Predicting origins of passerines migrating through Canadian migration monitoring stations using stable-hydrogen isotope analyses of feathers: a new tool for bird conservation

The Canadian Migration Monitoring Network (CMMN) consists of standardized observation and migration count stations located largely along Canada’s southern border. A major purpose of CMMN is to detect population trends of migratory passerines that breed primarily in the boreal forest and are otherwise poorly monitored by the North American Breeding Bird Survey (BBS). A primary limitation of this approach to monitoring is that it is currently not clear which geographic regions of the boreal forest are represented by the trends generated for each bird species at each station or group of stations. Such information on “catchment areas” for CMMN will greatly enhance their value in contributing to understanding causes of population trends, as well as facilitating joint trend analysis for stations with similar catchments. It is now well established that naturally occurring concentrations of deuterium in feathers grown in North America can provide information on their approximate geographic origins, especially latitude. We used stable hydrogen isotope analyses of feathers (δ²Hf) from 15 species intercepted at 22 CMMN stations to assign approximate origins to populations moving through stations or groups of stations. We further constrained the potential catchment areas using prior information on potential longitudinal origins based upon bird migration trajectories predicted from band recovery data and known breeding distributions. We detected several cases of differences in catchment area of species passing through sites, and between seasons within species. We discuss the importance of our findings, and future directions for using this approach to assist conservation of migratory birds at continental scales.

Using chemical and physical parameters to define the quality of Pardo River water (Botucatu-SP-Brazil)

This paper aims to study the ecological system of the Pardo River, at the source and lower-order passages, which are in the Botucatu area, São Paulo State, Brazil. This study was carried out to determine water quality with some chemical-physical indicators, coliforms, and chemical species of samples taken monthly, 1995/02-1996/01, from eight sampling stations sited along the Pardo River. The results in the river monitoring are discussed based on annual averages, analysis of variance, and compared to Tukey's Studentized Range-HSD, and principal component analysis (PCA) was applied to normalize data to assess association between variables. We can conclude that the variables used are very efficient for identifying and that the dry season shows the worst water quality. These were caused by organic matter, nutrients (originate) from anthropogenic sources (spatial sources) and mainly municipal wastewater, affecting the quality and hydrochemistry of the river water, which have been differentiated and assigned to polluting sources. Meanwhile, the degree of degradation of the Pardo River is low (sewage treatment carried out by the city of Pardinho is efficient), leaving the water of the river suitable for use by the population of Botucatu, after conventional treatment (Conama, Resolucao No. 20, CONAMA, Brazilia DF, 09-23, 1986-the water of the Pardo river is classified as level 03). (C) 2001 Elsevier B.V. Ltd. All rights reserved.

Macroinvertebrate abundance and proportion of trait categories in river biotopes across England and Wales

There is a long tradition of river monitoring using macroinvertebrate communities to assess environmental quality in Europe. A promising alternative is the use of species life-history traits. Both methods, however, have relied on the time-consuming identification of taxa. River biotopes, 1-100 m**2 'habitats' with associated species assemblages, have long been seen as a useful and meaningful way of linking the ecology of macroinvertebrates and river hydro-morphology and can be used to assess hydro-morphological degradation in rivers. Taxonomic differences, however, between different rivers had prevented a general test of this concept until now. The species trait approach may overcome this obstacle across broad geographical areas, using biotopes as the hydro-morphological units which have characteristic species trait assemblages. We collected macroinvertebrate data from 512 discrete patches, comprising 13 river biotopes, from seven rivers in England and Wales. The aim was to test whether river biotopes were better predictors of macroinvertebrate trait profiles than taxonomic composition (genera, families, orders) in rivers, independently of the phylogenetic effects and catchment scale characteristics (i.e. hydrology, geography and land cover). We also tested whether species richness and diversity were better related to biotopes than to rivers. River biotopes explained 40% of the variance in macroinvertebrate trait profiles across the rivers, largely independently of catchment characteristics. There was a strong phylogenetic signature, however. River biotopes were about 50% better at predicting macroinvertebrate trait profiles than taxonomic composition across rivers, no matter which taxonomic resolution was used. River biotopes were better than river identity at explaining the variability in taxonomic richness and diversity (40% and <=10%, respectively). Detailed trait-biotope associations agreed with independent a priori predictions relating trait categories to near river bed flows. Hence, species traits provided a much needed mechanistic understanding and predictive ability across a broad geographical area. We show that integration of the multiple biological trait approach with river biotopes at the interface between ecology and hydro-morphology provides a wealth of new information and potential applications for river science and management.

