Choosing a municipal landfill site by analytic network process

In this study, analytic network process (ANP), one of the multi-criteria decision making (MCDM) tools has been used to choose one of the four alternative landfill sites for the city of Eskisehir, Turkey. For this purpose, Super Decision Software has been used and benefit opportunity cost and risk (BOCR) analysis has been done to apply ANP. In BOCR analysis, each alternative site has been evaluated in terms of its benefits, costs and risks; the opportunity cluster has been examined under the benefit cluster. In this context, technical, economical and social assessments have been done for the site selection of sanitary landfill. Also, results have been compared with analytic hierarchy process (AHP) which is another MCDM technique used in the study conducted before. Finally, the current site has been determined as the most appropriate site in both methods. These methods have not been commonly used in the discipline of environmental engineering but it is believed to be an important contribution for decision makers.     

巢式监测井成井技术

从巢式监测井选用的材料、巢式监测井的安装、巢式监测井安装时注意事项、巢式监测井的清洗取样、以及在地下水污染监测方面的实际应用做了简单介绍，为地下水污染监测提供了一种新的参考。     

Revisiting prior distributions,Part II: Implications of the physical prior in maximum entropy analysis

The well-known “Maximum Entropy Formalism” offers a powerful framework for deriving probability density functions given a relevant knowledge base and an adequate prior. The majority of results based on this approach have been derived assuming a flat uninformative prior, but this assumption is to a large extent arbitrary (any one-to-one transformation of the random variable will change the flat uninformative prior into some non-constant function). In a companion paper we introduced the notion of a natural reference point for dimensional physical variables, and used this notion to derive a class of physical priors that are form-invariant to changes in the system of dimensional units. The present paper studies effects of these priors on the probability density functions derived using the maximum entropy formalism. Analysis of real data shows that when the maximum entropy formalism uses the physical prior it yields significantly better results than when it is based on the commonly used flat uninformative prior. This improvement reflects the significance of the incorporating additional information (contained in physical priors), which is ignored when flat priors are used in the standard form of the maximum entropy formalism. A potentially serious limitation of the maximum entropy formalism is the assumption that sample moments are available. This is not the case in many macroscopic real-world problems, where the knowledge base available is a finite sample rather than population moments. As a result, the maximum entropy formalism generates a family of “nested models” parameterized by the unknown values of the population parameters. In this work we combine this formalism with a model selection scheme based on Akaike’s information criterion to derive the maximum entropy model that is most consistent with the available sample. This combination establishes a general inference framework of wide applicability in scientific/engineering problems.     

改进微粒群算法在梯级电站长期优化调度中的应用

微粒群算法是一种简洁高效的智能优化算法，但基本算法容易陷入局部最优，并且搜索精度不高。本文在基本算法的基础上引入锦标赛选择机制和自适应惯性权重因子，提出了改进微粒群算法（MPSO）。将MPSO算法应用到黄河上游梯级电站的长期调度中，并与动态规划法和基本算法的调度结果相比较。实例表明了MPSO算法的有效性和可靠性，从而为梯级电站水库（群）长期优化调度提供了一种新的、有效的优化方法。     

高光谱数据的降维及Tabu搜索算法的应用

在高光谱影像的分类过程中,如何有效地降低特征空间的维数,又能保证原始数据所包含的丰富地物信息是一项十分重要而繁琐的工作。深入分析了这种降维的必要性,并针对当前常用的降维方法存在的问题,提出了运用Tabu搜索算法获取对分类最为有利的特征波段的思想。考虑到高光谱数据的特点,指出了算法运行中应该注意的若干关键参数设置问题。实验表明,Tabu搜索算法在求解质量和执行效率方面都有着良好的表现,可以用于高光谱数据的降维处理。     

Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier

In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification.     

Improving settlement selection for small-scale maps using data enrichment and machine learning

Acquiring and formalizing cartographic knowledge still is a challenge, especially when the generalization process concerns small-scale maps. We concentrate on the settlement selection process for small-scale maps, with the aim of rendering it more holistic, and making methodological contributions in four areas. First, we show how written specifications and rules can be validated against the actual published map products, thus pointing to gaps and potential improvements. Second, we use data enrichment based on supplementing information extracted from point-of-interest data in order to assign functional importance to particular settlements. Third, we use machine learning (ML) algorithms to infer additional rules from existing maps, thus making explicit the deep knowledge of cartographers and allowing to extend the cartographic rule set. And fourth, we show how the results of ML can be transformed into human-readable form for potential use in the guidelines of national mapping agencies. We use the case of settlement selection in the small-scale maps published by the Polish national mapping agency (GUGiK). However, we believe that the methods and findings of this paper can be adapted to other environments with minor modifications.     

红河油田地层可钻性研究与钻头优选

红河油田水平井钻头适应性差、起下钻频繁,导致机械钻速低和钻井周期长,严重制约了钻井提速提效。通过对红河油田地层岩心进行室内可钻性实验,建立了该地区岩石可钻性与声波测井相关性数学模型,从而确定了岩石可钻性级值剖面。实验及计算结果表明,红河油田地层可钻性级别为3～6级,上部属于软地层,下部属于中硬地层。基于地层可钻性优选了PDC钻头,经现场试验与应用,平均单只钻头进尺、机械钻速大幅提高,大大缩短了钻井周期,达到了水平井钻井提速提效的目的。     

干热岩勘查开发工程场地选址评价指标体系研究

干热岩是储量大、用途广的新型、可再生绿色能源,对其进行开发利用意义重大。就我国现状而言,寻找适宜的干热岩勘查开发工程场地是成功实现干热岩开发利用的关键。从干热岩工程开发亟待解决的选址问题出发,系统地总结了国内外干热岩勘查开发的选址经验,从资源、技术、安全和经济4个方面建立了包含4个指标层共20个指标的干热岩勘查开发工程场地选址评价指标体系; 基于层次分析法,计算了各评价指标的权重,并提出了基于层次分析-综合评分法的干热岩勘查开发工程场地选址评价方法。该指标体系和评价方法对干热岩勘查开发工程场地选址具有一定指导意义。