改进的最优窗宽理论对年径流量的插值预测研究——以黄河利津等站为例

针对小样本数据造成年径流量预测效果不理想的情况,以及非对称和非正态资料的处理问题,引入信息扩散和模糊映射思想,同时运用遗传算法改进最优窗宽理论,建立了新的扩散插值模型。该模型通过对零散数据点的信息进行模糊扩散,进而实现对有限数据点信息向其邻近区域点的概率插值。选取黄河利津站为例,根据其近70 a(1942—2011年)径流量实测数据,进行了缺损数据的插值和预测试验,同时与正态扩散插值模型进行对比分析,结果表明:1)预测值能较好地模拟实际径流序列的波形变化,对丰水年(如2007年)和枯水年(如2009年)的预报都比较准确;2)中长期预报(10a)平均相对误差仅为11.59%,相较传统方法有较大的改进;3)以黄河流域2个站点(花园口和兰州)和长江流域的3个站点(朱沱、宜昌和大通)年径流量预测试验以及海温资料的插值试验作为补充,验证了该算法的有效性和普适性。该模型可为实际水文数据资料的客观分析和中长期预报提供参考。     

小生境遗传算法求解多峰问题在反演中应用

基于反演问题的不确定性和目标函数的多峰性，引入改进的小生境遗传算法，求解出目标函数的若干个局部峰（或全局峰），然后利用先验知识，判定得到满意解，并利用褶积模型进行层速度反演，通过理论速度与反演速度的比较，验证了进行多峰优化的有效性。     

An Improved Method for Calculating Paleoheat Flow from Vitrinite Reflectance Profiles

lNTRODUCTIONIt is wel1 established that the vitrinite reflectance (R<, ) de-pends on the temperature history that a basin has undergone.In general, Ro is a function of maximum paleotemperature andeffective heating time. Thus, the paleotemperature of sedi-ments and the value of vitrinite ref1ectance are controlled by theflow of heat Q(t) into the basin and by the thermal parametersof the sediments. Therefore, in principle, it is possible to cal-culate the thermal vitrinite reflectance of …     

Real-time genetic spatial optimization to improve forest fire spread forecasting in high-performance computing environments

Abstract

Forest fires are a kind of natural hazard with a high number of occurrences in southern European countries. To avoid major damages and to improve forest fire management, one can use forest fire spread simulators to predict fire behavior. When providing forest fire predictions, there are two main considerations: accuracy and computation time. In the context of natural hazards simulation, it is well known that part of the final forecast error comes from uncertainty in the input data. These data typically consist of a set of GIS files, which should be appropriately conflated. For this reason, several input data calibration methods have been developed by the scientific community. In this work, the Two-Stage calibration methodology, which has been shown to provide good results, is used. This calibration strategy is computationally intensive and time-consuming because it uses a Genetic Algorithm as a solution. Taking into account the aspect of urgency in forest fire spread prediction, it is necessary to maintain a balance between accuracy and the time needed to calibrate the input parameters. In order to take advantage of this technique, one must deal with the problem that some of the obtained solutions are impractical, since they involve simulation times that are too long, preventing the prediction system from being deployed at an operational level. A new method which finds the minimum resolution reduction for such long simulations, keeping accuracy loss to a known interval, is proposed. The proposed improvement is based on a time-aware core allocation policy that enables real-time forest fire spread forecasting. The final prediction system is a cyberinfrastructure, which enables forest fire spread prediction at real time.     

Optimizing the reservoir operating rule curves by genetic algorithms

Genetic algorithms, founded upon the principle of evolution, are applicable to many optimization problems, especially popular for solving parameter optimization problems. Reservoir operating rule curves are the most common way for guiding and managing the reservoir operation. These rule curves traditionally are derived through intensive simulation techniques. The main aim of this study is to investigate the efficiency and effectiveness of two genetic algorithms (GAs), i.e., binary coded and real coded, to derive multipurpose reservoir operating rule curves. The curves are assumed to be piecewise linear functions where the coordinates of their inflection points are the unknowns and we want to optimize system performance. The applicability and effectiveness of the proposed methods are tested on the operation of the Shih‐Men reservoir in Taiwan. The current M‐5 operating curves of the Shih‐Men reservoir are also evaluated. The results show that the GAs provide an adequate, effective and robust way for searching the rule curves. Both sets of operating rule curves obtained from GAs have better performance, in terms of water release deficit and hydropower, than the current M‐5 operating rule curves, while the real‐coded GA is more efficient than the binary‐coded GA. Copyright © 2005 John Wiley & Sons, Ltd.     

