Best practice report on policy learning infrastructures in innovative labour market policies

This report synthesizes the findings of 11 country reports on policy learning in labour market and social policies that were conducted as part of WP5 of the INSPIRES project, which is funded by the 7th Framework Program of the EU-Commission. Notably, this report puts forward objectives of policy learning, discusses tools, processes and institutions of policy learning and presents the impacts of various tools and structures of the policy learning infrastructure for the actual policy learning process. The report defines three objectives of policy learning: evaluation and assessment of policy effectiveness, vision building and planning, and consensus building. In the 11 countries under consideration, the tools and processes of the policy learning, infrastructure can be classified into three broad groups: public bodies, expert councils, and parties, interest groups and the private sector. Finally, we develop four recommendations for policy learning: Firstly, learning processes should keep the balance between centralisation and plurality. Secondly, learning processes should be kept stable beyond the usual political business cycles. Thirdly, policy learning tools and infrastructures should be sufficiently independent from political influence or bias. Fourth, Policy learning tools and infrastructures should balance out mere effectiveness, evaluation and vision building.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

E-learning initiatives in forensic interpretation: report on experiences from current projects and outlook

This paper reports on the purpose, design, methodology and target audience of E-learning courses in forensic interpretation offered by the authors since 2010, including practical experiences made throughout the implementation period of this project. This initiative was motivated by the fact that reporting results of forensic examinations in a logically correct and scientifically rigorous way is a daily challenge for any forensic practitioner. Indeed, interpretation of raw data and communication of findings in both written and oral statements are topics where knowledge and applied skills are needed. Although most forensic scientists hold educational records in traditional sciences, only few actually followed full courses that focussed on interpretation issues. Such courses should include foundational principles and methodology - including elements of forensic statistics - for the evaluation of forensic data in a way that is tailored to meet the needs of the criminal justice system. In order to help bridge this gap, the authors' initiative seeks to offer educational opportunities that allow practitioners to acquire knowledge and competence in the current approaches to the evaluation and interpretation of forensic findings. These cover, among other aspects, probabilistic reasoning (including Bayesian networks and other methods of forensic statistics, tools and software), case pre-assessment, skills in the oral and written communication of uncertainty, and the development of independence and self-confidence to solve practical inference problems. E-learning was chosen as a general format because it helps to form a trans-institutional online-community of practitioners from varying forensic disciplines and workfield experience such as reporting officers, (chief) scientists, forensic coordinators, but also lawyers who all can interact directly from their personal workplaces without consideration of distances, travel expenses or time schedules. In the authors' experience, the proposed learning initiative supports participants in developing their expertise and skills in forensic interpretation, but also offers an opportunity for the associated institutions and the forensic community to reinforce the development of a harmonized view with regard to interpretation across forensic disciplines, laboratories and judicial systems.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Natural variation in learning ability in "Drosophila melanogaster"

Learning is the ability of an organism to adapt to the changes of its environment in response to its past experience. It is a widespread ability in the animal kingdom, but its evolutionary aspects are poorly known. Learning ability is supposedly advantageous under some conditions, when environmental conditions are not too stable - because in this case there is no need to learn to predict any event in the environment - and not changing too fast - otherwise environmental cues cannot be used because they are not reliable. Nevertheless, learning ability is also known to be costly in terms of energy needed for neuronal synthesis, memory formation, initial mistakes. During my PhD, I focused on the study of genetic variability of learning ability in natural populations. Genetic variability is the basis on which natural selection and genetic drift can act. How does learning ability vary in nature? What are the roles of additive genetic variation or maternal effects in this variation? Is it involved in evolutionary trade-offs with other fitness-related traits?¦I investigated a natural population of fruit fly, Drosophila melanogaster, as a model organism. Its learning ability is easy to measure with associative memory tests. I used two research tools: multiple inbred and isofemale lines derived from a natural population as a representative sample. My work was divided into three parts.