Novel deseasonalizing models for improving the prediction of total ozone in column using evolutionary programming and neural networks     

S. Salcedo-Sanz  J.L. Camacho  .M. Prez-Bellido  E. Hernndez-Martín 《Journal of Atmospheric and Solar》2010,72(18):1333-1340

In this paper we present a novel method for deseasonalizing TOC data using non-linear models, with evolutionary computation techniques, and its performance with a neural network as regression approach. Specifically, the proposed deseasonalization method uses an evolutionary programming (EP) approach to carry out a curve fitting problem, where a given function model is optimized to be as similar as possible to an objective curve (a real TOC measurement in this case). Different non-linear models are proposed to be optimized with the EP algorithm. In addition, we test the possibility of deseasonalizing the TOC measurement and also the meteorological input data. The deseasonalized series is then used to train a neural network (multi-layer perceptron). We test the proposed models in the prediction of several TOC series in the Iberian Peninsula, where we carry out a comparison against a reference deseasonalizing model previously proposed in the literature. The results obtained show the good performance of some of the deseasonalizing models proposed in this paper.  相似文献   

基于神经网络的房地产估价研究     

王晞 《测绘与空间地理信息》2013,(10):129-132

根据房地产估价的特点将人工神经网络引入到房地产价格评估领域,创建了基于神经网络的房地产估价模型,在对训练样本验证结果分析的基础上,对两种神经网络估价模型进行了对比分析,指出了两种网络各自的优缺点以及改进措施。  相似文献   

长短期记忆神经网络(LSTM)模型在低能见度预报中的应用     

方楠  谢国权  阮小建  任晨平  姜舒婕  张玮玮 《气象与环境学报》2022,38(5):34-41

利用浙江省义乌市2015—2019年逐小时气象观测数据(相对湿度、风速、地气温差、能见度)和空气质量指数(Air Quality Index, AQI)数据, 分析了义乌地区低能见度天气(观测能见度lt; 10 km)的分布特征和气象要素条件。利用长短期记忆神经网络(Long Short Term Memory Neural Network, LSTM)模型对逐小时能见度进行模拟, 分别对比了观测能见度作为输入变量与否的模拟效果; 根据义乌地区低能见度天气条件的特征, 将模拟时段分为三个时期(11月至翌年2月, 3—6月, 7—10月), 对比了分时期模拟的效果; 以及评估了模型的预报步长。结果表明: 高湿、高污染、气温高于地温和低风速是义乌地区低能见度天气的主要特征。LSTM模型对单站能见度有较好的模拟效果, 当输入参数中加入历史观测能见度时, 能大幅提高模拟准确度, 日均能见度模拟结果均方根误差RMSE=0.63 km, 平均绝对误差MAE=0.51 km, 拟合优度R²=0.99;分时期进行模拟能得到更精准的模拟结果。本研究中选用的输入要素在冬季(11月至翌年2月)模拟效果最好, RMSE=2.35 km, MAE=1.46 km, 低能见度均方根误差RMSE_10 km=1.81 km, 低能见度平均绝对误差MAE_10 km=1.13 km, R²=0.83; 3—6月的模拟中, 输入变量中不加AQI模拟效果更好, 这意味着3—6月义乌地区的低能见度天气以雾天气为主导, 加入过多变量并不一定能提高模型准确度; 随着预报步长增大, 模型预报效果变差, 预测步长等于3 h, R²=0.71, 预测结果已不具备实际应用意义。  相似文献   

Efficient collection of training data for sub-pixel land cover classification using neural networks     

Stien Heremans  Bert Bossyns  Herman Eerens  Jos Van Orshoven 《International Journal of Applied Earth Observation and Geoinformation》2011

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.  相似文献   

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation     

Dhaval Vyas  N.S.R. Krishnayya  K.R. Manjunath  S.S. Ray  Sushma Panigrahy 《International Journal of Applied Earth Observation and Geoinformation》2011

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.  相似文献   

Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine     

Maarit Middleton  Paavo Nrhi  Raimo Sutinen 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(3):287-297

In a humid northern boreal climate, the success rate of artificial regeneration to Scots pine (Pinus sylvestris L.) can be improved by including a soil water content (SWC) based assessment of site suitability in the reforestation planning process. This paper introduces an application of airborne visible-near-infrared imaging spectroscopic data to identify suitable subregions of forest compartments for the low SWC-tolerant Scots pine. The spatial patterns of understorey plant species communities, recorded by the AISA (Airborne Imaging Spectrometer for Applications) sensor, were demonstrated to be dependant on the underlying SWC. According to the nonmetric multidimensional scaling and correlation results twelve understorey species were found to be most abundant on sites with high soil SWCs. The abundance of bare soil, rocks and abundance of more than ten species indicated low soil SWCs. The spatial patterns of understorey are attributed to time-stability of the underlying SWC patterns. A supervised artificial neural network (radial basis functional link network, probabilistic neural network) approach was taken to classify AISA imaging spectrometer data with dielectric (as a measure volumetric SWC) ground referencing into regimes suitable and unsuitable for Scots pine. The accuracy assessment with receiver operating characteristics curves demonstrated a maximum of 74.1% area under the curve values which indicated moderate success of the NN modelling. The results signified the importance of the training set’s quality, adequate quantity (>2.43 points/ha) and NN algorithm selection over the NN algorithm training parameter optimization to perfection. This methodology for the analysis of site suitability of Scots pine can be recommended, especially when artificial regeneration of former mixed wood Norway spruce (Picea abies L. Karst) - downy birch (Betula pubenscens Ehrh.) stands is being considered, so that artificially regenerated areas to Scots pine can be optimized for forestry purposes.  相似文献   