Single‐and multi‐objective genetic algorithm optimization for identifying soil parameters

This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM‐simulation for which two constitutive models—a linear elastic perfectly plastic Mohr–Coulomb model and a strain‐hardening elasto‐plastic model—are successively considered. Two kinds of optimization algorithms have been used: a deterministic simplex method and a stochastic genetic method. The soil data come from the results of two pressuremeter tests, complemented by triaxial and resonant column testing. First, the inverse analysis has been performed separately on each pressuremeter test. The genetic method presents the advantage of providing a collection of satisfactory solutions, among which a geotechnical engineer has to choose the optimal one based on his scientific background and/or additional analyses based on further experimental test results. This advantage is enhanced when all the constitutive parameters sensitive to the considered problem have to be identified without restrictions in the search space. Second, the experimental values of the two pressuremeter tests have been processed simultaneously, so that the inverse analysis becomes a multi‐objective optimization problem. The genetic method allows the user to choose the most suitable parameter set according to the Pareto frontier and to guarantee the coherence between the tests. The sets of optimized parameters obtained from inverse analyses are then used to calculate the response of a spread footing, which is part of a predictive benchmark. The numerical results with respect to both the constitutive models and the inverse analysis procedure are discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

INTELLIGENT ALGORITHMS FOR SOLVING CNOP AND THEIR APPLICATIONS IN ENSO PREDICTABILITY AND TROPICAL CYCLONE ADAPTIVE OBSERVATIONS

Some intelligent algorithms (IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane (ZC) model to solve conditional nonlinear optimal perturbation (CNOP) for studying El Ni?o – Southern Oscillation (ENSO) predictability. Compared to the adjoint-based method (the ADJ-method), which is referred to as a benchmark, these IAs can achieve approximate CNOP results in terms of magnitudes and patterns. Using IAs to solve CNOP can avoid the use of an adjoint model and widen the application of CNOP in numerical climate and weather modeling. Of the proposed swarm IAs, PCA-based particle swarm optimization (PPSO) obtains CNOPs with the best patterns and the best stability. Of the proposed single individual IAs, continuous tabu search algorithm with sine maps and staged strategy (CTS-SS) has the highest efficiency. In this paper, we compare the validity, stability and efficiency of parallel PPSO and CTS-SS using these two IAs to solve CNOP in the ZC model for studying ENSO predictability. The experimental results show that CTS-SS outperforms parallel PPSO except with respect to stability. At the same time, we are also concerned with whether these two IAs can effectively solve CNOP when applied to more complicated models. Taking the sensitive areas identification of tropical cyclone adaptive observations as an example and using the fifth-generation mesoscale model (MM5), we design some experiments. The experimental results demonstrate that each of these two IAs can effectively solve CNOP and that parallel PPSO has a higher efficiency than CTS-SS. We also provide some suggestions on how to choose a suitable IA to solve CNOP for different models.     

Damage Identification of A TLP Floating Wind Turbine by Meta-Heuristic Algorithms

Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring (SHM). In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP (Tension-Leg Platform) floating wind turbine structure. The Genetic Algorithms (GA), Artificial Immune System (AIS), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine (TLPFWT) is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.     

资源三号卫星影像融合算法对比分析

我国第一颗高精度民用立体测绘卫星——资源三号卫星搭载了2.1m分辨率的全色相机和5.8m分辨率的多光谱相机,针对资源三号影像研究有效的影像融合算法对于提高资源三号的影像质量和应用范围具有很大意义。文章分析了Pansharp、Gram-Schmidt、Modified IHS、改进的SFIM和Brovey 5种融合算法,梳理了影像融合的质量评价指标体系,采用资源三号卫星影像覆盖的农田、城区、林地、裸地等不同类型的区域开展几种融合算法试验,并对试验结果进行了分析评价。实验结果表明,5种融合算法中,Pansharp融合算法对资源三号影像数据融合效果最优,Gram-Schmidt融合算法次之,其次为Modified IHS影像融合算法,改进的SFIM融合算法和Brovey融合算法融合效果较差。