基于多属性决策的统计数据分级综合评价模型

将决策理论中的多属性决策用于评价地图要素的分级(分类)方法是否最优,并用信息论中的熵函数法确定各个评价分级精度指标的权重,建立了统计数据分级(分类)的综合评价模型。模型的建立,不仅提高了评价的客观性,而且是建立分级方法与适合这种分级方法的数据之间的关系基础。最后,用实例说明这种方法的具体应用及计算过程。     

近50年我国持续性暴雨的统计分析及其大尺度环流背景

根据1951～2005年中国大陆730个台站的逐日降水资料和持续性暴雨发生的特点, 提出了采用局地持续性暴雨和区域持续性暴雨事件的两种客观定义来描述和统计近50年我国持续性暴雨时空特征及其变化。单站逐日降水量连续三天或三天以上均大于等于50 mm为一次局地持续性暴雨；区域持续性暴雨指在一定区域范围内连续三天降水量总和大于等于100 mm且每天降水量大于等于25 mm的面积超过某一阈值 (详细定义见正文）。根据局地持续性暴雨定义, 指出: 近50年中国局地持续性暴雨事件主要发生在江南和华南地区, 发生季节以6月为最多；根据区域持续性暴雨定义, 统计分析了四类典型的区域持续性暴雨类型, 分别是: 渤海辽西型、北方经向型、南方锋面型和华南低压型。其中南方锋面型又可根据持续性暴雨易发生的地理位置分为江淮型、江南型和华南型三种。对这六类区域持续性暴雨的历史个例进行同比分析, 研究了不同类型持续性暴雨发生的季节性和年际变化,以及它们在大尺度环流背景方面的共性特征。     

上海区域要素客观预报方法效果检验

数值模式的客观释用是数值模式在业务工作中发挥效能的重要环节。在对上海区域数值模式近5年的模式直接输出(DMO)进行检验的基础上,分别采用卡尔曼滤波释用(KLM)和最优化集成释用(OCF)的方法进行要素客观释用,总结出区域数值模式的预报性能,客观释用也取得令人鼓舞的结果:(1)近几年,上海区域数值模式的直接输出结果(DMO)对温度、湿度以及风向的预报改善不明显,甚至还有变差现象,风速的预报自2005年起有改善,但主要体现在预报的稳定性方面。(2)KLM方法较DMO在温度、相对湿度和风速的预报上均有明显提高,但是风向的预报无明显提高,预报准确率甚至略有下降。(3)OCF方法的预报性能较KLM方法略有提高。温度、相对湿度以及风向的预报准确率提高约2%,风速预报与KLM方法相当。在春季和冬季,OCF的预报水平已经与主观综合预报相当,如果主观综合预报能充分参考OCF的预报结果,主观综合预报"春季和冬季预报误差相对偏大"这一弱点能得到改善。检验结果能为数值模式的开发和调试者提供有益的参考,而最优化集成方法的成功业务尝试也佐证了集成预报在数值模式客观释用中的美好前景。     

A METHOD FOR PREDICTING SEASONAL PRECIPITATION ANOMALY DISTRIBUTION IN SUMMER OVER CHINA

Some main features of spatial-temporal variation of precipitation field in summer over Chinahave been analysed and a skillfull method with physical and statistical consideration for predictingprecipitation anomaly distribution in summer over China is suggested.The predictive skill forindependent sample of 1994-1998 with the method is assessed,i.e.the averaged anomalycorrelation coefficient between the observed and predicted precipitation filed over China is 0.26.     

矿山露天转井下境界顶柱的数值模拟优化方法

露天底境界顶柱优化是露天转井下过渡时期的重要课题,该优化问题包括多个决策变量和多个评价指标,已有方法的优化效果不够理想。针对露天转井下境界顶柱优化特点,将遗传算法与三维数值模拟相结合,研究了境界顶柱全局优化的进化数值模拟方法,给出优化的指标和步骤,并将该方法用于大杏山铁矿的露天转井下境界顶柱的优化。工程应用表明所提出的井下境界顶柱优化方法是可行的。获得的最优方案对该矿山露天转井下的采矿设计具有指导作用。     

挡土墙土体复合破裂面主动土压力分析

基于普朗德尔-瑞斯纳课题认为,挡土墙土体破裂面是由对数螺线与直线组成的复合滑动面的结论,采用极限平衡法和约束优化相结合的方法,重新推导了主动土压力及其合力和作用点高度的计算公式。在此基础上分析了主动土压力在纵向的分布特征,并讨论了主要参数对破裂面、主动土压力及其合力和作用点的影响。     

常熟市可持续农业与农村发展系统评价及其规划

杏持续农业与农村发展（ＳＡＲＤ）是发达国家与发展中国家面临的共同问题,而不同地区ＳＡＲＤ所处的阶段不同,发展模式也各具特色。科学地确定其发展偏离度并为其未来发展作出合理的规划,具有重要的理论与实践意义。文章构建了ＳＡＲＤ多目标规划模型,运用它对常熟市过去８年进行了系统评价,并对未来发展进行了动态规划,得出了３个不同的投入方案。     

