Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning

Evaporation of water from free water surfaces or from land surfaces is one of the main components of the hydrological cycle, and its occurrence is governed by various meteorological and physical factors. There is a multitude of models developed for estimating daily evaporation values by using weather data. This paper evaluates a Gene Expression Programming (GEP) model for estimating evaporation through spatial and temporal data scanning techniques. It is by using ‘leave‐one‐out’ procedures, a complete scan of the possible train and test set configurations is carried out according to temporal and spatial criteria. Comparison of the GEP model with empirical‐physical models shows that daily evaporation values computed by the GEP model are more accurate. Further, local calibration of the GEP model may not be needed, if enough climatic data are available at other stations. The performance of the GEP model fluctuates throughout the period of study and between stations. A suitable assessment of the model should consider a complete temporal and/or spatial scan of the data set used. Copyright © 2012 John Wiley & Sons, Ltd.     

Second-order non-stationary modeling approaches for univariate geostatistical data

A fundamental decision to make during the analysis of geostatistical data is the modeling of the spatial dependence structure as stationary or non-stationary. Although second-order stationary modeling approaches have been successfully applied in geostatistical applications for decades, there is a growing interest in second-order non-stationary modeling approaches. This paper provides a review of modeling approaches allowing to take into account the second-order non-stationarity in univariate geostatistical data. One broad distinction between these modeling approaches relies on the way that the second-order non-stationarity is captured. It seems unlikely to prove that there would be the best second-order non-stationary modeling approach for all geostatistical applications. However, some of them are distinguished by their simplicity, interpretability, and flexibility.     

A universal kriging approach for spatial functional data

In a wide range of scientific fields the outputs coming from certain measurements often come in form of curves. In this paper we give a solution to the problem of spatial prediction of non-stationary functional data. We propose a new predictor by extending the classical universal kriging predictor for univariate data to the context of functional data. Using an approach similar to that used in univariate geostatistics we obtain a matrix system for estimating the weights of each functional variable on the prediction. The proposed methodology is validated by analyzing a real dataset corresponding to temperature curves obtained in several weather stations of Canada.     

Comparison of two approaches for generation of daily rainfall data

There has been extensive research on the problem of stochastically generating daily rainfall sequences for use in water management applications. Srikanthan and McMahon [Australia Water Resources Council, Canberra, 1985] proposed a transition probability matrix (TPM) model that performs better for Australian rainfall than many alternative models, particularly where long records (say 100 years) are available. Boughton [Report 99/9, CRC for Catchment Hydrology, Monash University, Melbourne, 21pp, 1999] incorporated an empirical adjustment into the TPM model that allows the model to reproduce the observed variability in the annual rainfall. More recently, Harrold et al. [Water Resour Res 39(10, 12):1300, 1343, 2003a,b] proposed nonparametric models for the generation of daily rainfall occurrences and rainfall amounts on wet days. By conditioning on short, medium and long-term characteristics, this approach is also able to preserve the variability in annual rainfall. In this study, the above two approaches were used to generate daily rainfall data for Sydney and Melbourne, and the results evaluated. Both approaches preserved most of the daily, monthly and annual characteristics that were compared, with the nonparametric approach providing marginally better performance at the cost of greater model complexity. The nonparametric approach was also able to preserve the variability and persistence in the annual number of wet days.     

Quantitative comparison of spatial fields for hydrological model assessment––some promising approaches

The current practice for assessing spatial predictions from distributed hydrological models is simplistic, with visual inspection and occasional point observations generally used for model assessment. With the increasing availability of spatial observations from remote sensing and intensive field studies, the current methods for assessing the spatial component of model predictions need to advance. This paper emphasises the role that spatial field comparisons can play in model assessment. A review of the current methods used in hydrology, and other disciplines where spatial field comparisons are widely used, reveals some promising methods for quantitatively comparing spatial fields. These promising approaches––segmentation, importance maps, fuzzy comparison and multiscale comparison––are for local comparison of spatial fields. They address some of the weaknesses with the current approaches to spatial field comparison used in hydrological modelling and, in doing so, emulate some aspects of human visual comparison. The potential of these approaches for assessing spatial predictions and understanding model performance is illustrated with a simple example.     

An improved HASM method for dealing with large spatial data sets

Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method (HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem (LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations. A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model. Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion, HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.     

Bootstrap Pettitt test for detecting change points in hydroclimatological data: case study of Itaipu Hydroelectric Plant,Brazil

ABSTRACT

The Pettitt test is widely used in climate change and hydrological analyses. However, studies show difficulties of this test in detecting change points, especially in small samples. This study presents a bootstrap application of the Pettitt test, and compares it numerically with the classical Pettitt test by an extensive Monte Carlo simulation. The proposed test outperforms the classical test in all simulated scenarios. An application of the tests is conducted on the historical series of naturalized flows of the Itaipu Hydroelectric Plant in Brazil, for which several studies have shown a change point in the 1970s. When the series is split into shorter sub-series, to simulate actual situations of small samples, the proposed test is more powerful than the classical Pettitt test in detecting the change point. The proposed test can be an important tool for detecting abrupt changes in water availability, in support of hydroclimatological resources decision making.     