On-Chip Thermal Monitoring: Design, Placement and Interconnection of Temperature Sensors

La temperatura es una preocupación que juega un papel protagonista en el diseño de circuitos integrados modernos. El importante aumento de las densidades de potencia que conllevan las últimas generaciones tecnológicas ha producido la aparición de gradientes térmicos y puntos calientes durante el funcionamiento normal de los chips. La temperatura tiene un impacto negativo en varios parámetros del circuito integrado como el retardo de las puertas, los gastos de disipación de calor, la fiabilidad, el consumo de energía, etc. Con el fin de luchar contra estos efectos nocivos, la técnicas de gestión dinámica de la temperatura (DTM) adaptan el comportamiento del chip en función en la información que proporciona un sistema de monitorización que mide en tiempo de ejecución la información térmica de la superficie del dado. El campo de la monitorización de la temperatura en el chip ha llamado la atención de la comunidad científica en los últimos años y es el objeto de estudio de esta tesis. Esta tesis aborda la temática de control de la temperatura en el chip desde diferentes perspectivas y niveles, ofreciendo soluciones a algunos de los temas más importantes. Los niveles físico y circuital se cubren con el diseño y la caracterización de dos nuevos sensores de temperatura especialmente diseñados para los propósitos de las técnicas DTM. El primer sensor está basado en un mecanismo que obtiene un pulso de anchura variable dependiente de la relación de las corrientes de fuga con la temperatura. De manera resumida, se carga un nodo del circuito y posteriormente se deja flotando de tal manera que se descarga a través de las corrientes de fugas de un transistor; el tiempo de descarga del nodo es la anchura del pulso. Dado que la anchura del pulso muestra una dependencia exponencial con la temperatura, la conversión a una palabra digital se realiza por medio de un contador logarítmico que realiza tanto la conversión tiempo a digital como la linealización de la salida. La estructura resultante de esta combinación de elementos se implementa en una tecnología de 0,35 _m. El sensor ocupa un área muy reducida, 10.250 nm2, y consume muy poca energía, 1.05-65.5nW a 5 muestras/s, estas cifras superaron todos los trabajos previos en el momento en que se publicó por primera vez y en el momento de la publicación de esta tesis, superan a todas las implementaciones anteriores fabricadas en el mismo nodo tecnológico. En cuanto a la precisión, el sensor ofrece una buena linealidad, incluso sin calibrar; se obtiene un error 3_ de 1,97oC, adecuado para tratar con las aplicaciones de DTM. Como se ha explicado, el sensor es completamente compatible con los procesos de fabricación CMOS, este hecho, junto con sus valores reducidos de área y consumo, lo hacen especialmente adecuado para la integración en un sistema de monitorización de DTM con un conjunto de monitores empotrados distribuidos a través del chip. Las crecientes incertidumbres de proceso asociadas a los últimos nodos tecnológicos comprometen las características de linealidad de nuestra primera propuesta de sensor. Con el objetivo de superar estos problemas, proponemos una nueva técnica para obtener la temperatura. La nueva técnica también está basada en las dependencias térmicas de las corrientes de fuga que se utilizan para descargar un nodo flotante. La novedad es que ahora la medida viene dada por el cociente de dos medidas diferentes, en una de las cuales se altera una característica del transistor de descarga |la tensión de puerta. Este cociente resulta ser muy robusto frente a variaciones de proceso y, además, la linealidad obtenida cumple ampliamente los requisitos impuestos por las políticas DTM |error 3_ de 1,17oC considerando variaciones del proceso y calibrando en dos puntos. La implementación de la parte sensora de esta nueva técnica implica varias consideraciones de diseño, tales como la generación de una referencia de tensión independiente de variaciones de proceso, que se analizan en profundidad en la tesis. Para la conversión tiempo-a-digital, se emplea la misma estructura de digitalización que en el primer sensor. Para la implementación física de la parte de digitalización, se ha construido una biblioteca de células estándar completamente nueva orientada a la reducción de área y consumo. El sensor resultante de la unión de todos los bloques se caracteriza por una energía por muestra ultra baja (48-640 pJ) y un área diminuta de 0,0016 mm2, esta cifra mejora todos los trabajos previos. Para probar esta afirmación, se realiza una comparación exhaustiva con más de 40 propuestas de sensores en la literatura científica. Subiendo el nivel de abstracción al sistema, la tercera contribución se centra en el modelado de un sistema de monitorización que consiste de un conjunto de sensores distribuidos por la superficie del chip. Todos los trabajos anteriores de la literatura tienen como objetivo maximizar la precisión del sistema con el mínimo número de monitores. Como novedad, en nuestra propuesta se introducen nuevos parámetros de calidad aparte del número de sensores, también se considera el consumo de energía, la frecuencia de muestreo, los costes de interconexión y la posibilidad de elegir diferentes tipos de monitores. El modelo se introduce en un algoritmo de recocido simulado que recibe la información térmica de un sistema, sus propiedades físicas, limitaciones de área, potencia e interconexión y una colección de tipos de monitor; el algoritmo proporciona el tipo seleccionado de monitor, el número de monitores, su posición y la velocidad de muestreo _optima. Para probar la validez del algoritmo, se presentan varios casos de estudio para el procesador Alpha 21364 considerando distintas restricciones. En comparación con otros trabajos previos en la literatura, el modelo que aquí se presenta es el más completo. Finalmente, la última contribución se dirige al nivel de red, partiendo de un conjunto de monitores de temperatura de posiciones conocidas, nos concentramos en resolver el problema de la conexión de los sensores de una forma eficiente en área y consumo. Nuestra primera propuesta en este campo es la introducción de un nuevo nivel en la jerarquía de interconexión, el nivel de trillado (o threshing en inglés), entre los monitores y los buses tradicionales de periféricos. En este nuevo nivel se aplica selectividad de datos para reducir la cantidad de información que se envía al controlador central. La idea detrás de este nuevo nivel es que en este tipo de redes la mayoría de los datos es inútil, porque desde el punto de vista del controlador sólo una pequeña cantidad de datos |normalmente sólo los valores extremos| es de interés. Para cubrir el nuevo nivel, proponemos una red de monitorización mono-conexión que se basa en un esquema de señalización en el dominio de tiempo. Este esquema reduce significativamente tanto la actividad de conmutación sobre la conexión como el consumo de energía de la red. Otra ventaja de este esquema