基于黄金分割理论的改进BP网络地下水水质评价     

文靓  黄川友  殷彤 《地下水》2010,32(6):13-15

利用VB语言编写附加动量的改进BP人工神经网络模型程序,并将其加载到Excel中,以湛江市区地下水为例研究水质状况。该模型采用黄金分割理论和试算相结合的方法对网络模型的隐含层节点数进行了优选,研究结果与其他方法相比显示：改进BP人工神经网络模型在地下水水质评价中能够很好地解决评价因子与水质等级间复杂的非线形关系,评价结果的精度有较大地提高。  相似文献   

广西寒露风开始期短期气候预测方法研究     

覃卫坚  黄志  李耀先 《气象研究与应用》2014,35(3):11-14

利用1951-2013年广西90个气象观测站气温资料、国家气候中心74项指数和美国National Oceanic and Atmospheric Administration（NOAA）的Climate Prediction Center（CPC）60项指数以及海温和陆地雪盖资料、美国国家冰雪研究中心（NSIDC）的两极海冰资料,使用相关分析方法得到广西寒露风开始期气候影响因子,利用逐步回归和神经网络方法进行寒露风开始期的预测。结果表明：寒露风开始期与前一年9-10月北极海冰面积、当年3月南极海冰面积、前一年6月欧亚雪盖、当年5月北美雪盖、北半球雪盖的相关显著。与前一年9月北半球极涡面积指数、前一年10月亚洲区极涡面积指数、前一年3月热带印度洋海温偶极子等指数相关显著。粒子群-神经网络方法预测误差低于逐步回归方法,预报能力有明显提高。  相似文献   

Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping     

《ISPRS Journal of Photogrammetry and Remote Sensing》2014

Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas.  相似文献   

Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands     

《International Journal of Applied Earth Observation and Geoinformation》2015

Leaf pigment content provides valuable insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast, nondestructive method for pigment estimation. A number of methods were used previously for estimation of leaf pigment content, however, spectral bands employed varied widely among the models and data used. Our objective was to find informative spectral bands in three types of models, vegetation indices (VI), neural network (NN) and partial least squares (PLS) regression, for estimating leaf chlorophyll (Chl) and carotenoids (Car) contents of three unrelated tree species and to assess the accuracy of the models using a minimal number of bands. The bands selected by PLS, NN and VIs were in close agreement and did not depend on the data used. The results of the uninformative variable elimination PLS approach, where the reliability parameter was used as an indicator of the information contained in the spectral bands, confirmed the bands selected by the VIs, NN, and PLS models. All three types of models were able to accurately estimate Chl content with coefficient of variation below 12% for all three species with VI showing the best performance. NN and PLS using reflectance in four spectral bands were able to estimate accurately Car content with coefficient of variation below 14%. The quantitative framework presented here offers a new way of estimating foliar pigment content not requiring model re-parameterization for different species. The approach was tested using the spectral bands of the future Sentinel-2 satellite and the results of these simulations showed that accurate pigment estimation from satellite would be possible.  相似文献   

Primary proton spectrum around the knee deduced from the emulsion-chamber data obtained at Mts. Fuji and Kanbala     

J. Huang  N. Hotta  K. Kasahara  I. Ohta  S. Ozawa  T. Saito  M. Shibata  X. W. Xu  T. Yuda 《Astroparticle Physics》2003,18(6):637-648

We have done extensive Monte Carlo simulations using the new simulation codes of CORSIKA and COSMOS to compare with the gamma-family data obtained at Mts. Fuji (3750 m above sea level) and Kanbala (5500 m above sea level). Then, we estimated the primary proton and helium spectra around the knee energy region using a multiple-layered feed-forward neural network as a classifier of primary particle kind. The selection efficiency of proton-induced family events is estimated to be 82%. The flux value of protons at 2×10¹⁵ eV is (5.5±1.5)×10⁻¹⁴ (m⁻² s⁻¹ sr⁻¹ GeV⁻¹). The result suggests heavy-enriched primary composition around the knee region.  相似文献   

