基于多源数据融合的煤矿工作面瓦斯浓度预测*

为了解决瓦斯浓度预测使用的单一数据在预测中影响还不够深入的问题,提出基于LSTM神经网络的多源数据融合瓦斯浓度预测模型。模型将上隅角瓦斯浓度、采煤机速度、工作面吨煤瓦斯涌出量等不同数据融合作为输入层参数,使用Adam优化算法更新LSTM网络层参数,利用Attention机制突出关键影响瓦斯浓度的因素,开展多源数据融合的瓦斯浓度预测,结合某矿1008工作面的实际数据,分析不同数据在瓦斯浓度预测中的作用。研究结果表明:单变量下的Attention-aLSTM预测效果相比LSTM提升14.2%;多源数据融合下的Attention-aLSTM相比自身提升了5%。     

基于植被指数和神经网络的热带人工林地上蓄积量遥感估测

热带森林作为陆地生态系统的组成成分之一,研究其蓄积量估测对我们了解其在全球碳循环中的地位和作用有很重要的意义.但遥感估测森林生态参数的精度如何,还是个不确定的问题.利用LANDSAT-TM数据,基于森林清查数据和遥感技术,以尾叶桉和加勒比松为例,对中国南方地区人工林蓄积量估测进行了尝试研究.首先,通过测量样方胸径、树高,建立森林蓄积量估算模型.其次,通过对比分析不同植被指数与森林蓄积量之间的关系,选择合适植被指数组合,建立多元回归和神经网络模型.结果表明:单波段TM数据和大多数植被指数与蓄积量相关性并不好.神经网络比回归分析模拟效果好.而多元回归和神经网络模型大大提高预测精度.本研究方法对大面积的森林蓄积量估测具有一定的参考价值.     

The Reynolds transport theorem: Application to ecological compartment modeling and case study of ecosystem energetics

The Reynolds transport theorem (RTT) from mathematics and engineering has a rich history of success in mass transport dynamics and traditional thermodynamics. This paper introduces RTT as a complementary approach to traditional compartmental methods used in ecological modeling and network analysis. A universal system equation for a generic flow quantity is developed into a generic open-system differential expression for conservation of energy. Nonadiabatic systems are defined and incorporated into control volume (CV) and control surface (CS) perspectives of RTT where reductive assumptions in empirical data are then formally introduced, reviewed, and appropriately implemented. Compartment models are abstract, time-dependent systems of simultaneous differential equations describing storage and flow of conservative quantities between interconnected entities (the compartments). As such, they represent a set of flexible and somewhat informal, assumptions, definitions, algebraic manipulations, and graphical depictions subject to influence and selectively parsed expression by the modeler. In comparison, RTT compartment models are more rigorous and formal integro-differential equations and graphics initiated by the RTT universal system equation, forcing an ordered identification of simplifying assumptions, ending with clearly identified depictions of the transfer and transport of conservative substances in physical space and time. They are less abstract in the rigor of their equation development leaving less ambiguity to modeler discretion. They achieve greater consistency with other RTT compartment style models while possibly generating greater conformity with physical reality. Characteristics of the RTT approach are compared with those of a traditional compartment model of energy flow in an intertidal oyster-reef community.     

Invasive species impacts on ecosystem structure and function: A comparison of Oneida Lake, New York, USA, before and after zebra mussel invasion

Exotic species invasion is widely considered to affect ecosystem structure and function. Yet, few contemporary approaches can assess the effects of exotic species invasion at such an inclusive level. Our research presents one of the first attempts to examine the effects of an exotic species at the ecosystem level in a quantifiable manner. We used ecological network analysis (ENA) and a social network analysis (SNA) method called cohesion analysis to examine the effect of zebra mussel (Dreissena polymorpha) invasion on the Oneida Lake, New York, USA, food web. We used ENA to quantify ecosystem function through an analysis of food web carbon transfer that explicitly incorporated flow over all food web paths (direct and indirect). The cohesion analysis assessed ecosystem structure through an organization of food web members into subgroups of strongly interacting predators and prey. Our analysis detected effects of zebra mussel invasion throughout the entire Oneida Lake food web, including changes in trophic flow efficiency (i.e., carbon flow among trophic levels) and alterations of food web organization (i.e., paths of carbon flow) and ecosystem activity (i.e., total carbon flow). ENA indicated that zebra mussels altered food web function by shunting carbon from pelagic to benthic pathways, increasing dissipative flow loss, and decreasing ecosystem activity. SNA revealed the strength of zebra mussel perturbation as evidenced by a reorganization of food web subgroup structure, with a decrease in importance of pelagic pathways, a concomitant rise of benthic pathways, and a reorganization of interactions between top predator fish. Together, these analyses allowed for a holistic understanding of the effects of zebra mussel invasion on the Oneida Lake food web.     

