Geostatistics

The purpose of this study was to assess the spatial variability of the activity of three hydrolytic enzymes, i.e. urease activity (UAc), alkaline phosphatase activity (APAc), and arylsulfatase activity (ASAc), in pasture topsoils using geostatistics. Enzyme activities along a transect in a 1.35-ha pasture were determined using 77 soil samples from the upper 20 cm of soil. UAc varied from 101.0 to 182.7 μg N g−1 soil h−1; APAc varied from 1.56 to 3.62 μg p-nitrophenol g−1 soil h−1; and ASAc varied from 1.50 to 3.26 μg p-nitrophenol g−1 soil h−1. The linear models fit the best semivariogram models for UAc, APAc, and ASAc. Semivariograms for enzyme activities exhibited spatial dependence with ranges of influence of approximately 124.7 m.

Gridding methods produce a regularly spaced, rectangular array of Z values from irregularly spaced XYZ data. Gridding generates a Z value at each grid node by interpolating or extrapolating the data values. Different gridding methods provide different interpretations of data because each method calculates grid node values by using a different algorithm. In this paper, we apply and compare different gridding methods including Inverse Distance Weighting, Kriging, Minimum Curvature, Modified Shepard's Method, Natural Neighbor, Nearest Neighbor, Polynomial Regression, Radial Basis Function, Triangulation with Linear Interpolation, Moving Average and Local Polynomial for various pilot regions with varied elevations. Grid method parameters which are able to set when producing a grid file, control the interpolation procedures. We also change these parameters to evaluate their effects on the resulted precision. We use a set of checkpoints to compare the precision of these methods. To implement these methods, various elevation data with equal precision is used; therefore, the results are affected only by the interpolation method. In other words, the influences of data and other environmental factors on the resulted precision are reduced by using them similarly in all gridding methods. Eventually, RMSE is computed for each method with its special parameters in different regions using checkpoints. The resulted precision acquired from applying these methods as well as their advantages and disadvantages in various topographic regions are represented in this study.

Lack of accurate climate data is a problem that facing many Arab countries including Libya. This problem affects integrated planning on the environment and the agriculture. There is an increasing demand for gridded datasets of climate variables from fields such as hydrology, ecology, agriculture, climate change research and climate model verification. Consequently this paper attempted to make at spatial interpolation, of main climatic parameters using spatial geostatistics tools. This paper gave a brief overview of how to produce accurate climatic maps by producing a consistent series of climatic statistics which enables comparisons to be made across space and time using contemporary techniques that are inherent in the Science of Geographic Information Systems (GIS) and Genstat (Ordinary Kriging). The maps were created using Ordinary Kriging with accuracy of estimates for each 1000 m x 1000 m pixel. The kriged map for rainfall shows a rapid increase in mean annual rainfall from the ...

The aim of this study was the spatial analysis of the elements copper, lead and cinc in soil of the plate 168 (Algeria), located in the southeastern region of Antioquia Colombia. The samples used are part of the "Survey and compilation of information-Map Regional Geochemistry-Ingeominas (Colombia)" and were facilitated by Ingeominas for the project "Geochemical Map of Antioquia". For the preparation of the corresponding baselines, were applied lognormal kriging geostatistical techniques. The assessment of pollution by heavy metals Cu-Pb-Zn in soil horizon plate 168, was developed taking into account the analysis of the background level, Environmental Accumulation and the Index enrichment. The results obtained indicate that content available for the elements Cu-Pb-Zn in these soils pose no real danger to be lower than its content phytotoxicity threshold given by some authors.

Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S(x) based on observations Yi = S(xi) + Zi at sampling locations xi, where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly inappropriate. The first concerns the assessment of residual contamination from nuclear weapons testing on a South Pacific island, in which the sampling method generates spatially indexed Poisson counts conditional on an unobserved spatially varying intensity of radioactivity; we conclude that a conventional geostatistical analysis oversmooths the data and underestimates the spatial extremes of the intensity. The second application provides a description of spatial variation in the risk of campylobacter infections relative to other enteric infections in part of north Lancashire and south Cumbri...

Nowadays, people anticipate floods using flood warning systems, and building stop banks and flood ways in place that use flood models generated with hydrological information in their design. Nevertheless, various regions in the world are still hit by floods with catastrophic effects to urban areas, because of a lack of local hydrological knowledge, especially of upstream areas in their catchments. This lack of hydrological knowledge is a result of difficult accessible highly elevated upstream areas, which makes monitoring of hydrological variables difficult or impossible. 