¦First, I investigated the effects of inbreeding on aversive learning (avoidance of an odor previously associated with mechanical shock). While the inbred lines consistently showed reduced egg-to-adult viability by 28 %, the effects of inbreeding on learning performance was 18 % and varied among assays, with a trend to be most pronounced for intermediate conditioning intensity. Variation among inbred lines indicates that ample genetic variance for learning was segregating in the base population, and suggests that the inbreeding depression observed in learning performance was mostly due to dominance rather than overdominance. Across the inbred lines, learning performance was positively correlated with the egg-to-adult viability. This positive genetic correlation contradicts previous studies which observed a trade-off between learning ability and lifespan or larval competitive ability. It suggests that much of the genetic variation for learning is due to pleiotropic effects of genes affecting other functions related to survival. Together with the overall mild effects of inbreeding on learning performance, this suggests that genetic variation specifically affecting learning is either very low, or is due to alleles with mostly additive (semi-dominant) effects. It also suggests that alleles reducing learning performance are on average partially recessive, because their effect does not appear in the outbred base population. Moreover, overdominance seems unlikely as major cause of the inbreeding depression, because even if the overall mean of the inbred line is smaller than the outbred base population, some of the inbred lines show the same learning score as the outbred base population. If overdominance played an important part in inbreeding depression, then all the homozygous lines should show lower learning ability than¦outbred base population.¦In the second part of my project, I sampled the same natural population again and derived isofemale lines (F=0.25) which are less adapted to laboratory conditions and therefore are more representative of the variance of the natural population. They also showed some genetic variability for learning, and for three other fitness-related traits possibly related with learning: resistance to bacterial infection, egg-to-adult viability and developmental time. Nevertheless, the genetic variance of learning ability did not appear to be smaller than the variance of the other traits. The positive correlation previously observed between learning ability and egg- to-adult viability did not appear in isofemale lines (nor a negative correlation). It suggests that there was still genetic variability within isofemale lines and that they did not fix the highly deleterious pleiotropic alleles possibly responsible for the previous correlation.¦In order to investigate the relative amount of nuclear (additive and non-additive effects) and extra-nuclear (maternal and paternal effect) components of variance in learning ability and other fitness-related traits among the inbred lines tested in part one, I performed a diallel cross between them. The nuclear additive genetic variance was higher than other components for learning ability and survival to learning ability, but in contrast, maternal effects were more variable than other effects for developmental traits. This suggests that maternal effects, which reflects effects from mitochondrial DNA, epigenetic effects, or the amount of nutrients that are invested by the mother in the egg, are more important in the early stage of life, and less at the adult stage. There was no additive genetic correlation between learning ability and other traits, indicating that the correlation between learning ability and egg-to-adult viability observed in the first pat of my project was mostly due to recessive genes.¦Finally, my results showed that learning ability is genetically variable. The diallel experiment showed additive genetic variance was the most important component of the total variance. Moreover, every inbred or isofemale line showed some learning ability. This suggested that alleles impairing learning ability are eliminated by selection, and therefore that learning ability is under strong selection in natural populations of Drosophila. My results cannot alone explain the maintenance of the observed genetic variation. Even if I cannot eliminate the hypothesis of pleiotropy between learning ability and the other fitness-related traits I measured, there is no evidence for any trade-off between these traits and learning ability. This contradicts what has been observed between learning ability and other traits like lifespan and larval competitivity.¦L'apprentissage représente la capacité d'un organisme à s'adapter aux changement de son environnement au cours de sa vie, en réponse à son expérience passée. C'est une capacité très répandue dans le règne animal, y compris pour les animaux les plus petits et les plus simples, mais les aspects évolutifs de l'apprentissage sont encore mal connus. L'apprentissage est supposé avantageux dans certaines conditions, quand l'environnement n'est ni trop stable - dans ce cas, il n'y a rien à apprendre - ni trop variable - dans ce cas, les indices sur lesquels se reposer changent trop vite pour apprendre. D'un autre côté, l'apprentissage a aussi des coûts, en terme de synthèse neuronale, pour la formation de la mémoire, ou de coûts d'erreur initiale d'apprentissage. Pendant ma thèse, j'ai étudié la variabilité génétique naturelle des capacités d'apprentissage. Comment varient les capacités d'apprentissage dans la nature ? Quelle est la part de variation additive, l'impact des effets maternel ? Est-ce que l'apprentissage est impliqué dans des interactions, de type compromis évolutifs, avec d'autres traits liés à la fitness ?