Prediction of response of zooplankton biomass to climatic and oceanic changes

This paper examines the long-term variation in zooplankton biomass in response to climatic and oceanic changes, using a neural network as a nonlinear multivariate analysis method. Zooplankton data collected from 1951 to 1990 off the shore of northeastern Japan were analyzed. We considered patterns of the Kuroshio and the Oyashio, sea surface temperature, and meteorological parameters as environmental factors that affect zooplankton biomass. Back propagation neural networks were trained to generate mapping functions between environmental variables and zooplankton biomass. The performance of the network models was tested by varying the numbers of input and hidden units. Changes in zooplankton biomass could be predicted from environmental conditions. The neural network yielded predictions with smaller errors than those of predictions determined by linear multiple regression. The sensitivity analysis of networks was used to extract predictive knowledge. The air pressure, sea surface temperature, and some indices of atmospheric circulation were the primary factors for predictions. The patterns of the Kuroshio and the Oyashio demonstrated different effects among sea areas.     

USING ARTIFICIAL NEURAL NETWORKS TO ESTIMATE MISSING RAINFALL DATA1

ABSTRACT: Missing rainfall data from a time series or a spatial field of observations can present a serious obstacle to data analysis, modeling studies and operational forecasting in hydrology. Numerous schemes for replacing missing data have been proposed, ranging from simple weighted averages of data points that are nearby in time and space to complex statistically-based interpolation methods and function fitting schemes. This paper presents a technique for replacing missing spatial data using a backpropagation neural network applied to concurrent data from nearby gauges. Tests performed on a sample of gauges in the Middle Atlantic region of the United States show that this technique produces results that compare favorably to simple techniques such as arithmetic and distance-weighted averages of the values from nearby gauges, and also to linear optimization methods such as regression.     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.     

基于人工神经网络的巢湖富营养化分时分区评价

应用人工神经网络方法,以温度、高锰酸盐指数、总磷、总氮作为评价参数,对巢湖湖区12个点位的营养状态进行了分时段评价,一共评价了2000年至2003年四个年度的各月份的营养状态。因为湖泊的营养状态水平是变化的,不同区域、不同时段营养状态水平是不同的,因此采用分区分时评价更能全面客观地反映巢湖的营养状态水平及其变化特征。     

人工神经网络在深圳市水库富营养化评价中的应用

对富营养化评价标准进行插值获取大量的样本,建立了基于BP人工神经网络的富营养化评价模型。将模型应用于评价深圳市13座主要水库的富营养化状况,对其成因进行分析,并提出了对策与建议。研究结果表明,石岩水库与深圳水库为轻度富营养化,占评价水库总数的15.4%;西丽水库等11座水库为中营养,占评价水库总数的84.6%。人工神经网络用于建立湖库富营养评价模型是适合的。     

Identifying important species that amplify or mitigate the interactive effects of human impacts on marine food webs

Some species may have a larger role than others in the transfer of complex effects of multiple human stressors, such as changes in biomass, through marine food webs. We devised a novel approach to identify such species. We constructed annual interaction-effect networks (IENs) of the simulated changes in biomass between species of the southeastern Australian marine system. Each annual IEN was composed of the species linked by either an additive (sum of the individual stressor response), synergistic (lower biomass compared with additive effects), or antagonistic (greater biomass compared with additive effects) response to the interaction effect of ocean warming, ocean acidification, and fisheries. Structurally, over the simulation period, the number of species and links in the synergistic IENs increased and the network structure became more stable. The stability of the antagonistic IENs decreased and became more vulnerable to the loss of species. In contrast, there was no change in the structural attributes of species linked by an additive response. Using indices common in food-web and network theory, we identified the species in each IEN for which a change in biomass from stressor effects would disproportionately affect the biomass of other species via direct and indirect local, intermediate, and global predator–prey feeding interactions. Knowing the species that transfer the most synergistic or antagonistic responses in a food-web may inform conservation under increasing multiple-stressor impacts.     

基于人工神经网络的街道峡谷NO_x浓度的数值模型研究

通过对反向传播人工神经网络的算法和网络结构的研究,发现拟牛顿算法训练速度较快,能够较好地接近误差目标值,同时建立了包括输入层、隐含层、输出层的人工神经网络三层拓扑结构。通过对街道峡谷人工神经网络的训练,模拟计算了街道峡谷NOx浓度分布值。结果显示,训练误差和测试误差比为1.11,训练样本的模拟值与实测值的相关系数为0.93,测试样本的模拟值与实测值的相关系数为0.87,模拟值与实测值的相关系数均高于显著水平为α=0.05与α=0.01所对应检验性表的相关系数临界值。该模型能够用于街道峡谷污染物浓度的模拟计算,具有较好的泛化能力。     

基于人工神经网络的街道峡谷NOx浓度的数值模型研究

通过对反向传播人工神经网络的算法和网络结构的研究,发现拟牛顿算法训练速度较快,能够较好地接近误差目标值,同时建立了包括输入层、隐含层、输出层的人工神经网络三层拓扑结构。通过对街道峡谷人工神经网络的训练,模拟计算了街道峡谷NOx浓度分布值。结果显示,训练误差和测试误差比为1．11,训练样本的模拟值与实测值的相关系数为0．93,测试样本的模拟值与实测值的相关系数为0．87,模拟值与实测值的相关系数均高于显著水平为α=0．05与α=0．01所对应检验性表的相关系数临界值。该模型能够用于街道峡谷污染物浓度的模拟计算,具有较好的泛化能力。     

A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

Reefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250 m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250 m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints.     

茄科雷尔氏菌蛋白质相互作用网络预测及分析

细菌性青枯病是由茄科雷尔氏菌(Ralstonia solanacearum)引起的一种世界范围的细菌性土传病害.该细菌基因组序列的完全测序,使得从蛋白质组角度来分析其蛋白质相互作用网络成为可能.本文通过朴素贝叶斯模型整合系统发生谱法、基因邻近法、基因融合法、操纵子法、同源映射法、微阵列法、域相互作用法等7种方法,并根据约豋指数确定的阙值,预测了可信的茄科雷尔氏菌的蛋白质相互作用网络.对网络中的分泌子网络和信号转导进行了分析,提出可能的药物作用靶点(cyaB、pilD、Fli、Rsp1526、VsrA、VsrB、PilH).对于未注释的蛋白依据蛋白相互作用推测了部分蛋白质的功能.本文也提供了完全免费的在线数据库支持,提供了方便的茄科雷尔氏菌的蛋白相互作用的查询及相互作用数据和推测的蛋白质功能数据的查询和下载(http://www.scbmp.org.cn/rsoppi.php).