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Self-Optimized Computational Method Calculating Robust b Values for Earthquake Catalogs Containing Small Number of Events

AUTHORS

Konstantinos Arvanitakis, Romanos Kalamatianos, Markos Avlonitis

ABSTRACT

In the Gutenberg-Richter relation that describes the frequency-magnitude distribution of earthquakes, the b value represents the distribution’s slope. Since b values can be used for mapping the dynamic response of earthquake source, methodologies for calculating robust b values are of great importance. Although nowadays software which is meant for statistical analysis of earthquake data can determine b values with high accuracy, in occasions where catalogs that contain small number of earthquake events, the produced results are not satisfactory. In this paper we present a new self-optimized algorithm for a more efficient calculation of the b value. The algorithm’s results are compared with two widely known software for statistical analysis of earthquake data, showing a better performance in evaluating b values for earthquake catalogs containing small number of events.

Microclimates and their stochastic effect on olive fruit fly evolution – Modeling and simulation

AUTHORS

Romanos Kalamatianos, Markos Avlonitis

ABSTRACT

Climate variables play an important role in the development and general activity (diffusion, oviposition etc.) of the olive fruit fly. These variables can fluctuate from area to area due to the topography of the area and several other factors. Due to this fluctuations microclimates are created. Through simulation runs we investigate how the population dynamics of the olive fruit fly are affected in four distinct microclimates, from an olive grove area in Corfu, Greece with environmental data collected from environmental sensors. Finally, we investigate how current spraying practices affect the population of the olive fruit fly in each microclimate.
Researchgate

Treating Stochasticity of Olive-Fruit Fly’s Outbreaks via Machine Learning Algorithms

AUTHORS

Romanos Kalamatianos, Katia Kermanidis, Ioannis Karydis, Markos Avlonitis

ABSTRACT

Olive fruit fly trap measurements are used as one of the indicators for olive grove infestation, and therefore, as a consultation tool on spraying parameters. In this paper, machine learning techniques are used to predict the next olive fruit fly trap measurement, given input knowledge of previous trap measurements as well as an attribute that acts as a correlation model between the temperature and the development of a pest’s population, known as the Degree Day model. This is the first time the Degree Day model is utilized as input in classification algorithms for the prediction of olive fruit fly trap measurements. Various classification algorithms are mployed and applied to different environmental settings, in extensive comparative experiments, in order to detect the impact of the latter on olive fruit fly population prediction.
Researchgate

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