Técnicas inteligentes para el análisis de condiciones medioambientales

[ES] Como es bien sabido, la calidad del aire es un tema importante y preocupante en la actualidad que afecta no solamente a la salud humana sino a otros muchos aspectos como el cambio climático o la supervivencia de la biosfera. En los últimos años, numerosas entidades públicas se han ido adaptando a las restrictivas medidas de contaminación ambiental impuestas por las diversas normativas europeas, siendo España uno de los países obligados a cumplir estas normativas. Tanto en España como en otros países existen diversas redes de monitorización de la calidad del aire y de adquisición de valores meteorológicos de una forma continua. Estas redes de estaciones de medida no sólo están presentes en las grandes ciudades sino también en zonas periféricas, polígonos industriales y en zonas donde la preservación de la naturaleza es fundamental. Además, están sometidas a constantes procesos de reordenación para mejorar su función. En la presente Tesis Doctoral se han aplicado diversas técnicas inteligentes (Soft Computing más específicamente) a conjuntos de datos públicos con información meteorológica y/o de calidad del aire. Las técnicas aplicadas llevan a cabo fundamentalmente dos tareas: reducción de la dimensionalidad y agrupamiento (clustering). Estas se han aplicado de forma aislada y de forma combinada para mejorar los resultados obtenidos en el análisis de la información medioambiental. Las técnicas de reducción de la dimensionalidad aplicadas son: Principal Component Analysis (PCA) como técnica aplicada en primer lugar para obtener una primera aproximación a la estructura del conjunto de datos, Locally Linear Embedding (LLE) como técnica no lineal local, Maximum Likelihood Hebbian Learning (MLHL) y Cooperative Maximum Likelihood Hebbian Learning (CMLHL) como modelos neuronales que implementan Exploratory Projection Pursuit, Curvilinear Component Analysis (CCA) como modelo no lineal que intenta preservar la distancia entre los puntos en la salida, Multidimensional Scalling (MDS) como técnica global no lineal basada en la matriz de distancias, Isometric Mapping (ISOMAP) como técnica derivada de MDS y los Self-Organizing Maps (SOM), un importante modelo neuronal que implementa aprendizaje competitivo. Las técnicas de agrupamiento aplicadas han sido por una lado particionales: k-means como primer método a aplicar en agrupamiento y que busca la asignación de muestras a grupos aplicando métricas de distancia, SOM k-means que utiliza los algoritmos de SOM para la actualización de los pesos, k-medoids como técnica derivada de k-means y que asigna el centroide de cada grupo a uno de los puntos del mismo y fuzzy c-means, técnica que aplica lógica difusa para tareas de agrupamiento. Por otro lado, también se ha empleado el método aglomerativo jerárquico en el que se van formando los grupos de forma ascendente, junto con diversos métodos de evaluación de agrupamiento que sirven para determinar el posible número de grupos existentes en un conjunto de datos y dendrogramas para obtener una representación gráfica de la agrupación de los datos en forma de árbol. Los casos de estudio han sido cuidadosamente seleccionados y se extienden desde el ámbito local, regional hasta el nacional. Por otra parte, también se ha dado importancia a los periodos de tiempo seleccionados. En alguno de los estudios se analizan periodos de tiempo tan cortos como un día para el análisis de la meteorología/calidad del aire en un breve periodo de tiempo en un lugar determinado, mientras que en otros se emplean ventanas temporales próximas a una década y en los puntos más representativos climatológicamente en España. Partiendo de uno o más conjuntos de datos públicos con la información más completa posible acerca de las condiciones medioambientales (meteorológica, de calidad del aire o ambas), pero siempre analizando variables determinantes en la caracterización de las condiciones medioambientales, el objetivo es extraer la información fundamental almacenada en los conjuntos de datos mediante las técnicas inteligentes. De esta forma es posible analizar las condiciones medioambientales en los casos de estudio seleccionados. En cada uno de los casos de estudio se hace un análisis de la situación meteorológica o de calidad del aire en las localizaciones y periodos seleccionados, buscando semejanzas y diferencias en las muestras de datos analizadas y haciendo énfasis en aquellas situaciones anómalas detectadas y tratando de dar explicación a las mismas. También se hace un análisis comparativo de los resultados obtenidos con las distintas técnicas empleadas, planteando las ventajas e inconvenientes del uso de cada uno de ellas en cada caso de estudio. Las técnicas de reducción de la dimensionalidad resultan de gran utilidad para analizar gráficamente conjuntos de datos multidimensionales, encontrar relaciones en los datos y detectar situaciones anómalas. De manera complementaria, las técnicas de agrupamiento revelan la estructura de un conjunto