es que los datos de los monitores llegan directamente ordenados al controlador. Si este tipo de señalización se aplica a sensores que realizan conversión tiempo-a-digital, se puede obtener compartición de recursos de digitalización tanto en tiempo como en espacio, lo que supone un importante ahorro de área y consumo. Finalmente, se presentan dos prototipos de sistemas de monitorización completos que de manera significativa superan la características de trabajos anteriores en términos de área y, especialmente, consumo de energía. Abstract Temperature is a first class design concern in modern integrated circuits. The important increase in power densities associated to recent technology evolutions has lead to the apparition of thermal gradients and hot spots during run time operation. Temperature impacts several circuit parameters such as speed, cooling budgets, reliability, power consumption, etc. In order to fight against these negative effects, dynamic thermal management (DTM) techniques adapt the behavior of the chip relying on the information of a monitoring system that provides run-time thermal information of the die surface. The field of on-chip temperature monitoring has drawn the attention of the scientific community in the recent years and is the object of study of this thesis. This thesis approaches the matter of on-chip temperature monitoring from different perspectives and levels, providing solutions to some of the most important issues. The physical and circuital levels are covered with the design and characterization of two novel temperature sensors specially tailored for DTM purposes. The first sensor is based upon a mechanism that obtains a pulse with a varying width based on the variations of the leakage currents on the temperature. In a nutshell, a circuit node is charged and subsequently left floating so that it discharges away through the subthreshold currents of a transistor; the time the node takes to discharge is the width of the pulse. Since the width of the pulse displays an exponential dependence on the temperature, the conversion into a digital word is realized by means of a logarithmic counter that performs both the timeto- digital conversion and the linearization of the output. The structure resulting from this combination of elements is implemented in a 0.35_m technology and is characterized by very reduced area, 10250 nm2, and power consumption, 1.05-65.5 nW at 5 samples/s, these figures outperformed all previous works by the time it was first published and still, by the time of the publication of this thesis, they outnumber all previous implementations in the same technology node. Concerning the accuracy, the sensor exhibits good linearity, even without calibration it displays a 3_ error of 1.97oC, appropriate to deal with DTM applications. As explained, the sensor is completely compatible with standard CMOS processes, this fact, along with its tiny area and power overhead, makes it specially suitable for the integration in a DTM monitoring system with a collection of on-chip monitors distributed across the chip. The exacerbated process fluctuations carried along with recent technology nodes jeop-ardize the linearity characteristics of the first sensor. In order to overcome these problems, a new temperature inferring technique is proposed. In this case, we also rely on the thermal dependencies of leakage currents that are used to discharge a floating node, but now, the result comes from the ratio of two different measures, in one of which we alter a characteristic of the discharging transistor |the gate voltage. This ratio proves to be very robust against process variations and displays a more than suficient linearity on the temperature |1.17oC 3_ error considering process variations and performing two-point calibration. The implementation of the sensing part based on this new technique implies several issues, such as the generation of process variations independent voltage reference, that are analyzed in depth in the thesis. In order to perform the time-to-digital conversion, we employ the same digitization structure the former sensor used. A completely new standard cell library targeting low area and power overhead is built from scratch to implement the digitization part. Putting all the pieces together, we achieve a complete sensor system that is characterized by ultra low energy per conversion of 48-640pJ and area of 0.0016mm2, this figure outperforms all previous works. To prove this statement, we perform a thorough comparison with over 40 works from the scientific literature. Moving up to the system level, the third contribution is centered on the modeling of a monitoring system consisting of set of thermal sensors distributed across the chip. All previous works from the literature target maximizing the accuracy of the system with the minimum number of monitors. In contrast, we introduce new metrics of quality apart form just the number of sensors; we consider the power consumption, the sampling frequency, the possibility to consider different types of monitors and the interconnection costs. The model is introduced in a simulated annealing algorithm that receives the thermal information of a system, its physical properties, area, power and interconnection constraints and a collection of monitor types; the algorithm yields the selected type of monitor, the number of monitors, their position and the optimum sampling rate. We test the algorithm with the Alpha 21364 processor under several constraint configurations to prove its validity. When compared to other previous works in the literature, the modeling presented here is the most complete. Finally, the last contribution targets the networking level, given an allocated set of temperature monitors, we focused on solving the problem of connecting them in an efficient way from the area and power perspectives. Our first proposal in this area is the introduction of a new interconnection hierarchy level, the threshing level, in between the monitors and the traditional peripheral buses that applies data selectivity to reduce the amount of information that is sent to the central controller. The idea behind this new level is that in this kind of networks most data are useless because from the controller viewpoint just a small amount of data |normally extreme values| is of interest. To cover the new interconnection level, we propose a single-wire monitoring network based on a time-domain signaling scheme that significantly reduces both the switching activity over the wire and the power consumption of the network. This scheme codes the information in the time domain and allows a straightforward obtention of an ordered list of values from the maximum to the minimum. If the scheme is applied to monitors that employ TDC, digitization resource sharing is achieved, producing an important saving in area and power consumption. Two prototypes of complete monitoring systems are presented, they significantly overcome previous works in terms of area and, specially, power consumption.

