ALCGEIS知识获取与推理机设计

运用专家系统的知识获取和推理机设计原理与方法,探讨了ALCGEIS系统的基本结构、知识分级、知识获取过程和实现程序,推理机的基本原理和控制策略及其程序实现的算法,以及推理知识的搜索策略;论述了 AIEGEIS推理规则的设计方法及其推理算法。     

地下工程围岩分类模糊推理系统设计

将现场获取的影响地下工程围岩分类的几种主要因素视为判别围岩类别论域上的模糊子集,利用建立的地下工程围岩分类模糊逻辑推理系统对围岩类别进行模糊逻辑推理运算,获得与实际情况相一致的围岩类别的判定,直接为地下工程的信息化设计和施工提供依据。     

基于规范知识的隧道围岩快速分类

总结公路隧道设计规范中的围岩分类知识,运用专家系统技术,开发了一个隧道围岩分类程序SUROC,初步应用于实际工程表明程序可用于公路隧道围岩实时、快速分类。     

CTBTO国际数据中心地震数据处理软件研究进展

全球台网地震震相有效关联与快速精确定位是地震监测和禁核试核查的核心问题之一。禁核试条约组织（CTBTO）筹备委员会临时技术秘书处（PTS）国际数据中心（IDC）自成立以来,一直致力于台网震相自动关联有效率及自动处理公报质量的提高,以减轻分析员的工作量。NET-VISA作为IDC下一代全球震相自动关联和事件检测定位软件,使用概率模型和贝叶斯方法找到解释检测观测结果的最佳事件集,进而从这些观测结果中生成自动处理地震公报。测试结果表明,NET-VISA产生的公报要比IDC目前使用的全球关联方法生成的自动处理公报SEL3更加完整和精准。本文介绍了NET-VISA方法的基本原理和测试进展,最后对CTBT北京国家数据中心参加NET-VISA测试中朝鲜6次核试验的NET-VISA关联结果作了分析对比。     

Nonparametric Estimation of Undrained Shear Strength for Normally Consolidated Clays

For decades, various well-known empirical correlations have been established and commonly used to estimate the undrained shear strength of normally consolidated clays in geotechnical practice. However, there are still many contradictory cases regarding the validity of these correlations, so their applicability and reliability are questionable. In this study, an extensive geotechnical database for normally consolidated clays has been compiled. Next, a Bayesian nonparametric method is exploited to identify the most critical soil index properties in inferring the undrained shear strength and a new predictive model is proposed. Finally, the performance of the proposed predictive model is assessed and compared with two well-known correlations using an independent testing database. It confirms that the proposed model not only possesses high predictability, but also provides superior performance over other existing empirical correlations.     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

A novel spatial index using spatial analyses and hierarchical fuzzy expert system for obtaining green TOD: a case study in Tehran city

Sustainable development is a vital and challenging factor for managing urban growth smartly. This factor contains three main components, namely economic growth, ecological protection and social justice. Green Transit-Oriented Development (GTOD) is a consummate planning approach in line with those components. Implementation of GTOD in an urban area is underpinned by its quantification. Therefore, a quantitative spatial index based on several indicators related to TOD and Green urbanism concepts should be developed. In this study, Geo-spatial Information Science and hierarchical fuzzy inference system (HFIS) were employed to calculate the indicators and aggregate them, respectively. In order to showcase the feasibility of the proposed method, it was implemented in a case study area in the City of Tehran, Iran. The result of this method is an integrated spatial GTOD index, which measures the neighbourhoods’ GTOD levels. These measurements specify weaknesses and strengths of neighbourhoods’ factors. Therefore, this index helps decision-makers to plan neighbourhoods based on land use and public transit views. Additionally, the HFIS method helps decision-makers to consider criteria and indicators with their inherent uncertainties and aggregate them with much fewer rules. For evaluating the results, the developed GTOD index was assessed with municipal action planning and attraction maps. According to the outcomes of the assessment, it is concluded that the proposed method is adequately robust and efficient for smart and sustainable urban planning.     

土壤遥感分类识别推理决策器的设计

介绍了干旱区土壤遥感分类识别推理决策器的设计原理与实现方法。在用TM遥感图像对土壤类型进行非监督分类的基础上，建立了正向推理与逆向推理相结合的推理机制，对土壤类型进行分类识别决策。用知识表示的产生式规则与框架式规则相结合的数据结构表示土壤学专家的土壤分类识别知识。用像结构模式建立了土壤分类识别的规则，构造了土壤分类判决树，并用典型像例模式进行了各类型土壤判据文件的组织。用该方法对新疆天山北麓阜康试验区的土壤分类识别进行了试验研究。结果表明，该方法分类精度可靠，为干旱区土壤分类识别开辟了一条新的途径。     

中国西北大旱年发生概率的统计推断

根据中国西北近500a旱涝等级资料，用Bernoulli试验等理论模式，对大旱年发生的概率特征进行了研究，得出大旱年发生的统计规律。在全球气候变暖的大背景下，给出了21世纪中国西北大旱年发生概率的统计推断结果。