BP神经网络在沙棘生态功能评价中的应用

BP人工神经网络技术在环境评价领域中已经得到越来越广泛的运用,将该法引入到陕蒙砒砂岩区沙棘生态功能综合评价的研究中,以沙棘生态功能评价指标标准值作为样本输入,综合评价级别作为网络输出,建立了一个含有4个输入神经元节点、6个隐含神经元节点和1个输出神经元节点的BP人工神经网络等级模型。将目标年(2008年)各评价指标实际数据作为输入,得到输出值是0.44,大于Ⅱ级标准,研究结果表明:砒砂岩区种植十年沙棘后,其生态效益很好,对砒砂岩地区的生态环境改善作用显著。BP神经网络的评价结果与较成熟的AHP-模糊综合评价结果一致,证明将BP人工神经网络模型用于沙棘生态功能评价是可行的,且评价结论客观。     

旱区生态环境质量的综合定量评价模型

生态环境质量是社会经济可持续发展的基础。因此 ,建立旱区生态环境质量的定量评价是区域可持续发展的主要依据。现有的评价方法大多是通过根据评价区评价指标量化值与评价等级标准来建立评价模型 ,评价区不同 ,评价模型也不相同 ,计算工作量较大。根据给定的生态环境质量评价等级标准 ,采用随机技术模拟生成足够数量的评价指标序列 ,应用人工神经网络模型 (artificial neural network,ANN) ,以评价指标生成序列与其所属的评价等级值进行网络训练。网络训练后 ,以评价区的评价指标为网络的输入 ,通过计算 ,可获得相应的生态环境质量评价等级值。并以甘肃省石羊河流域生态环境脆弱的民勤县为研究对象 ,应用 1975～ 2 0 0 0年资料进行了实例研究。结果表明 ,民勤县 1975～ 2 0 0 0年生态环境质量效应评价值分别为 2 .95 0 1,4 .0 0 90 ,4 .1342 ,4 .16 37,4 .9736 ,5 .0 12 8,说明该地区的生态环境质量是持续下降的 ,与以往采用的模糊综合评价等级值 3,4 ,4 ,4 ,5 ,5一致。文中 ANN模型建立后 ,对于不同评价区 ,只要给定相应的评价指标值 ,通过 ANN模型计算 ,可直接得出生态环境质量评价等级值。因此 ,模型具有实用、可操作性强的特点 ,大大减少了评价区的计算工作量 ,可以用于生态环境质量效应评价     

基于BP人工神经网络方法的广西稻飞虱发生等级预测

利用广西45个农业病虫测报站1988—2012年稻飞虱发生等级及1987—2012年气象要素、大气环流特征量等资料,采用模糊聚类分析、BP人工神经网络等方法,将广西早稻稻飞虱发生等级分为桂东、桂西南和桂西北3个区域,分别对各区域稻飞虱发生等级进行预测。结果表明:各区域稻飞虱发生等级与气象要素及大气环流密切相关,冬春季气温高、雨日多、湿度大、光照少等因素均利于稻飞虱发生,副热带高压、印缅槽和西南气流等均对稻飞虱发生等级有影响;各区域稻飞虱发生等级序列从冬春季气象要素、大气环流特征量中选择初选预测因子,对初选预测因子作EOF展开构造综合预测因子,分区建立预测模型并进行交叉检验表明,3个区域的人工神经网络模型平均拟合绝对误差比逐步回归模型分别小0.07、0.1和0.02,2011、2012年独立样本预测试验表明,人工神经网络模型和逐步回归模型的实际预测绝对误差为0.42和0.5,可见稻飞虱发生等级的BP人工神经网络预测模型比传统逐步回归模型有更好的拟合和预测效果,为稻飞虱与气象要素之间的非线性关系研究开拓新的思路。     

基于人工神经网络的生态环境质量遥感评价

利用ETM遥感数据提取反映生态环境的植被、土壤亮度、湿度,MODIS地表温度产品提取的热度指数、气象指数及其它地学辅助信息作为神经网络的输入,野外调查标准兴趣区的遥感本底值评分值作为网络输出,建立一个3层结构的BP神经网络生态环境遥感本底值预测模型.利用MATLAB软件对网络进行训练和研究区生态环境遥感本底值的预测输出,并将预测结果按照生态环境遥感本底值分级评分标准进行等级划分.结果表明,总体分类精度达87.8%.利用神经网络方法对生态环境遥感本底值进行预测是可行的.采用先预测再分级的方法不仅能很好地评价区域生态环境质量,而且能够和区域生态环境类型紧密的结合起来.     

