# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 2 Related Studies
[PERSON] and [PERSON], in their 2016 study, \"Earthquake Prediction with Artificial Neural Network Method: Turkey West Anatolian Fault Line Application\", used a generalized method to predict in advance, with certainty, the location and time of earthquakes that have not yet been discovered [15]. In this study, a feedforward backpropagation artificial neural network based on the b value of the Gutenberg-Richter relationship was developed for performing the predictions. The artificial neural network was trained using earthquake data from four regions with intense seismic activity in western Turkey. After the training phase, earthquake data from later dates for the same regions were used for testing, and the network's success was demonstrated. In his 2011 study, titled \"Earthquake Probability Estimates for Kostamonu and Its Surroundings\", [PERSON] examined the city of Kastamonu, which is located in the first-degree earthquake zone [16]. The seismicity of Kastamonu was investigated using earthquake data with a magnitude of M \(\geq\) 4.0 that occurred between 1900 and 2011, which fell within the region drawn 150 km from the city center. Most earthquakes occurred on the North Anatolian Fault Zone, the Dodurga fault, the Eldivan-Elmadag tectonic wedge, the Merzifon fault, and the Tasova-Corum fault zone in the south of Kastamonu. This study used earthquake data with M \(\geq\) 4.0, which occurred in circular areas drawn to surround the Kastamonu city center for 50, 100, and 150 km and accepted as seismotectonic zones. It aims to find the a and b parameters in the Gutenberg-Richter magnitude-frequency relation for each region. Using these parameters and the Poisson model, it is possible to predict the probability of earthquakes of different magnitudes and their return periods. The earthquake probabilities of each region are calculated for earthquakes in 10, 20, 30, 40, 50, 75, and 100 years and with magnitudes of 5.0, 5.5, 6.0, 6.5, 7.0, and 7.5. [PERSON] et al. [17] investigated the \"Natural time\" of seismicity and identified an order parameter for the seismic events. Upon examining the earthquakes in the recent years, significant progress has been observed in the natural time analysis of seismicity. These advancements encompass the identification of distinct minima of the x1 order parameter in seismicity on both regional and global scales, the emergence of a correlation between the time correlations of earthquake magnitude time series and these minima, and the introduction of EQ nowcasting by [PERSON], [PERSON], and their colleagues [18]. The researchers have implemented these recent advancements in the analysis of global seismicity using the Global Centroid Moment Tensor (GCMT) catalog. The findings demonstrate that the combined effect of these three milestones can furnish valuable preliminary information regarding the timing and epicenter location of powerful earthquakes with magnitudes of M \(\geq\) 8.5 in the GCMT. The results exhibit notable statistical significance (with \(p\) valueson the order of \(10^{-5}\) while the epicentral regions comprise only 4% of the investigated area) [18]. In their study, [PERSON] et al. investigated numerous ionospheric disturbances that occur as a result of significant earthquake activity observed with the SwArm satellite. This study utilized the SafeNet deep learning framework. The system was trained using earthquake data from the period of 2014 to 2020, specifically focusing on earthquakes with magnitudes of 4.8 or greater. The findings revealed that nighttime data, collected within a circular region centered at the epicenter utilizing a radius and input window size of 70 consecutive data points as defined by [PERSON] [19], exhibited the highest performance in effectively identifying pre-earthquake perturbations [20]. In 2021, [PERSON] et al. studied earthquake prediction in a limited period, wherein an extended time series of Schumann Resonance records were analyzed using two multi-parametric statistical methods.