时序数据进行植被信息提取。首先利用S-G滤波对MODIS NDVI时序数据进行重建;再利用2013年的采样数据构建各类植被的标准NDVI时序曲线,逐像元计算与标准NDVI时序曲线的加窗DTW距离,利用临近度模糊分类实现植被信息提取;最后验证提取精度。结果表明,算法具有较高的运行效率,可避免错误匹配,以较高的精度(总体精度83.8%,kappa系数0.77)实现长时间序列的植被信息提取。  相似文献   

改进支持向量机的高分遥感影像道路提取   总被引：2，自引：0，他引：2  

朱恩泽  宋伟东  戴激光 《测绘科学》2016,41(12):224-228

针对支持向量机受分类数的限制在高分辨率遥感影像中无法直接获取高精度道路网信息的问题,该文提出一种新的混合的基于支持向量机的方法:首先,利用模糊C均值聚类方法将输入的遥感影像分为3类,以减少支持向量机的错分现象;其次,运用支持向量机将不同类别的像素分为道路类和非道路类;最后,应用马尔科夫随机场对分类结果进行噪声去除,并采用形态学进行后处理,进而得到精确道路网信息。实验结果表明:该算法不仅能够从高分辨率遥感影像中提取出道路网,而且精度优于直接使用支持向量机算法以及对比算法。  相似文献   

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran   总被引：1，自引：0，他引：1  

Mehdi Azari  Amin Tayyebi  Marco Helbich  Mohsen Ahadnejad Reveshty 《地理信息系统科学与遥感》2016,53(2):183-205

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.  相似文献   

Possibilities of land use change analysis in a mountainous rural area: a methodological approach     

Szilárd Szabó  László Bertalan  Ágnes Kerekes  Tibor J. Novák 《International journal of geographical information science》2016,30(4):708-726

Decreasing population density is a current trend in the European Union, and causes a lower environmental impact on the landscape. However, besides the desirable effect on the regeneration processes of semi-natural forest ecosystems, the lack of traditional management techniques can also lead to detrimental ecological processes. In this study we investigated the land use pattern changes in a micro-region (in North-Eastern Hungary) between 1952 and 2005, based on vectorised land use data from archive aerial photos. We also evaluated the methodology of comparisons using GIS methods, fuzzy sets and landscape metrics. We found that both GIS methods and statistical analysis of landscape metrics resulted in more or less the same findings. Differences were not as relevant as was expected considering the general tendencies of the past 60 years in Hungary. The change in the annual rate of forest recovery was 0.12%; settlements extended their area by an annual rate of 3.04%, while grasslands and arable lands had a net loss in their area within the studied period (0.60% and 0.89%, respectively). The kappa index showed a smaller similarity (~60%) between these dates but the fuzzy kappa and the aggregation index, taking into account both spatial and thematic errors, gave a more reliable result (~70–80% similarity). Landscape metrics on patch and class level ensured the possibility of a detailed analysis. We arrived at a similar outcome but were able to verify all the calculations through statistical tests. With this approach we were able to reveal significant (p < 0.05) changes; however, effect sizes did not show large magnitudes. Comparing the methods of revealing landscape change, the approach of landscape metrics was the most effective approach, as it was independent of spatial errors and ensuring a multiple way of interpretation.  相似文献   

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data     

Milad Janalipour 《International Journal of Digital Earth》2018,11(12):1241-1268

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.  相似文献