¦Afin de répondre à ces questions, je me suis intéressée à la mouche du vinaigre, ou drosophile, un organisme modèle. Ses capacités d'apprentissage sont facile à étudier avec un test de mémoire reposant sur l'association entre un choc mécanique et une odeur. Pour étudier ses capacités naturelles, j'ai dérivé de types de lignées d'une population naturelle: des lignées consanguines et des lignées isofemelles.¦Dans une première partie, je me suis intéressée aux effets de la consanguinité sur les capacités d'apprentissage, qui sont peu connues. Alors que les lignées consanguines ont montré une réduction de 28% de leur viabilité (proportion d'adultes émergeants d'un nombre d'oeufs donnés), leurs capacités d'apprentissage n'ont été réduites que de 18%, la plus forte diminution étant obtenue pour un conditionnement modéré. En outre, j'ai également observé que les capacités d'apprentissage était positivement corrélée à la viabilité entre les lignées. Cette corrélation est surprenante car elle est en contradiction avec les résultats obtenus par d'autres études, qui montrent l'existence de compromis évolutifs entre les capacités d'apprentissage et d'autres traits comme le vieillissement ou la compétitivité larvaire. Elle suggère que la variation génétique des capacités d'apprentissage est due aux effets pleiotropes de gènes récessifs affectant d'autres fonctions liées à la survie. Ces résultats indiquent que la variation pour les capacités d'apprentissage est réduite comparée à celle d'autres traits ou est due à des allèles principalement récessifs. L'hypothèse de superdominance semble peu vraisemblable, car certaines des lignées consanguines ont obtenu des scores d'apprentissage égaux à ceux de la population non consanguine, alors qu'en cas de superdominance, elles auraient toutes dû obtenir des scores inférieurs.¦Dans la deuxième partie de mon projet, j'ai mesuré les capacités d'apprentissage de lignées isofemelles issues de la même population initiale que les lignées consanguines. Ces lignées sont issues chacune d'un seul couple, ce qui leur donne un taux d'hétérozygosité supérieur et évite l'élimination de lignées par fixation d'allèles délétères rares. Elles sont ainsi plus représentatives de la variabilité naturelle. Leur variabilité génétique est significative pour les capacités d'apprentissage, et trois traits liés à la fois à la fitness et à l'apprentissage: la viabilité, la résistance à l'infection bactérienne et la vitesse de développement. Cependant, la variabilité des capacités d'apprentissage n'apparaît cette fois pas inférieure à celle des autres traits et aucune corrélation n'est constatée entre les capacité d'apprentissage et les autres traits. Ceci suggère que la corrélation observée auparavant était surtout due à la fixation d'allèles récessifs délétères également responsables de la dépression de consanguinité.¦Durant la troisième partie de mon projet, je me suis penchée sur la décomposition de la variance observée entre les lignées consanguines observée en partie 1. Quatre composants ont été examinés: la variance due à des effets nucléaires (additifs et non additifs), et due à des effets parentaux (maternels et paternels). J'ai réalisé un croisement diallèle de toutes les lignées. La variance additive nucléaire s'est révélée supérieure aux autres composants pour les capacités d'apprentissage et la résistance à l'infection bactérienne. Par contre, les effets maternels étaient plus importants que les autres composants pour les traits développementaux (viabilité et vitesse de développement). Ceci suggère que les effets maternels, dus à G ADN mitochondrial, à l'épistasie ou à la quantité de nutriments investis dans l'oeuf par la mère, sont plus importants dans les premiers stades de développement et que leur effet s'estompe à l'âge adulte. Il n'y a en revanche pas de corrélation statistiquement significative entre les effets additifs des capacités d'apprentissage et des autres traits, ce qui indique encore une fois que la corrélation observée entre les capacités d'apprentissage et la viabilité dans la première partie du projet était due à des effets d'allèles partiellement récessifs.¦Au, final, mes résultats montrent bien l'existence d'une variabilité génétique pour les capacités d'apprentissage, et l'expérience du diallèle montre que la variance additive de cette capacité est importante, ce qui permet une réponse à la sélection naturelle. Toutes les lignées, consanguines ou isofemelles, ont obtenu des scores d'apprentissage supérieurs à zéro. Ceci suggère que les allèles supprimant les capacités d'apprentissage sont fortement contre-sélectionnés dans la nature Néanmoins, mes résultats ne peuvent pas expliquer le maintien de cette variabilité génétique par eux-même. Même si l'hypothèse de pléiotropie entre les capacités d'apprentissage et l'un des traits liés à la fitness que j'ai mesuré ne peut être éliminée, il n'y a aucune preuve d'un compromis évolutif pouvant contribuer au maintien de la variabilité.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.