Surface water pesticide monitoring of Illinois streams : summary of results 1985-1998 /

This report provides a review and summarization of stream pesticide data collected by the Illinois EPA Ambient Water Quality Monitoring Network between October 1985 and December 1998. A list of sampling stations is provided in Appendix A along with data from the seven most detected pesticides which are provided in Appendix B.

VERIFYING SUCCESS OF ARTIFICIAL REEFS IN THE HURON-ERIE CORRIDOR FOR LAKE STURGEON

Lake sturgeon (Acipenser fulvescens) were historically abundant in the Huron-Erie Corridor (HEC), a 160 km river/channel network composed of the St. Clair River, Lake St. Clair, and the Detroit River that connects Lake Huron to Lake Erie. In the HEC, most natural lake sturgeon spawning substrates have been eliminated or degraded as a result of channelization and dredging. To address significant habitat loss in HEC, multi-agency restoration efforts are underway to restore spawning substrate by constructing artificial spawning reefs. The main objective of this study was to conduct post-construction monitoring of lake sturgeon egg deposition and larval emergence near two of these artificial reef projects; Fighting Island Reef in the Detroit River, and Middle Channel Spawning Reef in the lower St. Clair River. We also investigated seasonal and nightly timing of larval emergence, growth, and vertical distribution in the water column at these sites, and an additional site in the St. Clair River where lake sturgeon are known to spawn on a bed of ~100 year old coal clinkers. From 2010-12, we collected viable eggs and larvae at all three sites indicating that these artificial reefs are creating conditions suitable for egg deposition, fertilization, incubation, and larval emergence. The construction methods and materials, and physical site conditions present in HEC artificial reef projects can be used to inform future spawning habitat restoration or enhancement efforts. The results from this study have also identified the likelihood of additional uncharacterized natural spawning sites in the St. Clair River. In addition to the field study, we conducted a laboratory experiment involving actual substrate materials that have been used in artificial reef construction in this system. Although coal clinkers are chemically inert, some trace elements can be reincorporated with the clinker material during the combustion process. Since lake sturgeon eggs and larvae are developing in close proximity to this material, it is important to measure the concentration of potentially toxic trace elements. This study focused on arsenic, which occurs naturally in coal and can be toxic to fishes. Total arsenic concentration was measured in samples taken from four substrate treatments submerged in distilled water; limestone cobble, rinsed limestone cobble, coal clinker, and rinsed coal clinker. Samples were taken at three time intervals: 24 hours, 11 days, and 21 days. ICP-MS analysis showed that concentrations of total arsenic were below the EPA drinking water standard (10 ppb) for all samples. However, at the 24 hour sampling interval, a two way repeated measures ANOVA with a Holm-Sidak post hoc analysis (α= 0.05) showed that the mean arsenic concentration was significantly higher in the coal clinker substrate treatment then in the rinsed coal clinker treatment (p=0.006), the limestone cobble treatment (p