Assessment of the adequacy of the regional relationships between catchment attributes and catchment response dynamics,calibrated by a multi‐objective approach

This study aimed to evaluate the effectiveness of the regionalization method on the basis of a combination of a parsimonious model structure and a multi‐objective calibration technique. For this study, 12 gauged catchments in the Republic of Korea were used. The parsimonious model structure, requiring minimal input data, was used to avoid adverse effects arising from model complexity, over‐parameterization and data requirements. The IHACRES rainfall‐runoff model was applied to represent the dynamic response characteristics of catchments in Korea. A multi‐objective approach was adopted to reduce the predictive uncertainty arising from the calibration of a rainfall‐runoff model, by increasing the amount of information retrieved from the available data. The regional relationships (or models) between the model parameters and the catchment attributes were established via a multiple regression approach, incorporating correlation analysis and stepwise regression on linear and logarithmic scales. The impacts of the parameters, calibrated by the multi‐objective approach, on the adequacy of regional relationships were assessed by comparison with impacts obtained by the single‐objective approach. The regional relationships were well defined, despite limited available data. The drainage area, the effective soil depth, the mean catchment slope and the catchment gradient appeared to be the main factors for describing the hydrologic response characteristics in the areas studied. The overall model performance of the regional models based on the multi‐objective approach was good, producing reasonable results for high and low flows and for the overall water balance, simultaneously. The regional models based on the single‐objective approach yielded accurate predictions in high flows but showed limited predictive capability for low flows and the overall water balance. This was due to the optimal model parameter estimates when using a single‐objective measure. The parameters calibrated by the single‐objective approach decreased the predictability of the regional models. Copyright © 2013 John Wiley & Sons, Ltd.     

On the use of multi‐algorithm,genetically adaptive multi‐objective method for multi‐site calibration of the SWAT model

With the availability of spatially distributed data, distributed hydrologic models are increasingly used for simulation of spatially varied hydrologic processes to understand and manage natural and human activities that affect watershed systems. Multi‐objective optimization methods have been applied to calibrate distributed hydrologic models using observed data from multiple sites. As the time consumed by running these complex models is increasing substantially, selecting efficient and effective multi‐objective optimization algorithms is becoming a nontrivial issue. In this study, we evaluated a multi‐algorithm, genetically adaptive multi‐objective method (AMALGAM) for multi‐site calibration of a distributed hydrologic model—Soil and Water Assessment Tool (SWAT), and compared its performance with two widely used evolutionary multi‐objective optimization (EMO) algorithms (i.e. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non‐dominated Sorted Genetic Algorithm II (NSGA‐II)). In order to provide insights into each method's overall performance, these three methods were tested in four watersheds with various characteristics. The test results indicate that the AMALGAM can consistently provide competitive or superior results compared with the other two methods. The multi‐method search framework of AMALGAM, which can flexibly and adaptively utilize multiple optimization algorithms, makes it a promising tool for multi‐site calibration of the distributed SWAT. For practical use of AMALGAM, it is suggested to implement this method in multiple trials with relatively small number of model runs rather than run it once with long iterations. In addition, incorporating different multi‐objective optimization algorithms and multi‐mode search operators into AMALGAM deserves further research. Copyright © 2009 John Wiley & Sons, Ltd.     

Investigating model performance and parameter sensitivity for runoff simulation across multiple events for a large green roof

Green roofs are a form of green infrastructure aimed at retaining or slowing the movement of precipitation as stormwater runoff to sewer systems. To determine total runoff versus retention from green roofs, researchers and practitioners alike employ hydrologic models that are calibrated to one or more observed events. However, questions still remain regarding how event size may impact parameter sensitivity, how best to constrain initial soil moisture (ISM), and whether limited observations (i.e., a single event) can be used within a calibration-validation framework. We explored these questions by applying the storm water management model to simulate a large green roof located in Syracuse, NY. We found that model performance was very high (e.g., Nash Sutcliffe efficiency index > 0.8 and Kling-Gupta efficiency index > 0.8) for many events. We initially compared model performance across two parameterizations of ISM. For some events, we found similar performance when ISM was varied versus set to zero; for others, varying ISM yielded higher performance as well as greater water balance closure. Within a calibration-validation framework, we found that calibrating to larger events tended to produce moderate to high performance for other non-calibration events. However, very small storms were notoriously difficult to simulate, regardless of calibration event size, as these events are likely fully retained on the roof. Using regional sensitivity analysis, we confirmed that only a subset of model parameters was sensitive across 16 events. Interestingly, many parameters were sensitive regardless of event size, though some parameters were more sensitive when simulating smaller events. This emphasizes that storm size likely influences parameter sensitivity. Overall, we show that while calibrating to a single event can achieve high performance, exploring simulations across multiple events can yield important insight regarding the hydrologic performance of green roofs that can be used to guide the gathering of in situ properties and observations for refining model frameworks.