Probabilistic data integration for characterization of spatial distribution of residual LNAPL

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.     

基于空间子集的地震数据分析方法(英文)

野外空间采样密度的提高将增加室内数据分析的工作量,常规的基于地震数据的点与线分析方法有一定的局限性。本文简要说明空间子集的抽取方法,列举了正交子集和斜交子集的特点,并通过三维可视化展示了子集数据的空间特性。提出在数据处理中利用子集的时间切片：（1）分析地震道空间分布的均匀性和规则性;（2）研究面波及规则干扰的空间分布特点;（3）检测叠前数据中的异常信息;（4）监控叠前去噪的效果。实际数据的应用结果表明基于空间子集的分析方法是一种独特有效的地震数据分析方法,从另一种视角观察地震数据,可以发现其中某些新的特征,以提高处理人员对数据的洞察能力。     

Integrated spatial sampling modeling of geospatial data

Sampling is to, by efficient selection of samples, acquire the accurate information about the population (the research object) at less cost. Spatial sampling is a kind of sampling toward geospatial objects or features with spatial correlation. The differences between effi-cient sampling and completely universal survey lie in quality, time and cost. Sampling provides a kind of economical, prompt and accurate survey[13]. Efficient spatial sampling can be regarded as the optimization of the sampl…     

Recognizing spatial distribution patterns of grassland insects: neural network approaches

The main objective of this study was to fit and recognize spatial distribution patterns of grassland insects using various neural networks, and to analyze the feasibility of neural networks for detecting spatial distribution patterns of grassland insects. BP neural network, Learning vector quantization (LVQ) neural network, linear neural network and Fisher’s linear discriminant analysis were used to fit and recognize spatial distribution patterns at different ecological scales. Various comparisons and analysis were conducted. The results showed that BP, LVQ and linear neural networks were better algorithms for recognizing spatial distribution patterns of grassland insects. BP neural network was the best algorithm to fit spatial distribution patterns. BP network may be used to recognize the spatial details of distribution patterns, and the recognition performance of BP network became better as the increase of the number of hidden layers and neurons. Performance of linear neural network for pattern recognition was similar to linear discrimination method. Linear neural network would yield better performance in finding the general trends of distribution patterns. Recognition performance of LVQ network was just between BP network and linear network. It was found that recognition performance of neural networks depended upon not only the ecological scale but also the criterion for classification. Under the uniform criterion, recognition efficiency of linear methods tended to be weak as ecological scale became to be coarser. A joint use of neural networks was suggested in order to achieve both overall and detailed understanding on spatial distribution patterns.     

High spatial resolution data acquisition for the geosciences: kite aerial photography

This paper highlights the requirement for very high resolution (<0·25 m) elevation data for quantitative and qualitative morphometric analyses. Traditional techniques for high resolution data capture (e.g. airborne, heliborne) are prohibitively expensive for small studies and therefore a kite‐based platform was developed, in conjunction with a consumer non‐metric digital camera, for data capture. The combination of kite and digital camera is more generally termed kite aerial photography (KAP). The accuracy of data derived by digital photogrammetry and imagery acquired using a kite based non‐metric camera is assessed by three experiments: one on smooth terrain, one on tor terrain and one on a glaciofluvial esker. Ground control targets were surveyed at all three sites, with the imagery subsequently processed using the Leica Photogrammetry Suite. The results demonstrate that the method can extract a high number of sampling points at high accuracy, provided that there is suitable image texture across the site. However, final judgment concerning the suitability of derived data is dependent upon an understanding of measurement variability and user quantification of acceptable accuracy. Copyright © 2008 John Wiley & Sons, Ltd.     

一种适合多源地球物理数据三维可视化的快速空间索引技术

识别复杂地质条件下的地质构造,常需要融合多种地球物理探测技术的数据进行分析,应用地球物理数据三维可视化技术可以更好地解释复杂的地质现象,传统的可视化方法由于缺乏对多源地球物理数据一体化的存储管理与索引机制,使得在对大范围多源地球物理数据进行空间局部更加精细可视化时的效率很低.为了更有效地洞察研究区域的地下构造,本文研究了适合多源地球物理数据三维可视化技术的快速空间索引技术.首先根据各类地球物理数据空间分布特点,提出了一种改进的四叉树结构,用于建立对多源地球物理数据一体化存储与管理.接着利用该数据结构,文章现实了多源地球物理数据快速空间查询的机制.将此结构和机制服务于大规模多源地球物理数据精细尺度下的三维可视化,提高对特定空间范围的局部多源地球物理数据动态可视化的效率.最后给出了该数据结构下空间查询与可视化的效率分析,并通过实验对整个算法的效率进行了验证.实验表明,通过建立相应的索引机制,可在大规模多源地球物理数据条件下更高效地展示任意位置岩矿石多个物理特性之间的空间关系,为多源地球物理数据的三维可视化提供技术支撑.     