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Plataforma remota de gestão de um parque de GateBoxes

A monitorização de redes é um aspeto de elevada importância, principalmente em redes de média ou grande dimensão. A necessidade de utilização de uma ferramenta para realização dessa gestão facilita o trabalho e proporciona de uma forma mais rápida e eficaz a identificação de problemas na rede e nos seus sistemas. Neste sentido, o presente trabalho tem como objetivo o desenvolvimento de uma solução para a monitorização de GateBoxes, um dos produtos desenvolvidos e comercializados pela empresa NextToYou. A necessidade de monitorização das GateBoxes, por parte da NextToYou, é essencial para que possa detetar falhas no seu funcionamento ou realizar notificações aquando da deteção de problemas para uma rápida resolução. Neste contexto a empresa decidiu implementar uma ferramenta para a referida monitorização e propôs, no âmbito da tese, o desenvolvimento de uma aplicação que satisfizesse esses propósitos. Disponibilizou então, para o desenvolvimento uma plataforma, a WebForge, e definiu alguns requisitos funcionais dessa ferramenta, tais como, a monitorização remota de informação, gestão de alarmes, geração de avisos e notificações. Para a elaboração deste trabalho foram realizados estudos teóricos sobre o tema da gestão e monitorização remotas, realizando-se posteriormente o desenvolvimento de uma aplicação para a monitorização de GateBoxes. Após a implementação efetuou-se a validação do trabalho realizado através da execução de testes e demonstrações, de forma a poder validar e verificar o desempenho do sistema.

Spatial-temporal modellization of the NO2 concentration data through geostatistical tools

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO2 levels, by using geostatisti- cal approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This method- ology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

Recent plant diversity changes on Europe's mountain summits.

In mountainous regions, climate warming is expected to shift species' ranges to higher altitudes. Evidence for such shifts is still mostly from revisitations of historical sites. We present recent (2001 to 2008) changes in vascular plant species richness observed in a standardized monitoring network across Europe's major mountain ranges. Species have moved upslope on average. However, these shifts had opposite effects on the summit floras' species richness in boreal-temperate mountain regions (+3.9 species on average) and Mediterranean mountain regions (-1.4 species), probably because recent climatic trends have decreased the availability of water in the European south. Because Mediterranean mountains are particularly rich in endemic species, a continuation of these trends might shrink the European mountain flora, despite an average increase in summit species richness across the region.

The multipoint Morisita index for the analysis of spatial patterns

In many fields, the spatial clustering of sampled data points has many consequences. Therefore, several indices have been proposed to assess the level of clustering affecting datasets (e.g. the Morisita index, Ripley's Kfunction and Rényi's generalized entropy). The classical Morisita index measures how many times it is more likely to select two measurement points from the same quadrats (the data set is covered by a regular grid of changing size) than it would be in the case of a random distribution generated from a Poisson process. The multipoint version (k-Morisita) takes into account k points with k >= 2. The present research deals with a new development of the k-Morisita index for (1) monitoring network characterization and for (2) detection of patterns in monitored phenomena. From a theoretical perspective, a connection between the k-Morisita index and multifractality has also been found and highlighted on a mathematical multifractal set.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

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.

2

The quality of environmental data analysis and propagation of errors are heavily affected by the representativity of the initial sampling design [CRE 93, DEU 97, KAN 04a, LEN 06, MUL07]. Geostatistical methods such as kriging are related to field samples, whose spatial distribution is crucial for the correct detection of the phenomena. Literature about the design of environmental monitoring networks (MN) is widespread and several interesting books have recently been published [GRU 06, LEN 06, MUL 07] in order to clarify the basic principles of spatial sampling design (monitoring networks optimization) based on Support Vector Machines was proposed. Nonetheless, modelers often receive real data coming from environmental monitoring networks that suffer from problems of non-homogenity (clustering). Clustering can be related to the preferential sampling or to the impossibility of reaching certain regions.