基于遗传神经网络的成都市人均生态足迹预测

利用遗传神经网络模型分析成都市各年人均生态足迹值与其主要影响因素之间的内在关系,并以此来预测未来生态足迹发展趋势.首先计算出成都市1985～2005年共21年的人均生态足迹值,结果表明,20余年来成都市人均生态足迹值缓慢上升,从1.344hm2升至1.789hm2.选取6个影响因素指标,建立了6输入、1输出、1个隐含层的三层BP人工神经网络,利用遗传算法的全局优化性能对BP网络进行初始权值优化,以克服BP网络易陷入局部极小点的缺陷.运用遗传神经网络模型对训练样本(1985～2002年人均生态足迹值)进行拟合的精度达99.70%;对检验样本(2003～2005年人均生态足迹值)进行仿真检验,仿真精度为99.10%,高于普通BP神经网络模型(97.89%),体现出模型较强的应用价值.利用1985～2005年各影响因素的预测值作为网络输入,使用优化后的网络来预测成都市未来几年生态足迹值,最终预测得到2008～2010年成都市人均生态足迹的预测值分别为1.939、1.990、2.049 hm2人-1,人均生态赤字分别为1.629、1.688、1.749 hm2人-1.为减轻城市发展消耗所导致的生态环境压力,应有效控制成都市人口过快增长、引导消费观念和结构更新.     

基于BP神经网络的流域生态恢复度计算——以福建长汀朱溪小流域为例

以福建省长汀县朱溪小流域为研究对象,通过野外调查、室内分析以及遥感影像提取相结合的方法获取数据。利用Matlab7.0软件建立BP神经网络生态恢复模型,定量评价退化生态系统的恢复程度。选择土壤理化性质(有机质、全N、全P、全K、容重和p H)、植被结构(植被盖度)、物种多样性指数(Shannon-Wiener指数)和热环境(地表温度)等4个方面的9个指标建立退化生态系统评价体系,并作为生态恢复模型的输入层数据,生态恢复度作为输出层数据。使用Matlab7.0进行数据预处理、样本训练、样本检验并建立生态恢复模型。利用建立的生态恢复模型对整个朱溪小流域生态恢复度进行定量评价。结果表明,生态恢复模型预测结果与流域生态恢复的实际情况基本吻合,利用BP神经网络模型定量评价退化生态系统的恢复程度具有可行性。朱溪小流域内生态恢复程度极低的区域面积仅占0.94%,95.48%区域为中等恢复程度,说明生态保护措施已初见成效;生态恢复程度高的区域面积仅占3.62%,意味着未来仍需加强治理和保护工作。     

投影寻踪模型在区域生态环境质量评价中的应用

大量相关因子的简化和降维处理是生态环境质量评价的关键问题之一。而投影寻踪模型可将多个指标投影到线性空间,实现指标的降维处理。本文以巢湖流域生态环境质量综合评价进行了实证分析,巢湖流域生态环境质量为3级,其中合肥市和六安市所属区域生态环境质量为3级,巢湖市为4级。研究表明,应用投影寻踪模型进行区域生态环境质量评价人为干扰少,操作简便,便于在生产实践中应用,为区域生态环境质量评价提供了一条新途径。     

旱区流域水土环境质量的综合定量评价模型

现有流域水土环境质量的评价方法大多根据评价区评价指标量化值与评价等级标准建立评价模型.评价区不同,评价模型也不相同,计算工作量较大.本文根据给定的水土环境质量评价等级标准,采用随机技术模拟生成足够数量的评价指标序列,应用人工神经网络模型,以评价指标生成序列和其所属的评价等级值建立一种通用的评价模型,其特点是不需要构造评价指标集和评价等级值间的函数关系和计算权重值,减少了建立模型的工作量.以西北地区水资源开发利用程度最高的石羊河流域进行实例研究,表明该模型可操作性强,可用于流域水土环境质量评价.     

南瓮河自然保护区生态环境质量遥感评价

运用遥感技术,科学、快速地评价区域生态环境质量,能够为区域生态环境的保护、治理和规划等提供科学依据.本研究以1990、2000和2015年的Landsat TM/OLI/TIRS为数据源,使用主成分分析方法确定湿度分量、归一化植被指数、地表温度和干度指数4个指标的权重,运用遥感生态指数(RSEI)评价模型,对南瓮河自然保护区1990—2015年的生态环境进行评价.结果表明: 遥感生态指数能够较好地反映研究区生态环境质量状况及其时空分异;1990—2015年,保护区 RSEI均值由0.55上升至0.83,生态环境质量优良以上区域面积所占百分比逐年增加,这与该等级中森林所占比例增加有关;保护区生态环境质量变好的区域所占比例高达91.4%,这与保护区的建立及相关工程与非工程防护措施的实施密切相关;核心区、缓冲区北部生态环境质量下降的主要原因是火灾的发生导致森林植被遭到破坏,而道路附近和试验区的东南部生态环境质量的下降与人类活动的干扰密不可分.     