de datos asignando las muestras de datos a los distintos grupos en función de las medidas de distancias y similitud aplicadas. Esto resulta de gran utilidad en el presente trabajo para entender las semejanzas y diferencias en la meteorología y/o calidad del aire de los distintos puntos seleccionados en cada caso de estudio. [EN] It is well known that air quality is an important and worrying issue nowadays, affecting not only human health but also many other aspects such as climate change or the survival of the biosphere. In recent years, many public institutions have been adapted to the restrictive normative about environmental pollution imposed by European regulations, being Spain one of the countries that must comply with these regulations. Both in Spain and in other countries there are various air-quality networks and stations for the continuous acquisition of meteorological parameters. These networks are not only present in big cities, but also in peripheral and industrial areas, as well as in places where the preservation of nature is fundamental key issue. Furthermore, they are constantly rearranged to improve their function. In present PhD Thesis, different intelligent techniques (more specifically, Soft Computing techniques) have been applied to publicly available databases with air quality and/or meteorological information. The applied techniques perform two fundamental tasks: dimensionality reduction and clustering. They have been applied in isolation and in conjunction in order to improve the results in the analysis of environmental conditions. The applied dimensionality reductions techniques are: Principal Component Analysis (PCA) as the technique firstly applied to obtain an approximation to the dataset structure, Locally Linear Embedding (LLE) as a non-linear local technique, Maximum Likelihood Hebbian Learning (MLHL) and Cooperative Maximum Likelihood Hebbian Learning (CMLHL) as neural models which implement Exploratory Projection Pursuit, Curvilinear Component Analysis (CCA) as a non-linear technique which tries to preserve the interpoint distance in the output space, Multidimensional Scalling (MDS) as a non-linear global technique operating with the distance matrix, Isometric Mapping (ISOMAP) as a technique derived from MDS and Self-Organizing Maps (SOM), as a competitive learning neural model. The applied clustering techniques are, on the one hand partitional techniques: k-means as the clustering technique firstly applied, which assigns samples to groups using distance metrics, SOM k-means which use the SOM algorithm for the weight updating process, k-medoids as a k-means derived technique which assigns the centroid of each cluster to one of the belonging samples, and fuzzy c-means as a fuzzy-logic based technique for grouping samples. On the other hand, hierarchical agglomerative techniques have also been applied (where groups are formed in an ascending way) together with different clustering evaluation indexes, used to determine the possible number of existing groups in a dataset, and finally dendrograms for a tree-form graphical representation of clustering. Case studies have been carefully selected and range from local, regional to national contexts. Similarly, the selected periods of time have also been a priority. In some of the studies, the analyzed period of time is one day long, considered for the analysis of meteorological / air quality in a short time interval in a certain place, while in other cases, long periods of time (close to a decade), are used to analyze some of the most climatological representative places in Spain. From one or more public datasets comprising all the information about environmental conditions (weather, air quality, or both), but always analyzing key variables in the characterization of environmental conditions, the goal is to extract the meaningfully information in the datasets by applying intelligent techniques. This leads to an analysis of the environmental conditions in the selected case studies. In each case study, an analysis of the weather or air quality conditions is carried out in the selected places and periods of time, searching for similarities and differences in the analyzed data samples, emphasizing those detected anomalous situations and trying to give an explanation to these phenomena’s. A comparative analysis of the results obtained with the different techniques applied is also performed, considering the advantages and disadvantages of using each of them in each case study Dimensionality reduction techniques are useful for graphically analyzing high-dimensional data sets, find relationships in datasets and detect anomalous situations. Complementarily, clustering techniques reveal the structure of datasets by assigning the data samples to different clusters depending on the applied distance and similarity measures. This is useful in present work to understand the similarities and differences in the meteorological and / or air quality conditions of the different locations selected in each case study

