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11.
基于BP神经网络的三峡库区土壤侵蚀强度模拟 总被引:1,自引:0,他引:1
降雨侵蚀力变化是一复杂过程,其变化存在一定的随机波动性,土壤侵蚀是三峡库区生态环境脆弱最主要的影响因素之一,查明库区土壤侵蚀强度的演化过程及未来趋势是库区生态文明建设过程中急需解决的关键科学问题。论文基于三峡库区1990年侵蚀降雨特征,利用BP神经网络对2010年75个站点降雨侵蚀力进行模拟、验证,预测2030年75个站点降雨侵蚀力。选取2030年预测结果中位于库区周围的27个站点,结合2030年库区自然增长、生态保护情景下土地利用模拟数据,使用RUSLE计算2030年土壤侵蚀强度。结果表明:1)2010年库区降雨侵蚀力模拟相对误差为15%,测试样本数据相对误差为14.67%,预测相对误差为19.65%,NE系数为0.85,说明BP神经网络对库区降雨侵蚀力具有良好模拟效果;2)2010年库区土壤侵蚀强度的Kappa指数为0.75,计算结果能满足模拟与预测需求;3)在土地利用不变情况下,2030年库区轻度、中度侵蚀面积均有所增加,微度及强烈以上侵蚀面积均呈减少趋势,且侵蚀强度转变中的58%来源于相邻侵蚀强度,跨侵蚀等级区的较少;4)在降雨侵蚀力不变情况下,自然增长、生态保护情景下未来土地利用变化所导致的土壤侵蚀均呈下降趋势,后者下降的趋势更为明显;5)在降雨侵蚀力及土地利用均变化的情况下,自然增长、生态保护情景下土壤侵蚀均呈下降趋势。 相似文献
12.
目的为避免EIS,EN技术可能出现的问题,建立一个准确、高效的评价模型,以探究现役军用有机涂层防护性能。方法利用电化学阻抗谱(EIS)、电化学噪声(EN)技术分析了两种军车有机涂层在循环暴露试验中的腐蚀行为,提取低频阻抗模值|Z|_(0.1 Hz)与涂层噪声电阻R_n两种电化学评价参数作为自组织神经网络(SOM)的输入训练样本,同时结合支持向量机(SVM)方法建立涂层防护性能组合分类器。结果将涂层失效过程自适应地分为涂层防护性能良好、防护性能下降、基本失效三个阶段。结论所建立的SOM-SVM组合分类器对于辅助分析涂层防护性能具有可行性。 相似文献
13.
David L. Peterson 《Environmental monitoring and assessment》2000,64(1):81-91
A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies. 相似文献
14.
Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization 总被引:6,自引:0,他引:6
Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems. 相似文献
15.
Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong 总被引:4,自引:0,他引:4
As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NOx and NO2 are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NOx and NO2 concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods. 相似文献
16.
17.
基于B-P神经网络的环境质量评价方法 总被引:3,自引:0,他引:3
提出可将环境质量评价的无论是定量指标还是定性参数转化成"二进制"的"1"或"0",进而将这种二进制数引入B-P网络.通过实例探讨,这种新的B-P网络既适用于定量指标的水质参数又适用于定性指标的水质参数. 相似文献
18.
Combating desertification in natural rangelands has recently become a priority in large parts of southern Africa. Rangeland
managers, farmers, scientists, conservationists and land users have been applying a variety of restoration technologies to
address this problem. Bush encroachment, as part of the desertification process, involves the natural replacement of the herbaceous
plant cover by undesirable problem woody species. The active and passive restoration technologies that are applied, are mainly
based on indigenous knowledge and include the chemical, mechanical or manual reclamation of unproductive rangelands, as well
as the combating of woody and alien species encroachment. Indigenous practices and knowledge play a major role in the effectiveness
and success rate of these technologies. This project faces the challenge of bringing together both local and scientific knowledge
in a single user-friendly, computerised Decision Support System (DSS) which is directly accessible by land users to support
them in the process of decision making, concerning the combating of desertification. Case studies from central and northern
Namibia were used to combine qualitative and quantitative data to develop this Decision Support System. The DSS currently
consists of two databases and an expert system, which evaluates the results of land users’ management practices, and provides
easily accessible information and advice for participants in the system, based on the incorporated data. The DSS is also linked
to national and international web sites and databases to offer a wider range of information on technologies concerning agricultural
and conservation practices. 相似文献
19.
20.
人工神经网络用于铅的化学形态模拟计算 总被引:1,自引:0,他引:1
用前馈线性网络法求解水体系中Pb(2+)与OH-之间的反应常数,不同训练算法对求解结果的精度、收敛速度及权值均有影响.结果表明,批处理算法的精度最好,权值不出现负值,但运算时间最长;在线算法的精度虽不如批处理算法,而比数据变换-在线算法好,权值有时会出现负值.运算时间较长;数据变换-在线算法的优点是运算时间短,但相对误差较大,权值出现负值的机会多。采用反馈网络模拟计算铅的各种化学形态的浓度.用物料核算的方法对反馈网络模型进行检验表明,此种模型用于平衡计算是可行的,详细分析了理论模拟和实验曲线的差异的原因,温度的影响最小,在4<pH<9时,CO有重要的影响.在国代检验时,n值取整所引入的误差的影响亦不可忽视。从本文的结果可以看到,采用前馈网络和反馈网络相结合的方法考察水体中的化学形态是可行的.从而为解决这一类问题提供了一种可能的途径. 相似文献