内蒙古杭锦旗生态环境时空变化的遥感监测与评价

随着生态环境问题的日益突出和全球化,人们越来越注重环境保护,而且遥感技术在生态环境监测和评价中发挥着重要作用。本研究基于1992、2000、2008和2017年的Landsat影像数据,构建遥感生态指数(RSEI),对内蒙古杭锦旗的生态环境质量进行监测和评价,以期为该区生态环境保护提供理论依据。结果表明: 1992—2017年,杭锦旗的生态环境质量总体较差,其RSEI等级以差和较差为主;RSEI均值从0.31(1992年)上升到0.37(2008年),再下降至0.30(2017年),期间生态等级变化均以变化1个等级为主。在空间分布上,杭锦旗生态环境质量较差的区域主要分布在中西部的荒漠平原区,生态质量较好的区域主要分布在黄河沿岸和东南部,生态质量等级变化波动较大的区域主要分布在沙漠边缘、黄河沿岸和东部的丘陵沟壑区。研究期间,杭锦旗各生态等级的重心转移幅度较大,时空波动较明显。总之,杭锦旗生态环境脆弱、不稳定,生态建设对生态环境的改善有一定促进作用,同时也要合理地配置资源和利用土地。     

基于神经网络简单集成的湖库富营养化综合评价模型

根据中国水利部推荐的地表水富营养化控制标准,以叶绿素a、总磷、总氮、化学需氧量和透明度为评价指标,采用线性插值方法生成均匀分布的训练样本,建立了用于湖泊、水库富营养化综合评价的神经网络简单集成模型,其个体网络采用反向传播网络。通过递增法分别确定个体网络隐含层节点数为3,集成规模为40。所有个体网络均采用弹性反传训练算法和带动量的梯度下降学习算法。将该模型应用于巢湖富营养化综合评价,结果表明该模型有效消除了单个反向传播神经网络对初始网络权重的敏感性,泛化能力得到显著的提高。该模型的评价结果与综合营养状态指数法差异极显著,而与插值评分法差异不显著;但相关性较高,相关系数分别为0．9406和0．8891。通过对比分析,表明该模型较好地归纳了评价标准中的潜在评价规则,评价结果客观、可靠。     

一种自优化RBF神经网络的叶绿素a浓度时序预测模型

藻类水华发生过程具有复杂性、非线性、时变性等特点,其准确预测一直是一个国际性难题.以天津市于桥水库为研究对象,根据2000年1月至2003年12月常规监测的水生生态数据(采样周期为10 d),提出了一种结合时序方法的可自优化RBF神经网络智能预测模型,对判断藻类水华的重要指标叶绿素a浓度进行预测.研究了训练样本量及RBF神经网络扩展速度SPREAD值的可自优化性能,以及该模型用于于桥水库叶绿素a浓度的短期变化趋势预测的可行性.结果表明,预测性能指标随SPREAD值及样本量不同发生变化,该预测模型能自动寻到最优SPREAD值,并发现至少需要约两年的训练样本量才能达到较好预测效果.当样本量为105,SPREAD值为10时,预测效果最好,精度较高,预测值与实测值的相关系数R达到0.982.该方法对水库的藻类水华预警有一定的参考价值.     

基于BP神经网络与ETM+遥感数据的盐城滨海自然湿地覆被分类

高效而精确的湿地遥感分类是大范围湿地资源动态监测与管理的必要保障。本研究使用ETM 遥感数据,借助Matlab神经网络工具箱,构建了基于BP神经网络的滨海湿地覆被分类模型,并将其应用于江苏盐城沿海湿地珍禽国家级自然保护区的核心区的自然湿地覆被分类研究中。本研究选择3、4、7、8波段作为输入层变量,单隐藏层设为10个节点,输出层变量对应待划分的8种覆被类型,构建三层式BP神经网络滨海湿地覆被分类模型。结果显示,BP分类总精度为85.91%,Kappa系数为0.8328,与最小距离法和极大似然法的分类总精度相比,分别提高了7.99%和6.08%,Kappa系数也相比提高。研究结果表明,BP神经网络分类法是一种较为有效的湿地遥感影像分类技术,能够提高分类精度。     

Analysis and implementation of a neural extended Kalman filter for target tracking

Having a better motion model in the state estimator is one way to improve target tracking performance. Since the motion model of the target is not known a priori, either robust modeling techniques or adaptive modeling techniques are required. The neural extended Kalman filter is a technique that learns unmodeled dynamics while performing state estimation in the feedback loop of a control system. This coupled system performs the standard estimation of the states of the plant while estimating a function to approximate the difference between the given state-coupling function model and the behavior of the true plant dynamics. At each sample step, this new model is added to the existing model to improve the state estimate. The neural extended Kalman filter has also been investigated as a target tracking estimation routine. Implementation issues for this adaptive modeling technique, including neural network training parameters, were investigated and an analysis was made of the quality of performance that the technique can have for tracking maneuvering targets.     