This thesis examines models for determining montane rainfall using spatial estimation methods and data sets. The distribution and quantity of montane rainfall were assessed by applying five appropriated spatial estimation methods, data of historical and current rain gauges, and a performance measurement. 

The methodology applied to gain more knowledge about montane rainfall was established with the results of a literature analysis of 40 articles about montane rainfall. This literature analysis revealed that ordinary kriging is the most frequently applied spatial estimation method for montane rainfall, with regression and regression kriging completing the top three of the most applied methods. Also, two other spatial estimation methods, empirical Bayesian kriging and geostatistical simulation, performed well with rainfall data. The same literature analysis disclosed that the root mean square error was predominantly used as a performance measure of spatial estimation methods.

The literature analysis revealed a number of data gap-filling techniques, with the inverse distance weighting method and the coefficient of correlation weighting method as the two most suitable techniques. These techniques were applied to complete historical rainfall data sets and their performance was compared within this research. The result showed that the coefficient of correlation weighting method outperformed the inverse distance weighting method in 74% of all data gap estimations, and the coefficient of correlation weighting method was 22% more accurate (based on the overall performance) than the inverse distance weighting method. The most accurate data gap-filling technique, the coefficient of correlation weighting method, was used to complete the historical rain gauges data.

The overall ranking of the spatial estimation methods revealed that Gaussian geostatistical simulation performed the best. Regression kriging was the second best spatial estimation method, but there was no significant difference with Gaussian geostatistical simulation. At the same time, the results showed that the best performance of the spatial estimations was accomplished without the maximum number of rain gauges. However, better visual representation of the distinct pattern of rainfall was generated with the historical rain gauges in the second and third experiment of the spatial estimations.

Finally, this research discussed the factors that can impact the performance of the spatial estimations. Two of these factors were the removal of “bad data” and the the strategic placing of rain gauges. The results of this research clearly defined that the removal of “bad data” increased the accuracy of estimation, while a more even and strategic distribution of rain gauges was suggested to increase the accuracy of the spatial estimation of rainfall.

The cooperation of agents in smart grids to form coalitions could bring benefit both for agent itself and the distribution power system. To tackle the problem as a game of partition form function poses significant computing challenges due to the huge search space for the optimization problem. In this paper, we propose a stochastic optimization approach using Population Based Incremental Learning (PBIL) algorithm with top-k Merit Weighting and a customized strategy for choosing the initial probability to solve the problem. Empirical results show that the proposed algorithm gives competitive performance compared with a few stochastic optimization algorithms.

Dans cet article nous essayons d’approcher l’ampleur et l’intensité du phénomène de l’occupation du sol à travers une cartographie multidate de sa dynamique, le suivi et la modélisation de ses changements et la simulation de son évolution future. La méthodologie utilisée se base sur le traitement d’images satellitales, l’analyse statistique des changements et la modélisation à l’aide d’un modèle probabiliste. Enfin, sur la base des résultats obtenus, le travail discute des avantages et des limites de l’approche prospective dans le domaine de la dynamique environnementale en général et spécifiquement dans le domaine de l’occupation du sol. Le choix du littoral de la région du Gharb comme zone d’investigation est en relation avec le fait que celle-ci a connu depuis le début des années 80 une dynamique assez spectaculaire au niveau de l’occupation du sol. Initialement, zone de parcours, les dunes côtières du Gharb ont été converti vers une agriculture marquée par une intensification de la production ce qui a conduit à une vive course à la terre et par la même à l’exploitation intensive des ressources naturelles notamment les sols et la nappe aquifère locale de Mnasra.

Watershed management and hydrological modeling require data related to the very important matter of precipitation, often measured using rain gauges or weather stations. Hydrological models often require a preliminary spatial interpolation as part of the modeling process. The success of spatial interpolation varies according to the type of model chosen, its mode of geographical management and the resolution used. The quality of a result is determined by the quality of the continuous spatial rainfall, which ensues from the interpolation method used. The objective of this article is to review the existing methods for interpolation of rainfall data that are usually required in hydrological modeling. We review the basis for the application of certain common methods and geostatistical approaches used in interpolation of rainfall. Previous studies have highlighted the need for new research to investigate ways of improving the quality of rainfall data and ultimately, the quality of hydrolog...