A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions

The combined application of several soft-computing and statistical techniques is proposed for the characterization of atmospheric conditions in two European regions: Madrid (Spain) and Prague (Czech Republic). The resulting Hybrid Artificial Intelligence System (HAIS) combines projection models for dimensionality reduction and clustering, combining neural and fuzzy paradigms, in a decision support tool. In present article, this proposed HAIS is applied to analyse the air quality in these two geographical regions and get a better understanding of its circumstances and evolution. To do so, real-life data from six data-acquisition stations are analysed. The main pollutants recorded at these stations between 2007 and 2014, their geographical locations and seasonal changes are all studied, in a research that shows how such factors determine variations in air-borne pollutants. Furthermore, neural projections of the clustering results from data on atmospheric pollution are studied

Self-Organizing Maps to Validate Anti-Pollution Policies

This study presents the application of self-organizing maps to air-quality data in order to analyze episodes of high pollution in Madrid (Spain’s capital city). The goal of this work is to explore the dataset and then compare several scenarios with similar atmospheric conditions (periods of high Nitrogen dioxide concentration): some of them when no actions were taken and some when traffic restrictions were imposed. The levels of main pollutants, recorded at these stations for eleven days at four different times from 2015 to 2018, are analyzed in order to determine the effectiveness of the anti-pollution measures. The visualization of trajectories on the self-organizing map let us clearly see the evolution of pollution levels and consequently evaluate the effectiveness of the taken measures, after and during the protocol activation time

Soft computing models to analyze atmospheric pollution issues

Multidisciplinary research into statistical and soft computing models is detailed that analyses data on emissions of atmospheric pollution in urban areas. The research analyses the impact on atmospheric pollution of an extended bank holiday weekend in Spain. Levels of atmospheric pollution are classified in relation to the days of the week, seeking to differentiate between working days and non-working days by taking account of such aspects as industrial activity and traffic levels. The case study is based on data collected by a station at the city of Burgos, which forms part of the pollution measurement station network within the Spanish Autonomous Region of Castile-Leon

A Climatologycal Analysis by Means of Soft Computing Models

This research analyzes the meteorological conditions of four different places in Spain. The case study is based on real data provided by the AEMET (Meteorological Spanish Agency) in 2009. Thirteen variables with atmospheric conditions are considered. Different Statistical and Soft Computing Models are applied to show the great variability of the environmental conditions in the four well selected places. The results are confirmed by the Annual Environmental Summarized 2009 provided by the AEMET

Soft computing models to identify typical meteorological days

Soft computing models are capable of identifying patterns that can characterize a ‘typical day’ in terms of its meteorological conditions. This multidisciplinary study examines data on six meteorological parameters gathered in a Spanish city. Data on these and other variables were collected for over 6 months, in 2007, from a pollution measurement station that forms part of a network of similar stations in the Spanish Autonomous Region of Castile–Leon. A comparison of the meteorological data allows relationships to be established between the meteorological variables and the days of the year. One of the main contributions of this study is the selection of appropriate data processing techniques, in order to identify typical days by analysing meteorological variables and aerosol pollutants. Two case studies are analysed in an attempt to identify a typical day in summer and in autumn

Humidity forecasting in a potato plantation using time-series neural models

It is widely acknowledged that, under the frame of sustainable farming, using the minimum water resources is a relevant requirement. In order to do that, precision irrigation aims at identifying the irrigation needs of plantations and irrigate accordingly. Artificial intelligence is a promising solution in this field as intelligent models are able to learn the soil moisture dynamics in the soil-plant-atmosphere system and then generating appropriate irrigation scheduling. This is a complex task as the phenology of plants and its water demand vary with soil properties and weather conditions. The present research contributes to this challenging task by proposing the application of neural networks in order to learn the time-series evolution of irrigation needs associated to a potato plantation. Several of such models are thoroughly compared, together with different interpolation methods, in order to find the best combination for accurately forecasting water needs. In order to predict the soil water content in a potato field crop, in which soil humidity probes were installed at 15, 30, and 45 cm depth during the whole cycle of a potato crop. This innovative study and its promising results provide with significant contributions to address the problem of predicting and managing groundwater for agricultural use in a sustainable way.Lab-Ferrer (METER Group) and the UBUCOMP research group at the University of Burgos