A statistical framework of data fusion for spatial prediction of categorical variables

With rapid advances of geospatial technologies, the amount of spatial data has been increasing exponentially over the past few decades. Usually collected by diverse source providers, the available spatial data tend to be fragmented by a large variety of data heterogeneities, which highlights the need of sound methods capable of efficiently fusing the diverse and incompatible spatial information. Within the context of spatial prediction of categorical variables, this paper describes a statistical framework for integrating and drawing inferences from a collection of spatially correlated variables while accounting for data heterogeneities and complex spatial dependencies. In this framework, we discuss the spatial prediction of categorical variables in the paradigm of latent random fields, and represent each spatial variable via spatial covariance functions, which define two-point similarities or dependencies of spatially correlated variables. The representation of spatial covariance functions derived from different spatial variables is independent of heterogeneous characteristics and can be combined in a straightforward fashion. Therefore it provides a unified and flexible representation of heterogeneous spatial variables in spatial analysis while accounting for complex spatial dependencies. We show that in the spatial prediction of categorical variables, the sought-after class occurrence probability at a target location can be formulated as a multinomial logistic function of spatial covariances of spatial variables between the target and sampled locations. Group least absolute shrinkage and selection operator is adopted for parameter estimation, which prevents the model from over-fitting, and simultaneously selects an optimal subset of important information (variables). Synthetic and real case studies are provided to illustrate the introduced concepts, and showcase the advantages of the proposed statistical framework.     

Bootstrap confidence intervals for design storms from exceedance series

Abstract

An approach for better understanding of the physical implication of estimated aquifer parameters is demonstrated by analysing the pumping test data at Cottam in the Nottingham aquifer, UK. A sensitivity analysis showed that the area represented by the estimated parameters was much smaller than the area covered by the depression cone. After parameters are estimated, further research should be carried out to understand what portions of the aquifer the parameters represent. The parameters estimated at Cottam represented mainly aquifer features between roughly 100 and 2000 m. The sensitivity analysis also showed that the observed drawdown being satisfactorily matched by a model with uniform parameters does not prove that the aquifer is homogeneous. Slightly anomalous data may imply the existence of large anomalous zones. Although the drawdowns at Cottam could be ‘satisfactorily’ fitted by a model with uniform parameters, the fit could be improved by a model using a more permeable aquifer but with a zone about 700 m wide and with 42% less transmissivity.     

A kernel spatial density estimation allowing for the analysis of spatial clustering. Application to Monsoon Asia Drought Atlas data

A nonparametric density estimate that incorporates spatial dependency has not been studied in the literature. In this article, we propose a new spatial density estimator that depends on two kernels: one controls the distance between observations while the other controls the spatial dependence structure. The uniform almost sure convergence of the density estimate is established with the rate of convergence. The consistency of the mode of this kernel density is also studied. Then a spatial hierarchical unsupervised clustering algorithm based on the mode estimate is presented. Some simulations as well as an application to the Monsoon Asia Drought Atlas data illustrate the efficiency of our algorithm, and a comparison of the spatial structures of these data detected by the density estimate and clustering algorithm are done.     

New approaches in global ionospheric determination using ground GPS data

Since 1 June 1998, the group of Astronomy and Geomatics of the Polytechnic University of Catalonia (gAGE/UPC) is contributing to the international project of defining an ionospheric product (Total Electron Content, TEC) from the data gathered by the permanent ground GPS receivers of the International GPS Service (IGS) network. The strategy and algorithms related to such a preliminary product, its calibration with synthetic observations generated from the International Reference Ionosphere (IRI), and the comparison with TOPEX TEC data are presented. Finally, these methods are applied combining ionosonde with ground GPS data, in order to obtain the vertical structure of the free electron distribution.     

Evaporation modelling by heuristic regression approaches using only temperature data

Accurate estimation of pan evaporation (E_pan) is very important in water resources management, irrigation scheduling and water budget of lakes. This study investigates the accuracy of two heuristic regression approaches, multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in estimating pan evaporation using only temperature data as input. Monthly minimum temperature, maximum temperature and E_pan data from three Turkish stations were used, with month number (periodicity information) added as input to see its effect on estimation accuracy. The models were compared with the calibrated Hargreaves-Samani (CHS), Stephens-Stewart (SS) and multiple linear regression methods. Three different train-test splitting strategies (50%–50%, 60%–40% and 75%–25%) were employed for better evaluation of the applied methods. The results show that the MARS method generally estimated monthly E_pan with higher accuracy compared to the M5Tree, CHS and SS methods. When extraterrestrial radiation, calculated from Julian date and latitude information, was used as input to the SS instead of solar radiation, satisfactory estimates were obtained. A positive effect on model accuracy was observed when involving periodicity information in inputs and increasing training data length.