Identification of geospatial variability of fluoride contamination in ground water of Mathura District, Uttar Pradesh, India

Groundwater is one of the major sources of water in arid and semi-arid regions. Groundwater quality data and its spatial distribution are important for the purpose of planning and management. Geo-statistical methods are one of the most advanced techniques for interpolation of groundwater quality. In this study, kriging methods were used for predicting the spatial distribution of fluoride content in groundwater. Data were collected from 13 wells in Mathura district (Uttar Pradesh, India). After normalization of data, semivariogram was drawn, for selecting suitable model for fitness on experimental semivariogram, less residual sum of squares (RSS) value was used. Then fluoride endemic areas of the Mathura District (study area) were identified from developed semivariogram model and Geospatial variability (high and low fluoride containing areas) map was generated with the help of GeographicInformation System. In the analysis, spatial distribution characteristics and variation of fluoride concentration in shallow groundwater found to be 3.4 and 4.6 mg/l at Sahar, Shahpur were higher than the standard limits (1.5 mg/l) of drinking water and shows remarkable spatial variability

Exploration and estimation of gravel resource potential in southeast Chukchi Sea continental shelf off Kivalina, Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2005Frequent storm surges in the Alaskan arctic result in washovers and high erosion of barrier islands. The village council of Kivalina has resolved to relocate from its present location on a barrier island in Northwest arctic Alaska to an adjacent onshore site. The relocation plan envisages excavation of upper 4 meter of the 25 km² onshore permafrost ground and construction of a foundation pad. The objective of this research is to estimate the gravel resource potential in the continental shelf off Kivalina. In this context seismic surveys and sediment sampling were conducted. The seismic surveys were of limited use as they failed to resolve the upper 1-2 m of the seafloor. The lithostratigraphy indicated dominance of the 2.4-3.4 mm size fraction in the region north of Kivalina. The geostatistical analysis indicated an omnidirectional variogram fit to the data with ordinary kriging producing the best kriging estimate of the gravel resource potential. At least 20 x 10⁶ m³ of gravel above the 90 % cut-off is present in the upper 0.5 m of the seafloor. The regional Pleistocene glaciation has affected the lateral variations in gravel abundance in the nearshore southeast Chukchi Sea

Identification of homogeneous rainfall regions in New South Wales, Australia

Identifying homogeneous regions based on spatial variables is vital for providing a certain and fixed region’s spatial and temporal behavior. However, a significant problem of non-separation rises when the geographic coordinates are utilized for clustering, just because the Euclidean distance is not suitable for clustering when considering the geographic coordinates. Therefore, this study focuses on employing such methods where the non-separation is minimum for identifying homogenous regions. The average annual rainfall data of 226 meteorological monitoring stations for 1911–2018 of New South Wales (NSW), Australia, was considered for the current study. The data is standardized with zero mean and unit variance to remove the effect of different measurement scales. The geographical coordinates are then converted to rectangular coordinates by the Lambert projection method. Using the Partition Around Medoid (PAM) algorithm, also known as the kmedoid algorithm (which minimizes the sum of dissimilarities instead of the sum of squares of Euclidean distances) on rectangular Lambert projected coordinates, 10 well-separated clusters are obtained. The Mean Squared Prediction Error (MSPE) is comparatively smaller if the prediction of unobserved locations in cluster 3 is made. However, this error increases if the prediction is made for a complete monitoring network. The identified 10 homogeneous regions or clusters provide a good separation when the lambert coordinates are used instead of geographical coordinates

Improvements in groundwater flow modeling through the integration of resistivity logs and hydraulic conductivity and the use of variogram uncertainty

This study developed a coregionalized model to estimate hydraulic conductivity using spatial cross correlation between hydraulic conductivity and borehole geophysical data (a transform of the formation factor). An experimental pseudocross variogram is used instead of a cross variogram because data are not collocated. Experimental variogram uncertainty is investigated using confidence intervals for the experimental variogram calculated assuming variogram sills are lognormally distributed. These intervals are used for sensitivity modeling using kriging, cokriging, simulation and cosimulation. The hydraulic conductivity fields generated by kriging, cokriging, simulation, and cosimulation are then used in a high-resolution groundwater model created using telescopic mesh refinement (TMR) from a regional flow model of the Chicot Aquifer system in southwestern Louisiana. Results are analyzed to assess the significance of adding additional information (i.e., transform of formation factor), the process (i.e., kriging versus simulation and cokriging versus cosimulation) and variogram uncertainty on the groundwater flow model. Spatial images and flow predictions using regionalized models based on sparse conductivity data only are compared with coregionalized models using both conductivity and resistivity data, and the effects on model accuracy and robustness are discussed. Coregionalized model (i.e., cokriging) and simulation process (i.e., cosimulation) significantly affect groundwater flow model prediction. A new approach examines sensitivity of a capture zone groundwater model for the Chicot aquifer parameter uncertainty. Sensitivities to spatial variability of hydraulic conductivity, porosity, and aquifer thickness were investigated. The method calibrated aquifer properties to flow and geophysical data using cosimulation of hydraulic conductivity and formation factor, simulation for porosity, and kriging for aquifer thickness. Geostatistical model uncertainty was analyzed with a Bayesian method. Aquifer property models were scored using integral range to preserve correlation among variogram parameters. Variogram and pseudocrossvariogram models were selected from a lower bound, median, and upper bound of the posterior probability distribution of integral range. A steady-state two-dimensional groundwater flow model of the Chicot aquifer beneath Acadia Parish in Southwestern Louisiana examined capture zone sensitivity to spatial structure of aquifer properties. The capture zone model was insensitive to porosity variability and sensitive to hydraulic conductivity and aquifer thickness. The proposed method demonstrates the importance of model uncertainty compared with fluctuations of a fixed geostatistical model

