汶川地震在江苏的强震动记录及地震影响场

2008年5月12日四川汶川发生M8.0大地震,江苏数字强震动台网固定观测台站、宿迁和溧阳断层观测存放台阵共获取该次地震有效强震动事件45个,计134条加速度记录。本文介绍了这些强震动记录分析处理的基本情况及其时域与频域的一些特点;编写SICalculate谱烈度计算程序和D2InterP二维插值程序,输入所获取的汶川地震26组实测强震动记录数据,得到该次地震在江苏地区的峰值加速度与谱烈度的等值线图,并就区域内地震影响场特征进行了初步研究。     

Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community

A long‐standing “Digital Divide” in data representation exists between the preferred way of data access by the hydrology community and the common way of data archival by earth science data centers. Typically, in hydrology, earth surface features are expressed as discrete spatial objects (e.g., watersheds), and time‐varying data are contained in associated time series. Data in earth science archives, although stored as discrete values (of satellite swath pixels or geographical grids), represent continuous spatial fields, one file per time step. This Divide has been an obstacle, specifically, between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. and NASA earth science data systems. In essence, the way data are archived is conceptually orthogonal to the desired method of access. Our recent work has shown an optimal method of bridging the Divide, by enabling operational access to long‐time series (e.g., 36 years of hourly data) of selected NASA datasets. These time series, which we have termed “data rods,” are pre‐generated or generated on‐the‐fly. This optimal solution was arrived at after extensive investigations of various approaches, including one based on “data curtains.” The on‐the‐fly generation of data rods uses “data cubes,” NASA Giovanni, and parallel processing. The optimal reorganization of NASA earth science data has significantly enhanced the access to and use of the data for the hydrology user community.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

基于MODIS地面温度数据的成都市热岛时空变化

研究利用2000、2005和2010年逐月的MODIS/Terra和MODIS/Aqua卫星的地表温度数据产品,通过等间距法进行分析,得出成都市建成区2000~2010年间昼夜热岛的时空变化以及相互间差异。研究结果表明:成都市昼夜热岛存在显著差异,日间热岛主要呈散点分布,夜间热岛集中在城市中心区域。日夜间热岛变化趋势有显著不同,日间热岛继续分散而夜间热岛范围则有所扩大。日间热岛分布与植被覆盖存在一定的负相关性,但道路也在一定程度上促使了地面温度的增加,夜间城市热岛范围及变化则与其下垫面组成存在关系。     

Methodology of industry scale analysis under the constraint of regional resource capacity using HSY algorithm

This paper develops a methodology of proper scale analysis for regional industry development, which can be used in industry planning with the consideration of regional resource capacities. In the face of different data sources and even data scarcity, alternative methods based on linear programming and quadratic programming algorithms for calculating the resource intensity factors are designed. Based on the empirical study of industrialization, initial scenarios of industry development were set. Using HSY algorithm, sensitive industries that may cause exceeding regional resource capacity can be identified, and the risk of exceeding can be predicted and expressed in probability. Furthermore, a proper scale range can be designed for these sensitive industries according to resource capacity. Taking the case of Dalian city in China, this paper estimated the regional urban development plan, various resources capacities were studied, and land resources were estimated to be the most critical resource for the city. The land resource depletion intensities of different industries are calculated by quadratic programming algorithm. Under the constraint of 427.56 km² available industrial land resources, the electronic and power industries have the most significant impact on total land use, if the scale of power industry exceeds 1.27 billion USD, the probability of land resource capacity breakthrough will be 50%.     

River channel network design for drought and flood control: A case study of Xiaoqinghe River basin, Jinan City, China

Vulnerability of river channels to urbanization has been lessened by the extensive construction of artificial water control improvements. The challenge, however, is that traditional engineering practices on isolated parts of a river may disturb the hydrologic continuity and interrupt the natural state of ecosystems. Taking the Xiaoqinghe River basin as a whole, we developed a river channel network design to mitigate river risks while sustaining the river in a state as natural as possible. The river channel risk from drought during low-flow periods and flood during high-flow periods as well as the potential for water diversion were articulated in detail. On the basis of the above investigation, a network with “nodes” and “edges” could be designed to relieve drought hazard and flood risk respectively. Subsequently, the shortest path algorithm in the graph theory was applied to optimize the low-flow network by searching for the shortest path. The effectiveness assessment was then performed for the low-flow and high-flow networks, respectively. For the former, the network connectedness was evaluated by calculating the “gamma index of connectivity” and “alpha index of circuitry”; for the latter, the ratio of flood-control capacity to projected flood level was devised and calculated. Results show that the design boosted network connectivity and circuitry during the low-flow periods, indicating a more fluent flow pathway, and reduced the flood risk during the high-flow periods.     

模拟退火算法在大气环境质量综合评价中的应用

为了建立一种对多项指标都能普遍使用的大气环境质量综合评价模式,利用模拟退火算法对幂函数加和型指数评价中参数进行优化,并用优化后的综合评价指数公式对大气环境进行综合评价。该公式应用于大量实例的分析评价结果与其他多种评价方法评价结果比较表明:该公式不受污染物种类和数目多少的限制,计算简便,具有可行性、实用性和通用性。     

对地观测技术在重大自然灾害监测与评估中的应用实例分析

对地观测技术可提供大范围、多时相、高分辨率的海量数据,用于自然灾害的监测与评估有其极大的优越性。本文叙述了对地观测技术在洪涝灾害、沙尘天气、森林火灾的监测与评估和地震形变场研究中的应用,并指出对地观测技术是建立数字减灾系统不可或缺的强大信息源。