Recognizing spatial distribution patterns of grassland insects: neural network approaches

The main objective of this study was to fit and recognize spatial distribution patterns of grassland insects using various neural networks, and to analyze the feasibility of neural networks for detecting spatial distribution patterns of grassland insects. BP neural network, Learning vector quantization (LVQ) neural network, linear neural network and Fisher’s linear discriminant analysis were used to fit and recognize spatial distribution patterns at different ecological scales. Various comparisons and analysis were conducted. The results showed that BP, LVQ and linear neural networks were better algorithms for recognizing spatial distribution patterns of grassland insects. BP neural network was the best algorithm to fit spatial distribution patterns. BP network may be used to recognize the spatial details of distribution patterns, and the recognition performance of BP network became better as the increase of the number of hidden layers and neurons. Performance of linear neural network for pattern recognition was similar to linear discrimination method. Linear neural network would yield better performance in finding the general trends of distribution patterns. Recognition performance of LVQ network was just between BP network and linear network. It was found that recognition performance of neural networks depended upon not only the ecological scale but also the criterion for classification. Under the uniform criterion, recognition efficiency of linear methods tended to be weak as ecological scale became to be coarser. A joint use of neural networks was suggested in order to achieve both overall and detailed understanding on spatial distribution patterns.     

An application of Spartan spatial random fields in environmental mapping: focus on automatic mapping capabilities

This paper investigates the potential of Spartan spatial random fields (SSRFs) in real-time mapping applications. The data set that we study focuses on the distribution of daily gamma dose rates over part of Germany. Our goal is to determine a Spartan spatial model from the data, and then use it to generate “predictive” maps of the radioactivity. In the SSRF framework, the spatial dependence is determined from sample functions that focus on short-range correlations. A recently formulated SSRF predictor is used to derive isolevel contour maps of the dose rates. The SSRF predictor is explicit. Moreover, the adjustments that it requires by the user are reduced compared to classical geostatistical methods. These features present clear advantages for an automatic mapping system. The performance of the SSRF predictor is evaluated by means of various cross-validation measures. The values of the performance measures are similar to those obtained by classical geostatistical methods. Application of the SSRF method to data that simulate a radioactivity release scenario is also discussed. Hot spots are detected and removed using a heuristic method. The extreme values that appear in the path of the simulated plume are not captured by the currently used Spartan spatial model. Modeling of the processes leading to extreme values can enhance the predictive capabilities of the spatial model, by incorporating physical information.     

用空间光滑方法评估弱地震活动区的地震活动性参数

本文以长江中游地震带为研究区域,该区地震活动比较弱,发震构造不清晰,但人口、经济分布集中,中小地震往往会造成强烈的社会影响。自有记载以来,共记录到122次Ms≥4．7级的地震,最大的地震为1631年常德63/4级地震,在这样的区域进行地震危险性分析,依据构造特征和地震活动进行潜源划分的方法会带来较大的不确定性,为了更合理地评价其危险性,本文试图先在研究区内划分活动水平相当的等b值区域,然后利用空间光滑法计算各区的地震年平均发生率分布。结果显示,各分区内年平均发生率存在较大差异,年发生率分布图与现代和历史地震震中分布格局基本一致,为潜源区划分提供了一种量化的空间参考数据。     

Spatial assessment of fishing effort around European marine reserves: implications for successful fisheries management

We examined the spatial dynamic of artisanal fishing fleets around five European marine protected areas (MPAs) to derive general implications for the evaluation of MPAs as fisheries management tools. The coastal MPAs studied were located off France, Malta and Spain and presented a variety of spatial designs and processes of establishment. We developed a standardized methodology to define factors influencing effort allocation and to produce fishing effort maps by merging GIS with geostatistical modelling techniques. Results revealed that in most cases the factors “distance to the no-take”, “water depth”, and “distance to the port” had a significant influence on effort allocation by the fishing fleets. Overall, we found local concentration of fishing effort around the MPA borders. Thus, neglecting the pattern of fishing effort distribution in evaluating MPA benefits, such as spillover of biomass, could hamper sound interpretation of MPAs as fisheries management tools.     

强震作用下框架结构的优化设计

针对第三水准"大震不倒"的抗震设防目标,提出一种在强震作用下框架结构的优化设计方法.依据"用相同的投资获最好的设计"的设计理念,以在强烈地震波的作用下框架结构最大的层间相对位移最小化作为优化目标,建立了同时满足体积约束的优化数学模型.采用动力有限元分析模型和高效的显式动力分析方法进行结构分析,获得最大的层问相对位移;采用改进的复形法求解优化数学模型;在显式动力分析软件ANSYS/LS-DYNA的基础上进行二次开发,实现了一个三维框架结构的抗震优化设计.数值结果表明该设计方法不一定获得抗震结构的最优设计,但能获得一系列抗震性能良好的设计,具有现实的工程意义.     

沈大高速公路两侧土壤重金属污染分布特征研究

对沈大高速公路沈阳、辽阳、鞍山、营口和大连段两侧土壤中铅、镉、锌、铬、镍和铜的含量进行测定。结果表明:在距路肩250 m范围内,各段土壤中铅、镉均有不同程度的累积,锌、铬、镍、铜污染不显著。土壤中铅、镉最大污染指数为3.34和2.42,污染较高值出现在距路肩50 m内,尤以20—40 m为高,除铅外其余重金属含量在距路肩150 m以外趋于背景值,但土壤中铅污染为250 m以外。     

辽宁省太阳能资源分布及区划初探

根据1971—2006年沈阳、大连和朝阳太阳辐射观测站历年逐月太阳总辐射和日照百分率实测资料,应用统计方法计算辽宁无辐射观测地区的太阳总辐射量,进而了解辽宁省太阳能资源分布情况。结果表明:辽西和沿海地区及长山群岛太阳能资源较好,辽北次之,东部山区较差;春夏季较好,秋冬季较差,5月最好,12月最差。太阳能资源历年变化相对平稳,各年代呈小幅度波动,其中20世纪80年代偏小、90年代偏大,近几年回落。根据各地太阳总辐射年总量,将太阳能资源定为4级评估指标,并将辽宁省太阳能资源划分为丰富、较丰富、一般和贫乏4个区域。     

Sub-kilometer length scales in coastal waters

Patchiness or spatial variability is ubiquitous in marine systems. With increasing anthropogenic impacts to coastal resources and coastal systems being disproportionately large contributors to ocean productivity, identifying the spatial scales of this patchiness, particularly in coastal waters, is of critical importance to understand coastal ecosystem dynamics. The current work focuses on fine scale structure in three coastal regions. More specifically, we utilize variogram analyses to identify sub-kilometer scales of variability in biological and physical parameters measured by an autonomous underwater vehicle (AUV) in the Mid-Atlantic Bight, Monterey Bay, and in San Luis Obispo Bay between 2001 and 2004. Critical scales of variability in density, turbidity, fluorescence, and bioluminescence are examined as a function of depth and distance offshore. Furthermore, the effects of undersampling are assessed using predictive error analysis. Results indicate the presence of scales of variability ranging from 10s to 100s of meters and provide valuable insight for sampling design and resource allocation for future studies.