Quality of life, big data and the power of statistics

The digital era has opened up new possibilities for data-driven research. This paper discusses big data challenges in environmental monitoring and reflects on the use of statisticalmethodsintacklingthesechallengesforimprovingthequalityoflifeincities

Non-destructive evaluation of the spatial variability of reinforced concrete structures

La variabilité spatiale est une caractéristique importante qui représente l’hétérogénéité du béton des ouvrages. La connaissance de la variabilité des propriétés d’un béton sur ouvrage représente un intérêt pour l’évaluation de l’endommagement des différentes parties d’ouvrage, ainsi que pour l’évaluation de la pertinence et de la représentativité d’essais plus locaux. Le travail repose sur des séries de mesures réalisées sur corps d’épreuve en site-test, et sur ouvrage. Les techniques de contrôle non destructif (CND) considérées sont choisits parmi les plus usuelles en bureau d’étude : vitesse ultrasonore (US), résistivité électrique, rebond et radar (GPR). En laboratoire, le site-test est constitué de cinq dalles (2*2.45*0.2 m3) en béton armé. Sur site, dans le cadre du projet national RGCU-ACDC, les mesures de CND ont été effectuées sur les piles du pont de Marly. Les données sont ensuite étudiées avec les outils de la statistique et de la géostatistique pour quantifier la variabilité spatiale du béton. L’évaluation de la variabilité des mesures à différentes échelles montre que les techniques de CND sont des outils adaptés pour estimer la variabilité du béton des ouvrages en béton armé

Expert knowledge in geostatistical inference and prediction

Geostatistics provides an efficient tool for mapping environmental variables from observations and layers of explanatory variables. The number and configuration of the observations importantly determine the accuracy of geostatistical inference and prediction. Data collection is costly, and coarse sampling may lead to large uncertainties in interpolated maps. In such case, additional information may be gathered from experts who are knowledgeable about the spatial variability of environmental variables. Statistical expert elicitation has gradually become a mature research field and has proved to be able to extract from experts reliable information to form a sound scientific database. In this thesis, expert knowledge has been elicited and incorporated in geostatistical models for inference and prediction. Various extensions to the expert elicitation literature were required to make it suitable for elicitation of spatial data. The use of expert knowledge in geostatistical research is promising, yet challenging.</p

Spatially shifting temporal points: estimating pooled within-time series variograms for scarce hydrological data

Bhowmik, A. K., & Cabral, P. (2015). Spatially shifting temporal points: estimating pooled within-time series variograms for scarce hydrological data. Hydrology and Earth System Sciences: discussions, 2015(12), 2243-2265. https://doi.org/10.5194/hessd-12-2243-2015publishersversionpublishe

Steps towards comprehensive Bayesian decision analysis in fisheries and environmental management

