Browsing by Subject "Earthquake prediction"

Now showing 1 - 3 of 3

Open Access
Investigation on the reliability of earthquake prediction based on ionospheric electron content variation
(ISIF, 2013-07) Akyol, Ali Alp; Arıkan, Orhan; Arıkan F.; Deviren, M. N.
Due to lack of statistical reliability analysis of earthquake precursors, earthquake prediction from ionospheric parameters is considered to be controversial. In this study, reliability of earthquake prediction is investigated using dense TEC data estimated from the Turkish National Permanent GPS Network (TNPGN- Active). © 2013 ISIF ( Intl Society of Information Fusi.
Open Access
Spatiotemporal sequence prediction with point processes and self-organizing decision trees
(Institute of Electrical and Electronics Engineers , 2023-06-01) Karaahmetoğlu, Oğuzhan; Kozat, Süleyman Serdar
We study the spatiotemporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatiotemporal prediction is extensively studied in machine learning literature due to its critical real-life applications, such as crime, earthquake, and social event prediction. Despite these thorough studies, specific problems inherent to the application domain are not yet fully explored. Here, we address the non stationary spatiotemporal prediction problem on both densely and sparsely distributed sequences. We introduce a probabilistic approach that partitions the spatial domain into subregions and models the event arrivals in each region with interacting point processes. Our algorithm can jointly learn the spatial partitioning and the interaction between these regions through a gradient-based optimization procedure. Finally, we demonstrate the performance of our algorithm on both simulated data and two real-life datasets. We compare our approach with baseline and state-of-the-art deep learning-based approaches, where we achieve significant performance improvements. Moreover, we also show the effect of using different parameters on the overall performance through empirical results and explain the procedure for choosing the parameters.
Open Access
Spatiotemporal sequence prediction with point processes and self-organizing decision trees
(Institute of Electrical and Electronics Engineers, 2021-09-22) Karaahmetoğlu, Oğuzhan; Serdar Kozat, Süleyman
We study the spatiotemporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatiotemporal prediction is extensively studied in machine learning literature due to its critical real-life applications, such as crime, earthquake, and social event prediction. Despite these thorough studies, specific problems inherent to the application domain are not yet fully explored. Here, we address the nonstationary spatiotemporal prediction problem on both densely and sparsely distributed sequences. We introduce a probabilistic approach that partitions the spatial domain into subregions and models the event arrivals in each region with interacting point processes. Our algorithm can jointly learn the spatial partitioning and the interaction between these regions through a gradient-based optimization procedure. Finally, we demonstrate the performance of our algorithm on both simulated data and two real-life datasets. We compare our approach with baseline and state-of-the-art deep learning-based approaches, where we achieve significant performance improvements. Moreover, we also show the effect of using different parameters on the overall performance through empirical results and explain the procedure for choosing the parameters.