Neural-network predictability experiments for nearshore sandbar migration     

L. Pape  B.G. Ruessink 《Continental Shelf Research》2011,31(9):1033-1042

Cross-shore migratory behavior of nearshore sandbars is commonly studied with nearshore bathymetric-evolution models that represent underlying processes of hydrodynamics and sediment transport. These models, however, struggle to reproduce natural cross-shore sandbar behavior on timescales of a few days to weeks and have uncertain skill on longer scales of months to years. One particular concern for the use of models on prediction timescales that far exceed the timescale of the modeled processes is the exponential accumulation of errors in the nonlinear model equations. The relation between cross-shore sandbar migration, sandbar location and wave height has previously been demonstrated to be weakly nonlinear on timescales of several days, but it is unknown how this nonlinearity affects the predictability of long-term (months to years) cross-shore sandbar behavior. Here we study the role of nonlinearity in the predictability of sandbar behavior on timescales of a few days to several months with data-driven neural network models. Our analyses are based on over 5600 daily-observed cross-shore sandbar locations and daily-averaged wave forcings from the Gold Coast, Australia, and Hasaki, Japan. We find that neural network models are able to hindcast many aspects of cross-shore sandbar behavior, such as rapid offshore migration during storms, slower onshore return during quiet periods, seasonal cycles and annual to interannual offshore-directed trends. Although the relation between sandbar migration, sandbar location and wave height is nonlinear, sandbar behavior can be hindcasted accurately over the entire lifespan of the sandbars at the Gold Coast. Contrastingly, it is difficult to hindcast the long-term offshore-directed trends in sandbar behavior at Hasaki because of exponential accumulation of errors over time. Our results further reveal that during periods with low-wave conditions it becomes increasingly difficult to predict sandbar locations, while during high waves predictions become increasingly accurate.  相似文献   

盆地基底岩性的综合地球物理预测方法——以松辽盆地滨北地区基底岩性预测为例   总被引：3，自引：2，他引：1       下载免费PDF全文

李成立  崔瑞华  刘益中 《地球物理学报》2011,54(2):491-498

预测盆地基岩岩性不仅对于研究盆地的深部地质结构及盆地的形成演化具有重要的意义,而且也对基岩风化壳油气藏的勘探具有一定的指导作用.本文通过对盆地重、磁异常成因的综合分析,提出了一系列盆地基底岩性综合预测研究的综合地球物理资料处理解释方法技术.指出在地震构造界面的约束下采用重力剥皮技术可以较为可靠地获取基底岩性重力异常并分...  相似文献   

基于GIS和BP神经网络的区域农村贫困空间模拟分析——一种区域贫困程度测度新方法   总被引：5，自引：0，他引：5  

曾永明  张果 《地理与地理信息科学》2011,27(2):70-75

基于区域农村贫困程度测定方式不完善及缺乏地理视角的现状,选取四川省36个国家级扶贫县为实证对象,构建自然社会经济全面耦合的农村贫困测度指标体系,分析区域农村贫困的影响机制,并运用GIS与BP神经网络模拟区域自然致贫指数、社会致贫指数和经济消贫指数的空间分布格局。在此基础上,提出了全面表征区域农村贫困程度的区域扶贫压力指数——一种新的区域农村贫困测度方法,为国家扶贫政策文件《财政扶贫资金管理办法》中关于财政扶贫资金基于区域农村贫困程度分配提供实践基础。  相似文献   

NNFFC-adaptive output feedback trajectory tracking control for a surface ship at high speed     

Li-Jun Zhang  Xue Qi 《Ocean Engineering》2011,38(13):1430-1438

An adaptive output feedback controller based on neural network feedback-feedforward compensator (NNFFC) which drives a surface ship at high speed to track a desired trajectory is designed. The tracking problem of the surface ship at low speed has been widely investigated. However, the coupling interactions among the forces from each degree of freedom (DOF) have not been considered in general. Furthermore, the influence of the hydrodynamic damping is also simplified into a linear form or neglected. On the contrary, coupling interactions and the nonlinear characteristics of the hydrodynamic damping can never be neglected in high speed maneuvering situation. For these reasons, the influence of the nonlinear hydrodynamic damping on the tracking precision is considered in this paper. Since the hydrodynamic coefficients of the surface ship at high speed are very difficult to be accurately estimated as a prior, it will be compensated by NNFFC as an unknown part of the tracking dynamics system. The stability analysis will be given by the Lyapunov theory. It indicates that the proposed control scheme can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded (UUB), and numerical simulations can illustrate the excellent tracking performance of the surface ship at high speed under the proposed control scheme.  相似文献