A typical decision problem in an environmental field includes a complex system with countless uncertain factors of both nature and human behavior. There are many stakeholders with conflicting objectives and a lot of decision alternatives, and results need to be communicated clearly to decision makers and stakeholders. Organized analysis is needed to tackle these challenges. In an ideal situation, we should analyze the objectives of every stakeholder and the responses from different parts of the ecosystem within one framework, which integrates the expertise and efforts of many different disciplines. Bayesian inference, especially the influence diagram, is a perfect tool to be used in such decision problems. The main contribution of this thesis is in developing methods for the modeling of uncertainties in environmental decision problems. The focus is on having more complete decision analyses where more uncertainties are realistically modeled. By including more stochastic variables in the analysis, the decision makers get a more realistic picture of the uncertainties involved and can account for them in the decision making. The thesis consists of five separate research articles, which all contribute to the different parts of the Bayesian decision process presented in this summary. The process is divided into four steps: (1.) building a decision model, (2.) data gathering and processing, (3.) using the model, and (4.) post analysis. The summary presents the research articles and their contributions and critically reviews the tools and methods needed in the process. The articles include a model for oil spill management, a spatial multispecies stock assessment model, a model for the stock assessment of data-poor species, a model to estimate uncertainties in environmental valuation and an influence diagram for value of information analysis. The methods used cover many aspects of the Bayesian decision process, outlining the problem, different ways to define prior distributions, utility functions, and finding maximum utility policies and value of information analysis. Hence, the tools used are diverse, too. In the models, I have used graphical Bayesian networks, numerical MCMC estimation, and Gaussian processes. In conclusion, the results found in this thesis are small but important steps toward better and more comprehensive Bayesian decision analyses in environmental and fisheries management. They show that significant uncertainties exist in many parts of the system. Another important factor was the cooperation of scientists from many different disciplines with a variety of backgrounds, which is needed in the modeling of complex environmental problems.Ympäristöalan päätösongelmiin liittyy paljon epävarmuuksia, jotka johtuvat sekä monimutkaisesta ekosysteemistä että ihmisen toiminnasta. Ongelmiin liittyy useita asianosaisia, joilla on monesti risteäviä tavoitteita, joten myös eri päätösvaihtoehtojen määrä on suuri. Päätösanalyysin tuloksien pitää olla selkeitä ja asianosaisten sekä päätöksentekijän helposti tulkittavissa. Näiden haasteiden ratkaisemiseksi tarvitaan epävarmuudet huomioivaa organisoitua lähestymistapaa. Ihanteellisessa tapauksessa päätösanalyysi voitaisiin tehdä yhdellä työkalulla, joka huomioi niin luontoon kuin ihmistoimintaankin liittyvät seikat epävarmuuksineen. Bayes-verkot, ja etenkin vaikutuskaaviot, ovat menetelmiä, jotka soveltuvat erinomaisesti juuri tämän tyylisiin päätösongelmiin. Tässä työssä kehitetään menetelmiä päätösanalyysiin liittyvien epävarmuuksien mallintamiseen. Sovelluskohteet ovat ympäristön hallinnassa ja kalataloudessa. Tavoitteena ovat entistä kattavammat päätösanalyysit, joissa epävarmuudet on huomioitu realistisesti. Kun malleihin ja analyyseihin sisällytetään enemmän epävarmoja muuttujia, päätöksentekijät saavat paremman kuvan päätöksiin liittyvistä epävarmuuksista ja voivat huomioida ne päätöksenteossa. Tämä väitöskirja koostuu viidestä eri tutkimusartikkelista, jotka liittyvät epävarmuudet huomioivan päätösanalyysiprosessin eri vaiheisiin. Prosessi, joka on kuvattu tässä tiivistelmässä, on jaettu neljään osaan: 1) päätösmallin rakentaminen, 2) tiedon keruu ja käsittely, 3) mallin käyttö ja 4) jälkianalyysi. Tutkimusartikkelien aiheina ovat: malli öljyvahinkojen hallintaan, spatiaalinen monilajimalli kalakantojen arvioimiseksi, kalakantamalli tilanteisiin, joissa kannan tilasta, kalastuksesta ja lajin biologiasta on hyvin vähän tietoa, malli jolla voidaan arvioida ympäristön arvottamiseen liittyviä epävarmuuksia sekä vaikutuskaavio informaation arvoanalyysiä varten. Osajulkaisujen aiheet liittyvät osaltaan kaikkiin eri päätösanalyysiprosessin vaiheisiin, ongelman määrittelystä erilaisiin tapoihin kerätä ja käyttää aiempaa olemassa olevaa tietoa sekä hyötyfunktioista hyödynmaksimointiin ja informaation arvoanalyysiin. Täten myös käytetyt työkalut ovat erilaisia. Mallintamisessa on käytetty graafisia Bayes-verkkoja, numeerista Markovin ketju Monte Carlo -simulointia ja Gaussisia prosesseja. Työn tulokset ovat pieniä mutta tärkeitä askeleita kohti kattavampaa epävarmuudet huomioivaa päätösanalyysiä ympäristön ja kalakantojen hallinnassa ja käytössä. Tulokset osoittavat miten huomattavia epävarmuuksia pieniinkin päätösanalyysin osiin sisältyy. Tärkeää on myös yhteistyö eri tieteenalojen välillä, mitä tarvitaan mallinnettaessa monimutkaisia luonnonprosesseja ja niihin liittyvää laajempaa päätöskokonaisuutta

Importance of the Spatial Distribution of Rare Earth Elements in the Bottom Sediments of Reservoirs as a Potential Proxy for Tracing Sediments Sources.

