A Kriging-based approach for locating a sampling site—in the assessment of air quality

Bayraktar, Hanefi; Turalıoğlu, F. Sezer

doi:10.1007/s00477-005-0234-8

Cited by 117 publications

(78 citation statements)

References 11 publications

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“…Gaussian process regression (Kriging) is a group of geostatistical techniques to interpolate the value of a random field (e.g., the elevation, z, of the landscape as a function of the geographic location) at an unobserved location from observations of its value at nearby locations [24] and [25]. The theory behind interpolation and extrapolation by kriging was developed by the French mathematician Georges Matheron based on the Master's thesis of Daniel Gerhardus Krige, the pioneering plotter of distance-weighted average gold grades at the Witwatersrand reef complex in South Africa.…”

Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

“…The theory behind interpolation and extrapolation by kriging was developed by the French mathematician Georges Matheron based on the Master's thesis of Daniel Gerhardus Krige, the pioneering plotter of distance-weighted average gold grades at the Witwatersrand reef complex in South Africa. The English verb is to krige and the most common noun is kriging; both are often pronounced with a hard "g", following the pronunciation of the name "Krige" [24].Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully-automatic structural and parametric optimization of models [26]. GMDH is used in such fields as data mining, knowledge discovery, prediction, complex systems modeling, optimization and pattern recognition.…”

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confidence: 99%

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The Classification of the Applicable Machine Learning Methods in Robot Manipulators

Hormozi¹,

Hormozi²,

Nohooji³

2012

IJMLC

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Abstract-Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques used in robotic manipulators. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.Index Terms-Machine learning, adaptive control, repetitive control, robot manipulators. I. INTRODUCTIONMachine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data [1]. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Machine learning, like all subjects in artificial intelligence, require cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive science, data mining, adaptive control, computational neuroscience and theoretical computer science [2]. In this paper we are focused on learning algorithms for robot manipulators. II. MACHINE LEARNING ALGORITHMSMachine learning algorithms are organized into a taxonomy based on the desired outcome of the algorithm.Supervised learning generates a function that maps inputs to desired outputs. For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function for robot manipulators [1].Unsupervised learning models a set of inputs, like clustering [1].Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier in manipulators [3].Reinforcement learning learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm [1] and [3].Transduction tries to predict new outputs based on training inputs, training outputs, an...

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Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

mentioning

confidence: 99%

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The Classification of the Applicable Machine Learning Methods in Robot Manipulators

Hormozi¹,

Hormozi²,

Nohooji³

2012

IJMLC

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“…There are two main products when using SOM: (i) a low-dimensional model of the high-dimensional input space, the SOM itself; and (ii) a low-dimensional representation of high-dimensional vectors after they are mapped onto the trained SOM (17, pp. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The former is often useful in explaining patterns observed in the latter, once both are given visual form, as demonstrated in section 4.2.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…SO 4 mostly has the effect of increasing the air acidity that can lead to acid rain, whereas the role of NO 2 is more complicated as it contributes both to tropospheric ozone pollution and may have adverse effects on the cardio-respiratory system (1,2). A variety of statistical techniques can be used to study separately the spatial variation and temporal evolution of air pollutants and the associated risk assessment (e.g., [3][4][5][6][7]. The present work demonstrates a novel approach to study important characteristics of the SO 4 and NO 2 distributions in a composite space-time continuum.…”

Section: Introductionmentioning

confidence: 99%

Multi-Perspective Analysis and Spatiotemporal Mapping of Air Pollution Monitoring Data

Kolovos

Skupin

Jerrett

et al. 2010

Environ. Sci. Technol.

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Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty, leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian Maximum Entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly-averaged NO 2 and mean annual SO 4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

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Comparative spatiotemporal analysis of fine particulate matter pollution

2009

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SUMMARYThe prime focus of this work is the comparative investigation, theoretical and numerical, of spatiotemporal techniques used in air pollution studies. Space-time statistics techniques are classified on the basis of a set of criteria and the relative theoretical merits of each technique are discussed accordingly. The numerical comparison involves the applications of two representative techniques. For this purpose, the popular spatiotemporal epistemic knowledge synthesis and graphical user interface (SEKS-GUI) software of spatiotemporal statistics is used together with a dataset of PM 2.5 daily measurements obtained at monitoring stations geographically distributed over the state of North Carolina, USA. The analysis offers valuable insight concerning the choice of an appropriate spatiotemporal technique in air pollution studies.

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A Kriging-based approach for locating a sampling site—in the assessment of air quality

Cited by 117 publications

References 11 publications

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

Multi-Perspective Analysis and Spatiotemporal Mapping of Air Pollution Monitoring Data

Comparative spatiotemporal analysis of fine particulate matter pollution

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