‘Tailception’: using neural networks for assessing tail lesions on pictures of pig carcasses

Tail lesions caused by tail biting are a widespread welfare issue in pig husbandry. Determining their prevalence currently involves labour intensive, subjective scoring methods. Increased societal interest in tail lesions requires fast, reliable and cheap systems for assessing tail status. In the present study, we aimed to test the reliability of neural networks for assessing tail pictures from carcasses against trained human observers. Three trained observers scored tail lesions from automatically recorded pictures of 13 124 pigs. Nearly all pigs had been tail docked. Tail lesions were classified using a 4-point score (0=no lesion, to 3=severe lesion). In addition, total tail loss was recorded. Agreement between observers was tested prior and during the assessment in a total of seven inter-observer tests with 80 pictures each. We calculated agreement between observer pairs as exact agreement (%) and prevalence-adjusted bias-adjusted κ (PABAK; value 1=optimal agreement). Out of the 13 124 scored pictures, we used 80% for training and 20% for validating our neural networks. As the position of the tail in the pictures varied (high, low, left, right), we first trained a part detection network to find the tail in the picture and select a rectangular part of the picture which includes the tail. We then trained a classification network to categorise tail lesion severity using pictures scored by human observers whereby the classification network only analysed the selected picture parts. Median exact agreement between the three observers was 80% for tail lesions and 94% for tail loss. Median PABAK for tail lesions and loss were 0.75 and 0.87, respectively. The agreement between classification by the neural network and human observers was 74% for tail lesions and 95% for tail loss. In other words, the agreement between the networks and human observers were very similar to the agreement between human observers. The main reason for disagreement between observers and thereby higher variation in network training material were picture quality issues. Therefore, we expect even better results for neural network application to tail lesions if training is based on high quality pictures. Very reliable and repeatable tail lesion assessment from pictures would allow automated tail classification of all pigs slaughtered, which is something that some animal welfare labels would like to do.     

Neural network modeling in optimisation of continuous fermentation processes

The capability of self-recurrent neural networks in dynamic modeling of continuous fermentation is investigated in this simulation study. In the past, feedforward neural networks have been successfully used as one-step-ahead predictors. However, in steady-state optimisation of continuous fermentations the neural network model has to be iterated to predict many time steps ahead into the future in order to get steady-state values of the variables involved in objective cost function, and this iteration may result in increasing errors. Therefore, as an alternative to classical feedforward neural network trained by using backpropagation method, self-recurrent multilayer neural net trained by backpropagation through time method was chosen in order to improve accuracy of long-term predictions. Prediction capabilities of the resulting neural network model is tested by implementing this into the Integrated System Optimisation and Parameter Estimation (ISOPE) optimisation algorithm. Maximisation of cellular productivity of the baker's yeast continuous fermentation was used as the goal of the proposed optimising control problem. The training and prediction results of proposed neural network and performances of resulting optimisation structure are demonstrated.     

A neural network approach for the prediction of in vitro culture parameters for maximum biomass yields in hairy root cultures

The present study deals with ANN based prediction of culture parameters in terms of inoculum density, pH and volume of growth medium per culture vessel and sucrose content of the growth medium for Glycyrrhiza hairy root cultures. This kind of study could be a model system in exploitation of hairy root cultures for commercial production of pharmaceutical compounds using large bioreactors. The study is aimed to evaluate the efficiency of regression neural network and back propagation neural network for the prediction of optimal culture conditions for maximum hairy root biomass yield. The training data for regression and back propagation networks were primed on the basis of function approximation, where final biomass fresh weight (f_wt) was considered as a function of culture parameters. On this basis the variables in culture conditions were described in the form of equations which are for inoculum density: y=0.02x+0.04, for pH of growth medium: y=x+2.8, for sucrose content in medium: y=9.9464x+(−9.7143) and for culture medium per culture vessel: y=10x. The fresh weight values obtained from training data were considered as target values and further compared with predicted fresh weight values. The empirical data were used as testing data and further compared with values predicted from trained networks. Standard MATLAB inbuilt generalized regression network with radial basis function radbas as transfer function in layer one and purelin in layer two and back propagation having purelin as transfer function in output layer and logsig in hidden layer were used. Although in comparative assessment both the networks were found efficient for prediction of optimal culture conditions for high biomass production, more accuracy in results was seen with regression network.