Multiple-pattern parameter identification and uncertainty analysis approach for water quality modeling

This paper presents a multiple-pattern parameter identification and uncertainty analysis approach for robust water quality modeling using a neural network (NN) embedded genetic algorithm (GA). The modeling approach uses an adaptive NN–GA framework to inversely solve the governing equations in a water quality model for multiple parameter patterns, along with an alternating fitness method to maintain solution diversity. The procedure was demonstrated through a coupled 2D hydrodynamic and eutrophication model for Loch Raven Reservoir in Maryland. The inverse problem was formulated as a nonlinear optimization problem minimizing the degree of misfit (DOM) between model results and observed data. A set of NN models was developed to approximate the input-output response relationship of the Loch Raven Reservoir model and was incorporated into a GA framework in an adaptive fashion to search for near-optimal solutions minimizing the DOM. The numerical example showed that the adaptive NN–GA approach is capable of identifying multiple parameter patterns that reproduce the observed data equally well. The resulting parameter patterns were incorporated into the numerical model, and a multiple-pattern robust water quality modeling analysis, along with a compound margin of safety (CMOS) method, was proposed and applied to analyze the parameter pattern uncertainty.     

Predictability of marine nematode biodiversity

In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed.     

Effectiveness of Vegetation Surrogates for Parasitoid Wasps in Reserve Selection

Abstract:  Selecting suitable nature reserves is a continuing challenge in conservation, particularly for target groups that are time-consuming to survey, species rich, and extinction prone. One such group is the parasitoid Hymenoptera, which have been excluded from conservation planning. If basic characteristics of habitats or vegetation could be used as reliable surrogates of specific target taxa, this would greatly facilitate appropriate reserve selection. We identified a range of potential habitat indicators of the species richness of pimpline parasitoid communities (Hymenoptera: Ichneumonidae: Pimplinae, Diacritinae, Poemeniinae) and tested their efficiency at capturing the observed diversity in a group of small woodlands in the agricultural landscape of the Vale of York (United Kingdom). Eight of the 18 vegetation-based reserve-selection strategies were significantly better at parasitoid species inclusion than random selection of areas. The best strategy maximized richness of tree species over the entire reserve network through complementarity. This strategy omitted only 2–3 species more (out of 38 captured in the landscape as a whole) than selections derived from the parasitoid survey data. In general, strategies worked equally well at capturing species richness and rarity. Our results suggest that vegetation data as a surrogate for species richness could prove an informative tool in parasitoid conservation, but further work is needed to test how broadly applicable these indicators may be.     

基于小波神经网络的芦苇潜流人工湿地水质预测

人工湿地系统对污水的处理效果好,工艺简单,投资运行费用低,但影响其出水水质的因素很多,并且往往是非线性的,因此目前很难将这些影响因素模型化并用于水质预测. 已有的预测方法不是过于复杂就是预测精度不高. 神经网络是一种具有较强预测能力的新方法,适用于各种非线性模型的预测. 在小试研究的基础上,使用3种不同的、经过训练的小波神经网络,对芦苇潜流人工湿地沿程各采样口的水温,ρ(DO),pH,Eh和ρ(COD_Cr)等水质指标进行了预测. 结果显示,各指标的平均相对误差分别为:水温≤4.21%,pH≤1.36%,ρ(DO)≤9.77%,Eh≤6.50%,ρ(COD_Cr)≤17.76%,表明小波神经网络模型适用于人工湿地模型的预测.      

Modelling spatial distribution of hard bottom benthic communities and their functional response to environmental parameters

In this study we analyzed and modelled spatial distribution of hard bottom benthic communities in the Lagoon of Venice, and used the model to derive functional response of these communities to changing environmental conditions.     

环氧化合物分子结构与致变活性的关系

由化学物质毒性效应登录(RTECS)1998年光盘系统检索获得86种环氧化合物对鼠沙门氏菌致变活性数据.应用主成分分析法从11种分子描述符中选择出5种对致变活性有明显影响的描述符:碳原子数,苯环数,氢原子数,烷基数和氧原子数.通过样本学习集训练并优化神经网络结构,建模分类预报:对于86个样本,低和高2类活性,正确分类率达到92%.结果表明:3～4个骈接苯环的环氧化物的特殊结构具有很强的致变活性.