COUNCIL MEETING,CHICAGO, ILLINOIS November 3-4, 1972

This paper questions the validity and relevance of the application of procedures of classical statistical inference to population data in geography. Arguments for such procedures include the notions of temporal and spatial samples, measurement error, modifiable areal units, data vetting and stochastic processes. It is concluded that such arguments cannot be justified in terms of statistical theory.     

Comparative analysis of fuzzy inference systems for water consumption time series prediction

Two types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series.     

基于钻孔数据的含断层地质体三维建模方法

提出了一种构建含断层地质体三维模型的新方法。该方法以广义三棱柱作为建模的基本体元,根据钻孔数据的特点和知识推理规则,进行断层等复杂地质构造的推理和自动建模,避免了不必要的人为干预,扩展了钻孔数据建模的适用范围和表现能力。最后,通过研究实例展示该方法的实际建模效果。      

遥感影像构造地质解译过程中的逻辑形式与逻辑规律讨论

遥感图像是地表地物表征的集合，构成影像景观单元。遥感解译属于人脑意识活动的范畴，是利用表层信息由表及里、由此及彼的逻辑推理过程。探求因果联系的科学归纳法，探求等同关系的类比推理法，探求真实性的科学论证法是遥感解译过程中应遵循的思想方法。     

预报指标自动归纳和实时自动推理预报系统

本文结合实际应用介绍一种人工智能系统。系统的重要特点是,根据预报指标经验知识的产生过程的机理性知识所设计成功的系统主程序之一的“预报指标自动归纳程序”,能够通过对多因子历史资料的分析归纳,自动生成含有较全面知识规则的规则库。有关专家只须凭感性经验提供可能因子和原始资料,系统便能帮助升华出理性的经验规则,自动建成某一专项业务的应用预报系统。对这种“母系统”的反复使用,就可以根据不同类别的资料生成相应的多种应用系统。既适用于气象上风雨暑寒各类天气预报,也适用于其他某些专业,如水文、地震等的定性预报或分析。 本文除简单介绍系统的构造、功能、设计原理之外,还给出一个应用实例,介绍了如何使用本系统去产生应用性系统的具体方法步骤。     

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。     

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。     

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     

Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems

In this paper, a Bayesian sequential sensor placement algorithm, based on the robust information entropy, is proposed for multi‐type of sensors. The presented methodology has two salient features. It is a holistic approach such that the overall performance of various types of sensors at different locations is assessed. Therefore, it provides a rational and effective strategy to design the sensor configuration, which optimizes the use of various available resources. This sequential algorithm is very efficient due to its Bayesian nature, in which prior distribution can be incorporated. Therefore, it avoids the possible unidentifiability problem encountered in a sequential process, which starts with small number of sensors. The proposed algorithm is demonstrated using a shear building and a lattice tower with consideration of up to four types of sensors. Copyright © 2014 John Wiley & Sons, Ltd.