The geochemical composition of rare earth elements (REE) in the bottom sediments of two Do-minican reservoirs and in soils from their catchments was studied to identify possible sources of the deposited materials. Knowledge of the origin of the sediments will serve to control the ex-cessive rates of erosion and sedimentation that occur annually due to periodic extreme climatic events that promote excessive silting of the lakes, followed by loss of storage capacity and deg-radation of water quality. The REE contents of sediments and soils were normalized to the North American Shale Composite (NASC) and the ratio of light/heavy rare earths (LREE/HREE ratio), Ce and Eu anomalies, and some fractionation parameters were determined. The REE patterns are more homogeneous in the sediments, indicating uniform sedimentation in both deposits. The sediment data reflect depletion of REE from the sources, enrichment of light REE (LREE) and some middle REE (MREE), and positive Eu and Ce anomalies. All data were plotted in correlation diagrams between some fractionation parameters of light–middle–heavy REE and anomalies of Ce and Eu. The similarity of the ratios between these parameters in all samples and the overlap of data from soils and rocks on the sediment projection in the diagrams allowed a good discrimina-tion of the main sources of the materials

Soil Spatial Scaling: Modelling variability of soil properties across scales using legacy data

Understanding how soil variability changes with spatial scale is critical to our ability to understand and model soil processes at scales relevant to decision makers. This thesis uses legacy data to address the ongoing challenge of understanding soil spatial variability in a number of complementary ways. We use a range of information: precision agriculture studies; compiled point datasets; and remotely observed raster datasets. We use classical geostatistics, but introduce a new framework for comparing variability of spatial properties across scales. My thesis considers soil spatial variability from a number of geostatistical angles. We find the following: • Field scale variograms show differing variance across several magnitudes. Further work is required to ensure consistency between survey design, experimental methodology and statistical methodology if these results are to become useful for comparison. • Declustering is a useful tool to deal with the patchy design of legacy data. It is not a replacement for an evenly distributed dataset, but it does allow the use of legacy data which would otherwise have limited utility. • A framework which allows ‘roughness’ to be expressed as a continuous variable appears to fit the data better than the mono-fractal or multi-fractal framework generally associated with multi–scale modelling of soil spatial variability. • Soil appears to have a similar degree of stochasticity to short range topographic variability, and a higher degree of stochasticity at short ranges (less than 10km and 100km) than vegetation and Radiometrics respectively. • At longer ranges of variability (i.e. around 100km) only rainfall and height above sea level show distinctly different stochasticity. • Global variograms show strong isotropy, unlike the variograms for the Australian continent

Empirical correlations between rock cutting parameters and excavated rock surface rebound hardness

In field excavation, cutting tools operate on rock surfaces damaged from previous tool pass, yet, average intact rock properties are often used in field project estimations. This can result in overestimation of excavation time and cost. The ability to accurately correlate the damaged rock properties to the excavation parameters means more reliable estimates of project completion time and costs, and hence improved the application of mechanical excavation technology to a wider range of civil and mining industries. The purpose of this research was to better understand the relationship between rock cutting parameters and the excavated rock surface hardness during mechanical excavation. To do this, Roubidoux sandstone was subjected to linear cutting experiments using a radial drag pick at different cut spacing to depth of cut (s/d) ratios and the resultant forces and chips were analyzed. The rebound hardness of the excavated rock surface was subsequently measured using a rock-type Schmidt hammer. Results and subsequent analysis indicated that the wide variability of Roubidoux sandstone coupled with the complex process of rock cutting prevented a clear determination of the relationship between the cutting forces and the excavated rock surface hardness. 2D stereonet models of the resultant force orientation data and estimates of the tool path deviation indicated that the cutting tool experienced significant deflection during cutting. Finally, it was found that cutting geometry and excavated rock surface hardness contributed significantly to variations in the specific cutting energy --Abstract, page iii

Web-based tool for expert elicitation of the variogram

The variogram is the keystone of geostatistics. Estimation of the variogram is deficient and difficult when there are no or too few observations available due to budget constraints or physical and temporal obstacles. In such cases, expert knowledge can be an important source of information. Expert knowledge can also fulfil the increasing demand for an a priori variogram in Bayesian geostatistics and spatial sampling optimization. Formal expert elicitation provides a sound scientific basis to reliably and consistently extract knowledge from experts. In this study, we aimed at applying existing statistical expert elicitation techniques to extract the variogram of a regionalized variable that is assumed to have either a multivariate normal or lognormal spatial probability distribution from expert knowledge. To achieve this, we developed an elicitation protocol and implemented it as a web-based tool to facilitate the elicitation of beliefs from multiple experts. Our protocol has two main rounds: elicitation of the marginal probability distribution and elicitation of the variogram. The web-based tool has three main components: a web interface for expert elicitation and feedback; a component for statistical computation and mathematical pooling of multiple experts’ knowledge; and a database management component. Results from a test case study show that the protocol is adequate and that the online elicitation tool functions satisfactorily. The web-based tool is free to use and supports scientists to conveniently elicit the variogram of spatial random variables from experts. The source code is available from the journal FTP site under the GNU General Public License