Model-free estimation from spatial samples: A reappraisal of classical sampling theory

A commonly held view among geostatisticians is that classical sampling theory is inapplicable to spatial sampling because spatial data are dependent, whereas classical sampling theory requires them to be independent. By comparing the assumptions and use of classical sampling theory with those of geostatistical theory, we conclude that this view is both false and unfortunate. In particular, estimates of spatial means based on classical sampling designs require fewer assumptions for their validity, and are therefore more robust, than those based on a geostatistical model.     

基于小波分解和ANFIS模型的赤道东太平洋海温集成预测

用小波分解和自适应神经模糊推理系统（ANFIS）相结合的方法，建立了赤道东太平洋海温的集成预报模型。该方法将复杂海温系统分解为相对简单的带能分量信号，然后建立分量信号的独立预报模型，最后对预报结果进行集成。试验结果表明，模型的保留预报对象主要特征的前提下，有效地降低了预报难度，集成预报准确率和预报时效传统方法有明显的改进和提高。     

Intelligent control for modeling of real‐time reservoir operation,part II: artificial neural network with operating rule curves

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network‐based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M‐5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input–output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M‐5 curves in real‐time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved. Copyright © 2004 John Wiley & Sons, Ltd.     

Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

基于规则知识表示的模型单元选择专家系统的实现

本文根据矿床统计预测中模型单元选择的领域专家知识，用基于规则知识表示的ＧＵＲＵ专家系统工具及Ｃ语言建造了一个模型单元选择的专家系统及其咨询界面，解决了如何确定最佳的模型单元的这一难题，最终形成了模型单元数据库，这就提高了数学模型的精度和预测的效果，为用户提供了一个高效率的计算机辅助工具。     

Development of paleolimnological inference models for pH, total nitrogen and specific conductivity based on planktonic diatoms

Canonical correspondence analysis (CCA was used to explore and identify statistically significant relationships between the distributions of planktonic diatoms and the physical and chemical properties of 50 Connecticut lakes. Six variables (pH, total nitrogen, calcium, sulfate, potassium and chlorophyll- a concentrations) were found to be significantly correlated with either or both of the first two extracted axes. The pH and calcium concentration, and to a lesser extent total nitrogen concentrations, were the most important variables controlling the distributions of planktonic diatoms in this suite of lakes. Paleolimnological inference models were developed for pH, total nitrogen (TN) and specific conductivity. Weighted averaging with (WAtol) and without (WA) tolerance downweighting, with and without bootstrap resampling techniques, and using either classical or inverse deshrinking methods were used to develop inference models for each variable. The pH and TN yielded sufficiently high 1/2 ratios and a highly significant first (constrained) axis when entered as single variables in both constrained and partially constrained CCA analyses, supporting the idea that reliable inference models could be developed for these variables. The r2 and RMSE of prediction values ranged from 0.73 to 0.86 and 0.37 to 0.6, respectively for pH, and from 0.4 to 0.64 and 59 g/l to 95 g/l, respectively for TN. Inference models for specific conductivity also yielded significant goodness-of-fit statistics. However, because specific conductivity was removed from the CCA analysis due to its high variance inflation factor and did not yield a significant relationship when entered as the sole variable in a partial constrained CCA, inference models for this variable will probably not yield any additional environmental information. The use of only planktonic diatoms in construction of inference models is discussed.     

Statistical estimation of the relative efficiency of natural attenuation mechanisms in contaminated aquifers

Using monitored natural attenuation is an increasingly popular strategy for dealing with contaminated aquifers. This paper provides a statistical methodology for the estimation of the relative efficiency of natural attenuation mechanisms. The methodology provides estimates, with associated measures of uncertainty, of the relative efficiency of four types of bio-degradation (oxidation using oxygen as the electron-acceptor, denitrification, iron reduction and sulfate reduction). A data set from Trecate, Italy, is analysed using the methodology. The analysis shows that sulfate is the main cause of hydrocarbon removal on this site. It is also seen that oxidation using oxygen seems to be more preferential than the other reactions, in the sense that this reaction is relatively more efficient than other reactions at locations where the hydrocarbon concentration is low.     

Design, analysis, and inference for studies comparing thematic accuracy of classified remotely sensed data: a special case of map comparison

Assessing thematic map accuracy is a special type of map comparison that is frequently applied to remote sensing classification problems. For map comparisons in the accuracy assessment setting, one map represents the classified output and the other map represents the true or “reference” condition. Several articles in this special issue describe state-of-the-art map comparison analysis tools that could serve to quantify accuracy of a single map. However, accuracy assessment objectives generally extend beyond describing accuracy of a single map to comparing accuracy of several maps. Consequently, interest focuses on comparing map comparison measures when these measures are used to represent accuracy. The virtual workshop emphasizes the analysis component of map comparisons, but it is also important to examine the underlying study designs generating the data input into these analyses. The study designs for accuracy comparisons implemented in remote sensing practice often investigate only a single test site, thus limiting our ability to generalize the results of these accuracy comparisons. Map accuracy comparison studies can be designed to provide stronger generalizations by incorporating experimental design principles such as replication and blocking, and identifying an experimental unit appropriate for the application. It is also important to recognize the role of statistical hypothesis testing and inference for different objectives that motivate map accuracy comparisons. Deciding which of two maps to use for a particular site can be addressed by enumerative inference and does not require hypothesis testing. For the objective of a more general comparison of classification procedures, analytic inference is appropriate and hypothesis testing plays a more prominent role.     

边坡变形分析与预报的模糊人工神经网络方法

边坡变形受地质、水文、气候、突发因素（如暴雨、地震）、人工活动（如开挖或防护）等各种因素的影响,这些因素与变形之间一般不是确定的定量关系。本文介绍了不同影响因素与位移量的模糊关系及位移量的模糊近似推论,在此基础上应用模糊人工神经网络进行位移量预报,并以一个边坡监测资料为例说明了具体的应用方法。