Derivation of Photosynthetically Available Radiation from METEOSAT data in the German Bight with Neural Nets   总被引：1，自引：0，他引：1  

Kathrin Schiller 《Ocean Dynamics》2006,56(2):79-85

Two different models, a Physical Model and a Neural Net (NN), are used for the derivation of the Photosynthetically Available Radiation (PAR) from METEOSAT data in the German Bight; advantages and disadvantages of both models are discussed. The use of a NN for derivation of PAR should be preferred to the Physical Model because by construction, a NN can take the various processes determining PAR on a surface much better into account than a non-statistical model relying on averaged relations.

Kathrin SchillerEmail:

  相似文献   

Gap Filling and Quality Assessment of CO2 and Water Vapour Fluxes above an Urban Area with Radial Basis Function Neural Networks     

A. Schmidt  T. Wrzesinsky  O. Klemm 《Boundary-Layer Meteorology》2008,126(3):389-413

Vertical turbulent fluxes of water vapour, carbon dioxide, and sensible heat were measured from 16 August to the 28 September 2006 near the city centre of Münster in north-west Germany. In comparison to results of measurements above homogeneous ecosystem sites, the CO₂ fluxes above the urban investigation area showed more peaks and higher variances during the course of a day, probably caused by traffic and other varying, anthropogenic sources. The main goal of this study is the introduction and establishment of a new gap filling procedure using radial basis function (RBF) neural networks, which is also applicable under complex environmental conditions. We applied adapted RBF neural networks within a combined modular expert system of neural networks as an innovative approach to fill data gaps in micrometeorological flux time series. We found that RBF networks are superior to multi-layer perceptron (MLP) neural networks in the reproduction of the highly variable turbulent fluxes. In addition, we enhanced the methodology in the field of quality assessment for eddy covariance data. An RBF neural network mapping system was used to identify conditions of a turbulence regime that allows reliable quantification of turbulent fluxes through finding an acceptable minimum of the friction velocity. For the data analysed in this study, the minimum acceptable friction velocity was found to be 0.15 m s⁻¹. The obtained CO₂ fluxes, measured on a tower at 65 m a.g.l., reached average values of 12 μmol m⁻² s⁻¹ and fell to nighttime minimum values of 3 μmol m ⁻² s⁻¹. Mean daily CO₂ emissions of 21 g CO₂ m⁻²d ⁻¹ were obtained during our 6-week experiment. Hence, the city centre of Münster appeared to be a significant source of CO₂. The half-hourly average values of water vapour fluxes ranged between 0.062 and 0.989 mmol m⁻² s⁻¹and showed lower variances than the simultaneously measured fluxes of CO₂.  相似文献   

Prediction of pile settlement using artificial neural networks based on standard penetration test data   总被引：4，自引：0，他引：4  

F. Pooya Nejad  Mark B. Jaksa  M. Kakhi  Bryan A. McCabe 《Computers and Geotechnics》2009,36(7):1125-1133

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.  相似文献   

Using neural networks for the derivation of Runge-Kutta-Nyström pairs for integration of orbits     

Ch. Tsitouras  I.Th. Famelis 《New Astronomy》2012,17(4):469-473

In this paper we present Runge-Kutta-Nyström (RKN) pairs of orders 4(3) and 6(4). We choose a test orbit from the Kepler problem to integrate for a specific tolerance. Then we train the free parameters of the above RKN4(3) and RKN6(4) families to perform optimally. For that we form a neural network approach and minimize its objective function using a differential evolution optimization technique. Finally we observe that the produced pairs outperform standard pairs from the literature for Pleiades orbits and Kepler problem over a wide range of eccentricities and tolerances.  相似文献   

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations     

R. Qahwaji  T. Colak  M. Al-Omari  S. Ipson 《Solar physics》2008,248(2):471-483

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.  相似文献