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Drought Indices in Panama Canal
Gutiérrez Hernández, Julián Eli ; Máca, Petr (advisor)
Panama has a warm, wet, tropical climate. Unlike countries that are farther from the equator, Panama does not experience seasons marked by changes in temperature. Instead, Panama's seasons are divided into Wet and Dry. The Dry Season generally begins around mid-December, but this may vary by as much 3 to 4 weeks. Around this time, strong northeasterly winds known as "trade winds" begin to blow and little or no rain may fall for many weeks in a row. Daytime air temperatures increase slightly to around 30-31 Celsius (86-88 Fahrenheit), but nighttime temperatures remain around 22-23 Celsius (72-73 Fahrenheit). Relative humidity drops throughout the season, reaching average values as low as 70 percent. The Wet Season usually begins around May 1, but again this may vary by 1 or 2 weeks. May is often one of the wettest months, especially in the Panama Canal area, so the transition from the very dry conditions at the end of the Dry Season to the beginning of Wet Season can be very dramatic. With the arrival of the rain, temperatures cool down a little during the day and the trade winds disappear. Relative humidity rises quickly and may hover around 90 to 100% throughout the Wet Season. Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. The presented thesis compares forecast of drought indices based on seven different models of artificial neural networks model. The analyzed drought indices are SPI and SPEI-ANN Drought forecast, and was derived for the period of 1985-2014 on Panama Canal basin; I've selected seven of sixty-one Hydro-meteorological networks, existing in the Panama Canal basin. The rainfall is 1784 mm per year. The meteorological data were obtained from the PANAMA CANAL AUTHORITY, Section of Water Resources, and Panama Canal Authority, Panama. The performance of all the models was compared using ME, MAE, RMSE, NS, and PI. The results of drought indices forecast, explained by the values of seven model performance indices, show, that in Panama Canal has problem with the drought. Even though The Panama is generally seen as a wet country, droughts can cause severe problems. Significant drought conditions are observed in the index based on precipitation and potential evaporation found in this thesis; The Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), were used to quantify drought in the Panama Canal basin, Panama Canal, at multiple time scales within the period 1985-2014. The results indicate that drought indices based on different variables show the same major drought events. Drought indices based on precipitation and potential evaporation are more variable in time while drought indices based on discharge. Spatial distribution of meteorological drought is uniform over Panama Canal.
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Elliot Wave Detection
Kaleta, Marek ; Šperka, Svatopluk (referee) ; Petřík, Patrik (advisor)
This work deals with Elliott wave detection, which are statistical tool used to describe financial makret cycles and predict market trends. The work proposes methods to detect Elliott Waves and evaluetes them. From several methods of Elliott wave detection, Committee machines of multilayer perceptrons are used. Result of this work is a program, which detect Elliott impulse waves on input signal and builds hierarchy of Elliott waves.
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Neural Network Based Edge Detection
Křepský, Jan ; Grézl, František (referee) ; Švub, Miroslav (advisor)
Utilization of artificial neural networks in digital image processing is nothing new. The aim of this work is to design and implement neural network based edge detector and learn how effective this approach is for edge detection in images and to compare these results with common detectors. In theoretical part of my work I describe some methods of image pre-processing, common approach to edge detection and their thinning and I try to introduce basics for understanding artificial neural networks theory.
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Recurrent Neural Networks in Computer Vision
Křepský, Jan ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.
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Comparison of Libraries of Artificial Neural Networks
Dohnal, Zdeněk ; Zbořil, František (referee) ; Dalecký, Štěpán (advisor)
This thesis is about comparison of libraries of artificial neural networks. Basic theory of neuron, neural networks and their learning algorithms are explained here. Multilayer perceptron, Self organizing map and Hopfield net are chosen for experiments. Criteria of comparison such as licence, community or last actualization are designed. Approximation of function, association and clustering are chosen as task for experiments. After that, there is implementation of applications using chosen libraries. At the end, result of comparison and experiment are evaluated.
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Artificial neural network for modeling electromagnetic fields in a car
Kostka, Filip ; Škvor, Zbyněk (referee) ; Raida, Zbyněk (advisor)
The project deals with artificial neural networks. After designing and debugging the test data set and the training sample set, we created a multilayer perceptron network in the Neural NetworkToolbox (NNT) of Matlab. When creating networks, we used different training algorithms and algorithms improving the generalization of the network. When creating a radial basis network, we did not use the NNT, but a specific source code in Matlab was written. Functionality of neural networks was tested on simple training and testing patterns. Realistic training data were obtained by the simulation of twelve monoconic antennas operating in the frequency range from 2 to 6 GHz. Antennas were located inside a mathematical model of Octavia II. Using CST simulations, electromagnetic fields in a car were obtained. Trained networks are described by regressive characteristics andthe mean square error of training. Algorithms improving generalization are applied on the created and trained networks. The performance of individual networks is mutually compared.
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Optimization of Multilayer Perceptron Training Parameters Using Artificial Bee Colony and Genetic Algorithm
Kartci, A.
In this paper, the momentum coefficient, learning rate, and the number of hidden neurons where the multilayer perceptron works best, are determined. The network and optimization algorithms are written in MATLAB, which was also successfully used to carry out results. To obtain the results, IRIS, mammographic_mass, and new_thyroid data sets have been used. Obtained results show that the determining effect on the neural learning process of parameters (momentum coefficient, learning rate, number of hidden neurons) are compatible with other approaches available in the literature. Both genetic algorithm (GA) and artificial bee colony (ABC) algorithm were successful on finding the values to get high performance as well as effect on performance of the population number.
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Comparison of selected classification methods for multivariate data
Stecenková, Marina ; Řezanková, Hana (advisor) ; Berka, Petr (referee)
The aim of this thesis is comparison of selected classification methods which are logistic regression (binary and multinominal), multilayer perceptron and classification trees, CHAID and CRT. The first part is reminiscent of the theoretical basis of these methods and explains the nature of parameters of the models. The next section applies the above classification methods to the six data sets and then compares the outputs of these methods. Particular emphasis is placed on the discriminatory power rating models, which a separate chapter is devoted to. Rating discriminatory power of the model is based on the overall accuracy, F-measure and size of the area under the ROC curve. The benefit of this work is not only a comparison of selected classification methods based on statistical models evaluating discriminatory power, but also an overview of the strengths and weaknesses of each method.
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