# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 2 Related Studies  
Many methods, analyses, and results have been used for prediction. Some of these results approached the goal and while others did not. After the literature analysis, the studies that most closely approached the goal were selected and are listed in Table 1.  
In a 2006 study, [PERSON] was one of the first to propose the use of artificial neural networks for earthquake prediction [11]. The author, [PERSON], was inspired by the successful application of similar approaches to financial forecasting tasks, which are similar to seismic activity in their chaotic nature. He tested this method on seismic data from the Azores region, Portugal. [PERSON] scientifically stated that he predicted the earthquakes in July 1998 (MMI = 8) and January 2004 (MMI = 5) correctly. However, it was not assessed using any statistical measurement. Therefore, the performance of this approach is yet to be evaluated objectively. In the studies by [PERSON] and [PERSON] in 2007 and in 2009, the problem of earthquake prediction was organized in terms of output classes with the largest seismic magnitude ranges in a predefined time series [12]. They used this dataset to estimate the magnitude of the largest earthquake in a predefined region for the next month. They prepared eight \begin{table}
\begin{tabular}{c c c} \hline \hline
**Article** & **Authors** & **Date** \\ \hline Earthquake Forecasting Using Neural & & \\ Networks: Results and Future Work. & [PERSON] [11] & 2006 \\ \hline Neural Network Models for Earthquake & & \\ Magnitude Prediction Using Multiple & [PERSON] and [PERSON] [12] & 2007 \\ Seismicity Indicators. & & \\ \hline A Probabilistic Neural Network for & & \\ Earthquake Magnitude Prediction. & [PERSON] and [PERSON] [13] & 2009 \\ \hline Artificial Neural Networks for Earthquake & & \\ Prediction Using Time Series Magnitude & & \\ Data or Seismic Electric Signals. & [PERSON] [14] & 2011 \\ \hline Yapay Sinir Agi Yüntemiyle Deprem & & \\ Tahmini: Turkiye Bath Anadolu Fay Hatt & & \\ Uygulamas. & & \\ \hline Katamoun ve Yakun Cevresi Için Deprem & & \\ Olasilig Talhminlieri & & \\ \hline Natural Time Analysis of Global Seismicity. & & \\  & & \\ \hline Forecasting Earthquakes: The RELM Test. & [PERSON], [PERSON], [PERSON] and [PERSON] [18] & 2012 \\ \hline Estimation of the Size of Earthquake & [PERSON], [PERSON] and & \\ Preparation Zones. & [PERSON] [19] & 1979 \\ \hline SafeNet: SwArm for Earthquake & & \\ Perturbations Identification Using Deep & & \\ Learning Networks. & & \\ \hline Possible Earthquake Forecasting In a & & \\ Narrow Space-Time-Magnitude Window. & [PERSON], [PERSON], [PERSON], & \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review table. mathematically calculated \"seismicity indicators\" that can be used to evaluate the seismic potential of a region [13]. The authors proposed the architecture of a probabilistic neural network (PNN) for prediction using the formulas they created. The model has been tested on data for the Southern California seismic region (33.8-35.4\({}^{\circ}\) N and 114.75-119.25\({}^{\circ}\) W) and has been proven to provide good prediction accuracy for earthquakes of magnitude 4.5-6.0. However, the PNN did not perform satisfactorily for earthquakes greater than magnitude 6.0. [PERSON] et al. evaluated the accuracy of the neural network for earthquake prediction using different inputs [14]. 1. The first case study focuses on estimating the next day's biggest seismic event using only time series earthquake magnitude data;
2. The latter study focuses on the use of seismic electrical signals (SES) to predict the magnitude of the next seismic event. For the first case, a feedforward backpropagation neural network is used. An input file contains the maximum size value for each day. The accuracy rate resulting from the model was stated as 58.02%. In the second case, ANN was used to generate the lost data using magnitude time series. The accuracy of the size estimation was stated to be just over 60% in the initial dataset.