Soft Computing Techniques Applied to a Case Study of Air Quality in Industrial Areas in the Czech Republic

This multidisciplinary research analyzes the atmospheric pollution conditions of two different places in Czech Republic. The case study is based on real data provided by the Czech Hydrometeorological Institute along the period between 2006 and 2010. Seven variables with atmospheric pollution information are considered. Different Soft Computing models are applied to reduce the dimensionality of this data set and show the variability of the atmospheric pollution conditions among the two places selected, as well as the significant variability of the air quality along the time

COVID-19 Severity and Survival over Time in Patients with Hematologic Malignancies: A Population-Based Registry Study

Mortality rates for COVID-19 have declined over time in the general population, but data in patients with hematologic malignancies are contradictory. We identified independent prognostic factors for COVID-19 severity and survival in unvaccinated patients with hematologic malignancies, compared mortality rates over time and versus non-cancer inpatients, and investigated post COVID-19 condition. Data were analyzed from 1166 consecutive, eligible patients with hematologic malignancies from the population-based HEMATO-MADRID registry, Spain, with COVID-19 prior to vaccination roll-out, stratified into early (February–June 2020; n = 769 (66%)) and later (July 2020–February 2021; n = 397 (34%)) cohorts. Propensity-score matched non-cancer patients were identified from the SEMI-COVID registry. A lower proportion of patients were hospitalized in the later waves (54.2%) compared to the earlier (88.6%), OR 0.15, 95%CI 0.11–0.20. The proportion of hospitalized patients admitted to the ICU was higher in the later cohort (103/215, 47.9%) compared with the early cohort (170/681, 25.0%, 2.77; 2.01–3.82). The reduced 30-day mortality between early and later cohorts of non-cancer inpatients (29.6% vs. 12.6%, OR 0.34; 0.22–0.53) was not paralleled in inpatients with hematologic malignancies (32.3% vs. 34.8%, OR 1.12; 0.81–1.5). Among evaluable patients, 27.3% had post COVID-19 condition. These findings will help inform evidence-based preventive and therapeutic strategies for patients with hematologic malignancies and COVID-19 diagnosis.Depto. de MedicinaFac. de MedicinaTRUEFundación Madrileña de Hematología y HemoterapiaFundación Leucemia y LinfomaAsociación Madrileña de Hematología y Hemoterapiapu

Atlas de las praderas marinas de España

Knowledge of the distribution and extent of seagrass habitats is currently the basis of management and conservation policies of the coastal zones in most European countries. This basic information is being requested through European directives for the establishment of monitoring programmes and the implementation of specific actions to preserve the marine environment. In addition, this information is crucial for the quantification of the ecological importance usually attributed to seagrass habitats due to, for instance, their involvement in biogeochemical cycles, marine biodiversity and quality of coastal waters or global carbon budgets. The seagrass atlas of Spain represents a huge collective effort performed by 84 authors across 30 Spanish institutions largely involved in the scientific research, management and conservation of seagrass habitats during the last three decades. They have contributed to the availability of the most precise and realistic seagrass maps for each region of the Spanish coast which have been integrated in a GIS to obtain the distribution and area of each seagrass species. Most of this information has independently originated at a regional level by regional governments, universities and public research organisations, which explain the elevated heterogeneity in criteria, scales, methods and objectives of the available information. On this basis, seagrass habitats in Spain occupy a total surface of 1,541,63 km2, 89% of which is concentrated in the Mediterranean regions; the rest is present in sheltered estuarine areas of the Atlantic peninsular regions and in the open coastal waters of the Canary Islands, which represents 50% of the Atlantic meadows. Of this surface, 71.5% corresponds to Posidonia oceanica, 19.5% to Cymodocea nodosa, 3.1% to Zostera noltii (=Nanozostera noltii), 0.3% to Zostera marina and 1.2% to Halophila decipiens. Species distribution maps are presented (including Ruppia spp.), together with maps of the main impacts and pressures that has affected or threatened their conservation status, as well as the management tools established for their protection and conservation. Despite this considerable effort, and the fact that Spain has mapped wide shelf areas, the information available is still incomplete and with weak precision in many regions, which will require an investment of major effort in the near future to complete the whole picture and respond to demands of EU directives