湖泊水质参数空间优化估算的原理与方法

地统计学是近几十年发展起来的一门处理空间数据的数学方法,在地学方面的应用已日趋成熟.近年来,环境学的研究开始借鉴其理论,开辟了环境学研究的新方向.本文着重阐述了区域化变量理论和克立格法在湖泊水质参数分析研究中的应用,并对湖泊水质参数空间优化估算的方法进行了探讨.

复合形法在水质模型参数优化估算中的应用研究

本文根据复合形法的基本思路，探讨了利用该方法优化估算河流水质模型参数的问题。根据实测资料，运用复合形法进行演算。从复合形法的适用范围，计算机编程的复杂程度，整体工作量的大小，计算结果的代表性和合理性等方面进行了分析，并与常用的最速下降法做了对比。

基于对流层检测仪和臭氧检测仪的我国近地面NO_(2)浓度的估算对比与优化

由于二氧化氮(NO_(2))在大气中的存活寿命较短,卫星遥感反演的对流层NO_(2)柱浓度与近地面NO_(2)浓度关系密切。欧洲航天局(ESA)S5P卫星的对流层检测仪(TROPOMI)载荷提供了目前最高空间分辨率的对流层NO_(2)数据,其在近地面NO_(2)浓度估算方面的潜在优势亟待检验。为此,本文采用极限梯度提升(XGBoost)算法和4年(2018—2021年)的TROPOMI/臭氧检测仪(OMI)数据估算了我国近地面NO_(2)浓度并开展了对比性分析。结果表明:1)TROPOMI的估算结果在精度和空间覆盖度两个方面,均明显高于OMI的结果;2)OMI数据由于自身空间分辨率的限制,无法和TROPOMI一样识别出NO_(2)浓度高值区附近的空间分布细节,导致其估算结果存在更严重的高估或低估。进一步,针对机器学习方法估算近地面NO_(2)普遍存在高值低估的现象,通过集成模型进行优化,得到了更优的结果(R^(2)=0.85,slope为0.89)。该研究结果有利于促进卫星遥感在近地面NO_(2)浓度估算与暴露评估领域的深入应用。

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems

In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

Analogy-based software effort estimation using multi-objective feature selection

The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other objective is designed to minimize the number of selected features. Based on these two potential conflict objectives, a novel wrapper- based feature selection method, multi-objective feature selection for analogy-based software effort estimation （MASE）, is proposed. In the empirical studies, 77 projects in Desharnais and 62 projects in Maxwell from the real world are selected as the evaluation objects and the proposed method MASE is compared with some baseline methods. Final results show that the proposed method can achieve better performance by selecting fewer features when considering MMRE （mean magnitude of relative error）, MdMRE （median magnitude of relative error）, PRED （ 0. 25 ）, and SA （ standardized accuracy） performance metrics.

Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:A case study

In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.

Estimation of Design Sea Ice Thickness with Maximum Entropy Distribution by Particle Swarm Optimization Method

The maximum entropy distribution, which consists of various recognized theoretical distributions, is a better curve to estimate the design thickness of sea ice. Method of moment and empirical curve fitting method are common-used parameter estimation methods for maximum entropy distribution. In this study, we propose to use the particle swarm optimization method as a new parameter estimation method for the maximum entropy distribution, which has the advantage to avoid deviation introduced by simplifications made in other methods. We conducted a case study to fit the hindcasted thickness of the sea ice in the Liaodong Bay of Bohai Sea using these three parameter-estimation methods for the maximum entropy distribution. All methods implemented in this study pass the K-S tests at 0.05 significant level. In terms of the average sum of deviation squares, the empirical curve fitting method provides the best fit for the original data, while the method of moment provides the worst. Among all three methods, the particle swarm optimization method predicts the largest thickness of the sea ice for a same return period. As a result, we recommend using the particle swarm optimization method for the maximum entropy distribution for offshore structures mainly influenced by the sea ice in winter, but using the empirical curve fitting method to reduce the cost in the design of temporary and economic buildings.

Nonlinear least square estimation using difference quotient instead of derivative containing different classes of measurements

Using difference quotient instead of derivative, the paper presents the solution method and procedure of the nonlinear least square estimation containing different classes of measurements. In the meantime, the paper shows several practical cases, which indicate the method is very valid and reliable.

Safety Evaluation Method of Evacuation Routes in Areas in Case of Earthquake Disasters Using Ant Optimization Algorithm and Geographic Information Systems

The present study aims to propose the method for the quantitative evaluation of safety concerning evacuation routes in case of earthquake disasters in urban areas using ACO （Ant Colony Optimization） algorithm and GIS （Geographic Information Systems）. Regarding the safety evaluation method, firstly, the similarity in safety was focused on while taking into consideration road blockage probability, and after classifying roads by means of the hierarchical cluster analysis, the congestion rates of evacuation routes using ACO simulations were estimated. Based on these results, the multiple evacuation routes extracted were visualized on digital maps by means of GIS, and its safety was evaluated. Furthermore, the selection of safe evacuation routes between evacuation sites, for cases when the possibility of large-scale evacuation after an earthquake disaster is high, is made possible. As the safety evaluation method is based on public information, by obtaining the same geographic information as the present study, it is effective in other areas regardless of whether the information is of the past and future. Therefore, in addition to spatial reproducibility, the safety evaluation method also has high temporal reproducibility. Because safety evaluations are conducted on evacuation routes based on quantified data, highly safe evacuation routes that are selected have been quantitatively evaluated, and thus serve as an effective indicator when selecting evacuation routes.

非线性动力系统的稳定区域理论、估算和应用

这本权威性著作覆盖了非线性动力系统的理论、优化估算和一系列实际应用。这是在非线性动力系统领域中起主导作用的研究人员所写的第一本有关主题的著作，对一系列非线性动力系统的稳定边界和稳定区域，提供了清晰而严密的全方位的理论，包括：连续的、

UNIFORM OPTIMAL-ORDER ESTIMATES FOR FINITE ELEMENT METHODS FOR ADVECTION-DIFFUSION EQUATIONS

This article summarizes our recent work on uniform error estimates for various finite elementmethods for time-dependent advection-diffusion equations.