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°x 0.2° in size, which is approximately 20 × 20 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . 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.
Highlights
Taiwan is located at the junction where the Eurasian Plate collides with the Philippine Sea plate
From the predicted results calculated using the active probability backpropagation neural network model (PBNNM) given in Figs. 5 to 16, no earthquakes occurred during some months
Its depth was 5.44 km, and it had Richter magnitude (ML) of 5.27. It occurred in a grid with a high probability of 94% predicted using the active PBNNM (Table 1 and Fig. 8)
Summary
Taiwan is located at the junction where the Eurasian Plate collides with the Philippine Sea plate. It has a structure typical of a continental margin island arising from a plate collision. Many large and small earthquakes occur in Taiwan. The Philippine Sea plate moves from the southeast to the northwest relative to the Eurasian plate, at the rate of 7 cm y−1 – 8 cm y−1. The slip rate controls the seismic activity in Taiwan. The plate’s lateral extrusion creates high stress in the geological structures of Taiwan.
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