The current era is witnessing serious geopolitical changes during the Arab spring and its waves in the Arab countries specifically. This geopolitics change is reflecting serious geostrategic changes on the OIC (Organization of Islamic Countries) as well, which causes serious impacts on all OIC internally and externally. Under the forum theme “Unity in diversity: Source of power”, the paper introduces some crucial geostrategic concepts to develop common national and regional understanding in order to avoid geostrategic confrontation, and to exploit the strategic opportunities. The paper calls for geostrategic interests’ integration management, and for reaching the point of geostrategic equilibrium so as to start developing, what is called by the writer, geostrategic regional initiatives. This paper will propose crucial strategic imperatives within the context of the complex dynamic of the Arab spring’s transitional period and its impact on the region in order to support the unity of the OIC countries in this dilemma.

urce data and it is necessaw to standardize the traditional
classification pracedeies by quantifying the three basic evaluation
criteria of economic feasibility, geologic assurance and recovery.
For well documcntcd deposits, geostatistical methods can considerably
improve classification quality.
A geostatistically based algorithm for classifiying coal
resources within tlie current Queensland and New South Wales
codes has been developed. Application of the method in classifying
resources from a number of seains exhibiting different structural
characteristics is assessed. The inethod is found to give
classification results which closely reflect the error associated with
an estimate of resource quantities based on current sampling densities.
It requircs a careful geostatistical analysis with enlphasis on
geological awareness. Resource category restrictions must be arbitrarily
assigned and remain constant for all deposits being compared.
Engineering judgement is needed and interpretation may be required
with isolated and peripheral blocks

This thesis addresses the important question of how to incorporate location into a model for the mass valuation of residential (domestic) real property.  Two new methods for doing so are described.  The first is a process for detecting distinct market segments within a defined study area.  It makes use of Geographically Weighted Regression to construct a three dimensional point pattern surface throughout the area for a home of identical characteristics.  When the surface displays a discernable spatial structure, evidence is provided for the existence of market segments.  The segments detected by this technique are used in a system of models that account for large-scale effects of location on value and thereby improve the predictive accuracy of this model system as compared to a single global model of the same form.  The second is a modification to the comparable sales method of valuation such that it provides a prediction accuracy superior to both ordinary least squares (OLS) estimates and the comparables sales method itself.  It is formed by taking an optimal linear combination of the OLS model and the comparables sales model based on an examination of the spatial structure of the localized residual errors of the OLS.  In combination, these two methods provide improved predictive accuracy and a reduction in the spatial autocorrelation of the residual errors of the resultant predictions when compared to alternative model structures

Pour beaucoup de pays, les eaux souterraines constituent la principale source d'eau d'irrigation et de consommation dans les régions où cette ressource est rare, telles que les régions à climat semi-aride et aride. Cependant, l'utilisation rationnelle de cette eau et la réussite de toute mise en valeur agricole dépendra de sa connaissance quantitative mais également qualitative. Le présent travail a pour but de cartographier la salinité des eaux souterraines de la plaine du Moyen-Chéliff dont on a recourt souvent à ces eaux pour assurer aussi bien l'irrigation que la consommation domestique. Le krigeage d'indicatrices qui est une méthode géostatistique non paramétrique a été appliqué pour optimiser l'estimation spatiale de la salinité de ces eaux et contribuer ainsi à une meilleure connaissance de sa qualité. La comparaison entre les estimations faites par krigeage ordinaire et d'indicatrices a montré que ce dernier a permis une meilleure estimation spatiale de la salinité en termes de superficie et d'incertitude. Mot clés : cartographie, eau souterraine, krigeage d'indicatrices, salinité ABSTRACT For many countries, the subsoil waters constitute the principal source of irrigation water and consumption in the areas where this resource is rare, such as the areas with semi-arid and arid climate. However, the rational use of this water and the success of agricultural development will depend on its quantitative but also qualitative knowledge. The purpose of this work is to map the salinity of subsoil waters of of the Middle-Cheliff plain which are often used in irrigation and domestic consumption. The indicator kriging which is a nonparametric geostatistic method was applied to optimize the spatial estimating of the water salinity and to thus contribute to a better knowledge of its quality. The comparison between estimates made by ordinary kriging and indicator kriging showed that last method allowed a better spatial estimating of salinity in both surface and uncertainty.