Prediction of hourly actual evapotranspiration using neural networks,genetic programming,and statistical models

The complexity of the evapotranspiration process and its variability in time and space have imposed some limitations on previously developed evapotranspiration models. In this study, two data‐driven models: genetic programming (GP) and artificial neural networks (ANNs), and statistical regression models were developed and compared for estimating the hourly eddy covariance (EC)‐measured actual evapotranspiration (AET) using meteorological variables. The utility of the investigated data‐driven models was also compared with that of HYDRUS‐1D model, which makes use of conventional Penman–Monteith (PM) model for the prediction of AET. The latent heat (LE), which is measured using the EC method, is modelled as a function of five climatic variables: net radiation, ground temperature, air temperature, relative humidity, and wind speed in a reconstructed landscape located in Northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg–Marquardt and Bayesian regularization. The GP technique was used to generate mathematical equations correlating AET to the five climatic variables. Furthermore, the climatic variables, as well as their two‐factor interactions, were statistically analysed to obtain a regression equation and to indicate the climatic factors having significant effect on the evapotranspiration process. HYDRUS‐1D model as an available physically based model was examined for estimating AET using climatic variables, leaf area index (LAI), and soil moisture information. The results indicated that all three proposed data‐driven models were able to approximate the AET reasonably well; however, GP and regression models had better generalization ability than the ANN model. The results of HYDRUS‐1D model exhibited that a physically based model, such as HYDRUS‐1D, might be comparable or even inferior to the data‐driven models in terms of the overall prediction accuracy. Based on the developed GP and regression models, net radiation and ground temperature had larger contribution to the AET process than other variables. Copyright © 2010 John Wiley & Sons, Ltd.     

Impacts of climate change and human activities on surface runoff in the Dongjiang River basin of China

Observed rainfall and flow data from the Dongjiang River basin in humid southern China were used to investigate runoff changes during low‐flow and flooding periods and in annual flows over the past 45 years. We first applied the non‐parametric Mann–Kendall rank statistic method to analyze the change trend in precipitation, surface runoff and pan evaporation in those three periods. Findings showed that only the surface runoff in the low‐flow period increased significantly, which was due to a combination of increased precipitation and decreased pan evaporation. The Pettitt–Mann–Whitney statistical test results showed that 1973 and 1978 were the change points for the low‐flow period runoff in the Boluo sub‐catchment and in the Qilinzui sub‐catchment, respectively. Most importantly, we have developed a framework to separate the effects of climate change and human activities on the changes in surface runoff based on the back‐propagation artificial neural network (BP‐ANN) method from this research. Analyses from this study indicated that climate variabilities such as changes in precipitation and evaporation, and human activities such as reservoir operations, each accounted for about 50% of the runoff change in the low‐flow period in the study basin. Copyright © 2010 John Wiley & Sons, Ltd.     

http://www.sciencedirect.com/science/article/pii/S1674987112000254

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters.In particular,the behavior of earth resembles the non-linearity applications.An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth.Artificial Neural Networks(ANN) perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used.The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network.The single-layer feed-forward neural network with the back propagation algorithm is chosen as one of the well-suited networks after comparing the results.Initially,certain synthetic data sets of all three-layer curves have been taken for training the network,and the network is validated by the Held datasets collected from Tuticorin Coastal Region(78°7′30″E and 8°48′45″N),Tamil Nadu.India.The interpretation has been done successfully using the corresponding learning algorithm in the present study.With proper training of back propagation networks,it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network.The network is trained with more Vertical Electrical Sounding(VES) data,and this trained network is demonstrated by the field data.Groundwater table depth also has been modeled.     

基于遗传神经网络的克钦湖叶绿素反演研究

叶绿素a浓度能够在一定程度上反映内陆湖泊水质情况。为实现对克钦湖水体叶绿素a浓度的监测,于2010年8月15日对克钦湖进行了现场光谱测量和同步采样。通过分析叶绿素a浓度和光谱数据之间的关系,建立基于反射比、人工神经网络和遗传神经网络的叶绿素a浓度估测模型。结果表明:利用R700nm/R670nm反射比建立的模型估测精度为R2=0.67;人工神经网络模型的估测精度较高,R2=0.882;将遗传算法引入神经网络之后,模型的估测精度进一步提高,R2达到0.956,将模型预测的结果与克里格内插法相结合对研究区的叶绿素a空间分布情况进行定量估测,发现北湖的叶绿素a浓度明显高于南湖,有由北向南逐渐递减的趋势,这为今后利用高光谱数据对克钦湖叶绿素a浓度大面积遥感反演提供了研究基础。     

