Detection precursor of sumatra earthquake based on ionospheric total electron content anomalies using N-Model Articial Neural Network

LSTM-Based Deep Learning Methods for Prediction of Earthquakes Using Ionospheric Data

“…As mentioned, the machine learning algorithms are performed on ionospheric data to extract the relationship between earthquakes and them [30,31]. The research [32] was conducted to review the correlation between earthquakes and ionospheric magnetic field disturbances that supervised machine learning methods are used to identify active seismic areas from the magnetic field in the ionosphere.…”

Deep Learning of Detecting Ionospheric Precursors Associated With M ≥ 6.0 Earthquakes in Taiwan

“…The ROC (receiver operating characteristic) analysis reveals that the neural network forecast can predict aftershock locations better than the classic Coulomb failure stress change. In addition, Aji et al (2017) applied the N-Model Neural Network Model by using the values of the TEC and Dst index at midnight (00:00 LT) to detect TEC earthquake precursors in Sumatra from December 2004 to March 2005. Akhoondzadeh Hanzaei (2018) used a Multi-Layer Perceptron (MLP) neural network to estimate TEC variations induced by the 2017 Mexico earthquake.…”

“…In addition, Aji et al. (2017) applied the N‐Model Neural Network Model by using the values of the TEC and Dst index at midnight (00:00 LT) to detect TEC earthquake precursors in Sumatra from December 2004 to March 2005. Akhoondzadeh Hanzaei (2018) used a Multi‐Layer Perceptron (MLP) neural network to estimate TEC variations induced by the 2017 Mexico earthquake.…”

Natural Disaster Application on Big Data and Machine Learning: A Review

“…Citation Long-term risk assessment and reduction [12], [13], [14], [15] [16] Forecasting and Predicting [17], [18], [19], [20], [21] [27], [28], [29], [30], [31], [32], [33], [ 34], [35], [36] Early warning Damage [47], [48], [49], [50][51] [52][53][54 ] Damage Assessment [55], [15], [56], [15], [55], [56][57][5 8] Post-disaster Coordination and Response [59], [60][61][62] [63] Based on the results of table 2, part of the research is often done in the area of monitoring and detection. The monitoring and detection section is a component that is usually researched because based on the results of the paper to obtain data sources in the process of monitoring and detection is straightforward.…”

“…Also, the model that is often used is ANN (artificial neural network). The study used ANN for earthquake early detection, also known as precursor [27]. Also, ANN was also used to predict earthquakes magnitude in Tokyo [20].…”