Statistical Analyses of Ozone Temporal Trends in Calgary, Alberta: an Application of Multivariate Geostatistics

The prediction of tropospheric (surface) ozone episodes is a challenging task that requires the integration of physicochemical and statistical techniques. Governmental agencies such as the U.S. Environmental Protection Agency (EPA) and Alberta Environment favor physicochemical modeling in order to capture the complexity of the underlying physical processes. Unlike physicochemical models, statistical techniques are usually based on spatial and/or temporal correlations between relevant variates. The statistical models also require less exhaustive data sets for accurate predictions; this major advantage is perhaps more obvious when ozone prediction is performed for a longer period of interest. The primary objective of this research is to investigate statistical techniques for modeling ozone and/or other pollutant concentrations given only sparse environmental records at the monitoring stations. Straightforward linear regression based techniques are implemented initially but the inadequacy of these approaches for predicting detailed temporal ozone variations is verified by the results. Then geostatistical paradigms of kriging and sequential stochastic simulation are implemented to incorporate temporal correlation in the form ofvariogram. Secondary variables (covariates) can also be useful for providing extra information and their influence is accounted for in cokriging and cosimulation. The positive-definiteness of auto and cross-covariances are ensured via a linear model of coregionalization (LMC). The "two-point" statistic (variogram) is found to be insufficient and hence this thesis strives to explore methodologies for modeling the highly fluctuating temporal profiles with a view to providing a sound framework for subsequent extensions to spatiotemporal modeling. Il

A review of earth-viewing methods for in-flight assessment of modulation transfer function and noise of optical spaceborne sensors

Several earth observation satellites bear optical imaging sensors whose outputs are essential in many environmental aspects. This paper focuses on two parameters of the quality of the imaging system: the Modulation Transfer Function (MTF) and Signal to Noise Ratio (SNR). These two parameters evolve in time and should be periodically monitored in-flight to control the quality of delivered images and possibly mitigate defaults. Only a very limited number of past and current sensors have an on-board calibration device fully appropriate to the assessment of the noise and none of them has capabilities for MTF assessment. Most often, vicarious techniques should be employed which are based on the Earth-viewing approach: an image, or a combination of images, is selected because the landscape offers certain properties, e.g., well-marked contrast or on the contrary, spatial homogeneity, whose knowledge or modeling permit the assessment of these parameters. Several methods have been proposed to perform in-flight assessments. This paper proposes a review of the principles and techniques employed in this domain

Gaussian processes for RSS fingerprints construction in indoor localization

Location-based applications attract more and more attention in recent years. Examples of such applications include commercial advertisements, social networking software and patient monitoring. The received signal strength (RSS) based location fingerprinting is one of the most popular solutions for indoor localization. However, there is a big challenge in collecting and maintaining a relatively large RSS fingerprint database. In this work, we propose and compare two algorithms namely, the Gaussian process (GP) and Gaussian process with variogram, to estimate and construct the RSS fingerprints with incomplete data. The fingerprint of unknown reference points is estimated based on measurements at a limited number of surrounding locations. To validate the effectiveness of both algorithms, experiments using Bluetooth-low-energy (BLE) infrastructure have been conducted. The constructed RSS fingerprints are compared to the true measurements, and the result is analyzed. Finally, using the constructed fingerprints, the localization performance of a probabilistic fingerprinting method is evaluated

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

Learning from biophysical heterogeneity: inductive use of case studies for maize cropping systems in Central America

Global society has become conscious that efforts towards securing food production will only be successful if agricultural production increases are obtained through mechanisms that ensure active regeneration of the natural resource base. Production options should be targeted in the sense of that their suitability to improve agricultural production and maintain natural resources is evaluated prior to their introduction. Biophysical targeting evaluates production options as a function of the spatial and temporal variability of climate conditions, in interaction with soil, crop characteristics and agronomic management strategies. This thesis contributes to the development of a system-based methodology for biophysical targeting. Cropping system simulation and weather generator tools are interfaced to geographical information systems. Inductive use of two case studies - a green manure cover crop and reduced tillage with residue management - helped to develop the methodology. Insight is gained into the regional potential for and the soil and climate conditions under which successful introduction of these production options may be achieved. The resulting information supports regional stakeholders involved in agriculture in their analysis and discussion, negotiation and decision-making concerning where to implement production systems. This process can improve the supply of appropriate agricultural production practices that enhance production and conserve soil and water resources