基于过滤文本和社交网络的用户常驻位置预测

社交用户的文本具有地理差异性,并且社交关系密切的用户之间居住位置更近,因而文本和社交网络均可用于推断用户常驻位置。现有基于文本和社交网络的用户常驻位置预测方法对文本的位置指示性特征挖掘不充分,而用户文本中地名等位置指示信息却提供了最有用的位置信号。因此,本文提出一种基于地理命名实体识别（GER）和图卷积神经网络（GCN）的社交用户位置预测方法。首先,通过地理命名实体识别方法对用户文本进行过滤以凸显位置指示性特征;其次,基于提及关系和关注与被关注关系抽取社交网络;再次,结合社交网络和用户文本内容,采用基于图卷积神经网络的方法进行用户常驻位置预测;最后,将GER-GCN与GCN以及最新研究成果进行比较,并探究该模型的小样本学习能力及其影响因素。基于Geotext数据集和2个微博数据集的实验表明：① GER文本过滤方法可显著提升用户位置预测精度;② 在所有实验中,GER-GCN的预测精度最高,并在基准数据集GeoText上比最新研究成果提升1%~2%;③ 在最小监督的现实场景中,本文印证了GER-GCN模型的小样本学习能力,并发现社交网络质量对其小样本学习能力起到决定性作用。实验结果验证了GER-GCN方法的先进性,且该方法符合社交媒体现实场景的应用需求。     

分形理论在交通网络分布形态研究中的应用

探讨了以分形几何理论为基础的表达空间网络分布特征的几种分维数,重点描述了相似分维数的定义及其地理意义,以实例说明了相似分维数在交通网络分布特征研究中的应用,对测算结果进行了分析和评述。     

The international outer planets watch atmospheres node database of giant-planet images

The Atmospheres Node of the International Outer Planets Watch (IOPW, formerly known as International Jupiter Watch; Russell et al., 1990) intends to encourage and coordinate the imaging observations and study of the atmospheres of the Giant Planets. The main activity of the atmospheres node is to provide an interaction between the professional and amateur astronomical communities maintaining a large database of images of the giant planets (primarily Jupiter and Saturn but with increasing contributions of Uranus and Neptune too). The observational datasets of Jupiter and Saturn correspond to images obtained in the visible range (300 nm-1 μm), during the last decade, most of them performed by amateur observers. We here describe the organization and structure of the database as posted on the Internet and in particular the PVOL software (Planetary Virtual Observatory Laboratory) designed to manage the site in the spirit of the Virtual Observatory projects. We also describe with examples the important role of the amateur-professional collaboration in the study of the atmospheres of Jupiter and Saturn in an epoch of large telescopes and spacecraft observations of both planets.     

基于BP-Adaboost方法的天然地震和人工爆炸事件波形信号分类识别研究

BP神经网络和支持向量机(SVM)是两种主流的分类识别方法,用于天然地震和人工爆炸事件波形信号分类识别时取得了较好的效果。但BP神经网络存在易陷入局部最优及隐层数和隐层节点数与训练样本数据密切相关而无法有效预先确定;而支持向量机(SVM)方法则缺乏有效手段来选取合适的核函数,从中不能很好地扩展到多分类。针对天然地震和人工爆炸事件波形信号的分类识别问题,文中将上述两种方法和集成学习——BP-Adaboost方法进行了对比实验研究。据对所选用的地震、爆炸事件波形信号数据集的分类识别结果表明,BP-Adaboost方法得到了98%以上的正确识别率,并且具有较好的泛化能力。相较于BP神经网络和PCA-SVM方法,BP-Adaboost方法对于数据集的划分和识别结果具有更好的鲁棒性,应用于天然地震和人工爆炸事件波形信号分类识别时,可取得更好的识别效果。同时,结合Adaboost方法的原理,阐述了BP-Adaboost方法拥有更好分类结果和泛化能力的原因。     

Intelligent reservoir operation system based on evolving artificial neural networks

We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.     

海底天然气水合物中甲烷逸出对全球气候的影响

全球气候变化会降低海底天然气水合物的稳定性导致水舍物失稳分解,反过来天然气水合物分解释放出的大量甲烷气,又会对全球气候变化产生巨大的影响,地质历史上许多重大地质事件(如LPTM事件)都可能与天然气水合物的分解释气作用有关．除了地质历史上强烈的甲烷突然释放事件,现代海底的渗漏或喷口也连续不断地向海水甚至大气输入甲烷,从而影响着全球气候变化．由于天然气水合物中甲烷逸出对全球气候影响的研究刚刚起步,还缺乏海洋沉积物和海水中甲烷传输的恰当模式,甲烷在海水中的溶解度、甲烷的氧化作用及上升流等因素对其的影响程度,以及它对大气甲炕和二氧化碳浓度变化的具体贡献等目前还很不清楚,亟需深化研究．