A search grid for parameter optimization as a byproduct of model sensitivity analysis

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Classification of built-up areas for cartographic generalization of state map series

This diploma thesis deals with the topic of automatic classification of buildings. The main goal of this diploma thesis was to design an algorithm for the identification of building types for the purposes of cartographic generalization. For the purposes of this diploma thesis, a total of six types of development were defined with respect to different generalization of individual types on ZM 50. The first part of the proposed method is represented by an algorithm for segmenting buildings into clusters based on the use of already generalized road network and DBSCAN algorithm. The partial goal of this diploma thesis was to compare classifiers from the field of machine learning and neural networks and at the same time to compare classifiers using descriptive characteristics with classifiers using visual assessment. The resulting classifications were evaluated using data from a manually selected training set and using an algorithm comparing the resulting type of development with the type of cartographic representation used to represent the development on ZM 50. The whole method was implemented in Python using Arcpy, Scikit-learn and Tensorflow libraries. Testing took place on elements from the ZABAGED and Data50 databases. Keywords: Generalization of built-up areas, Classification, Machine learning,...Tato diplomová práce se zabývá tématem automatické klasifikace zástavby. Hlavním cílem této diplomové práce bylo navrhnout algoritmus pro identifikaci typů zástavby pro účely kartografické generalizace. Pro účely této diplomové práce bylo vymezeno celkem šest typů zástavby s ohledem na rozdílnou generalizaci jednotlivých typů na ZM 50. První část navržené metody je představována algoritmem pro segmentaci budov do shluků založeného na využití již generalizované cestní sítě a algoritmu DBSCAN. Dílčím cílem této diplomové práce bylo porovnat klasifikátory z oblasti strojového učení a neuronových sítí a zároveň porovnat klasifikátory využívající popisných charakteristik s klasifikátory využívajících vizuální posouzení. Výsledné klasifikace byly zhodnoceny pomocí dat z ručně vybrané trénovací množiny a též pomocí algoritmu porovnávající výsledný typ zástavby s typem kartografické reprezentace využité pro znázornění zástavby na ZM 50. Celá metoda byla implementována v jazyce Python zejména s využitím knihoven Arcpy, Scikit-learn a Tensorflow. Testování probíhalo nad prvky z databází ZABAGED a Data50. Klíčová slova: Generalizace zástavby, Klasifikace, Strojové učení, Neuronové sítě, ZABAGED, Data50Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Development and application of a framework for model structure evaluation in environmental modelling

In a fast developing world with an ever rising population, the pressures on our natural environment are continuously increasing, causing problems such as floods, water- and air pollution, droughts,... Insight in the driving mechanisms causing these threats is essential in order to properly mitigate these problems. During the last decades, mathematical models became an essential part of scientific research to better understand and predict natural phenomena. Notwithstanding the diversity of currently existing models and modelling frameworks, the identification of the most appropriate model structure for a given problem remains a research challenge. The latter is the main focus of this dissertation, which aims to improve current practices of model structure comparison and evaluation. This is done by making individual model decisions more transparent and explicitly testable. A diagnostic framework, focusing on a flexible and open model structure definition and specifying the requirements for future model developments, is described. Methods for model structure evaluation are documented, implemented, extended and applied on both respirometric and hydrological models. For the specific case of lumped hydrological models, the unity between apparently different models is illustrated. A schematic representation of these model structures provides a more transparent communication tool, while meeting the requirements of the diagnostic approach