Active Probability Backpropagation Neural Network Model for Monthly Prediction of Probabilistic Seismic Hazard Analysis in Taiwan

Abstract

In this study, an active probability backpropagation neural network model (PBNNM) was built by training a backpropagation neural network (BPNN) to predict the probability distribution of the probabilistic seismic hazard analysis (PSHA) monthly. The four-layered BPNN framework was determined using training data that were obtained from an earthquake catalogue for the time period of 1990-2015 (Taiwan Standard Time, TST). The studied region was divided into 500 small grids, each 0.2 • × 0.2 • in size, which is approximately 20 × 20 km 2. Each grid was assigned a predicted earthquake occurrence probability for a month between 2015 and 2018 by the PBNNM. The PBNNM successfully predicted the Tainan earthquake (2 February 2016 TST) and Hualien earthquake (2 February 2018 TST) with probabilities of 94% and 95%, respectively. A quantitative analysis of the reliability of the PBNNM, the standard error of the mean (SEM), and the normalised mean square error (NMSE) were used as statistical approaches to evaluate predicted probability errors of the PBNNM. The SEM (1.79) and NMSE (1.45) of 2015 were the inside test and within the time period of the training data. The SEM (2.11) and NMSE (2.03) of 2016, the low SEM (2.17) of 2017, the NMSE (2.13) and SEM (2.17) of 2017, and the SEM (1.71) and NMSE (1.32) of 2018 were the outside tests and not within the time period of the training data. These low SEM and NMSE values confirmed the accuracy of the PBNNM. In addition, the PBNNM does not consider the prerequisite conditions of past studies (e.g., the return period and assumed probability density model). Therefore, the PBNNM can be commercialised with relatively low cost and minimal resources and equipment compared with the methods presented in previous studies; only earthquake catalogues would be necessary. INDEX TERMS Active probability backpropagation neural network model (PBNNM), probabilistic seismic hazard analysis (PSHA), standard error of the mean (SEM), normalized mean square error (NMSE).

The Possibility of Real-Time and Long-Term Prediction for Geomagnetic Storms Using Neural Network

“…A four-layered BPNN with two hidden layers was employed based on the error backpropagation (EBP) algorithm (Omatu et al 2018;Lin and Chiou 2019), which is a technology of artificial intelligence (AI), shown in Figure 5. The Levenberg-Marquardt algorithm (LMA) was used as the EBP algorithm because it is a more commonly used and well-established method than other EBP algorithms for use in general nonlinear problems (Lera and Pinzolas, 2002;Mammadli, 2017).…”

Generalized Two-Dimensional Principal Component Analysis and Two Artificial Neural Networks to Detect Traveling Ionospheric Disturbances Using the Tsunami-Atmosphere-Ionosphere Coupling Mechanism

“…5), where J = K = 10. Some researchers have indicated that a neural network with two hidden layers and few neurons can replace a network with numerous neurons in one hidden layer (Lin et al, 2018;Lin and Chiou, 2019;Chu et al, 2020). The training data were used to train the BPNN.…”

“…(2) For the first BPNN model, all the first principal eigenvalues of the B2DPCA (Fig. 3a), with random noise added in the range of 0-0.5 (Chen et al, 2013) as target outputs, and their corresponding grids forming a 20 × 30 input matrix of positions 1) in the studies of Lin et al (2018) and Lin & Chiou (2019) as the training inputs to form the training data. The largest principal eigenvalues were considered to indicate the TIDs.…”

“…Two BPNN models were constructed to predict the magnitudes of all of the principal eigenvalues of the B2DPCA to determine whether the largest first and second principal eigenvalues were associated with the TIDs. Detailed explanations of BPNNs are available in the publications of Lin et al (2018) and Lin and Chiou (2019). The BPNN models were constructed as follows:…”

Linking the Random Forests Model and GIS to Assess Geo-Hazards Risk: A Case Study in Shifang County, China

“…In addition, the development of artificial intelligence (AI) technology offers more possibilities for scientific assessments of geo-hazards risks. Numerous machine learning models, such as decision tree (DT) [23], support vector machine (SVM) [24], artificial neural network (ANN) [25], BP-artificial neural network (BP-ANN) [26], and Bayesian network (BN) models [27], have been applied for geo-hazards risk assessments. Among them, the SVM model, which is an efficient and reliable AI algorithm, has a very strong nonlinear processing ability and is one of the significant methods in risk assessment [28].…”