Showing papers by "National Center for Research on Earthquake Engineering published in 2022"

Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

Abstract: This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.

Early Peak Ground Acceleration Prediction for On-Site Earthquake Early Warning Using LSTM Neural Network

Abstract: On-site earthquake early warning techniques, which issue alerts based on seismic waves measured at a single station, are promising, and have performed quite successfully during some damaging earthquakes. Conventionally, most existing techniques extract several P-wave features from the first few seconds of seismic waves after the trigger to predict the intensity or destructiveness of an incoming earthquake. This type of technique neglects the behavior of temporal varying features within P waves. In other words, the characteristics of data sequences are not considered. In this study, a long short-term memory (LSTM) neural network, which is capable of learning order dependence in seismic waves, is employed to predict the peak ground acceleration (PGA) of the coming earthquake. A dense LSTM architecture is proposed and a large data set of earthquakes is used to train the LSTM model. The general performance of the LSTM model indicated that the predicted PGA values are quite promising but are generally overestimated. However, the predicted PGA of the Chi-Chi earthquake data set, whose fault rupture is complex and long, using the proposed LSTM model is more accurate than the PGA predicted in a previous study using a support vector regression approach. In addition, an alternative alert criterion, which issues alerts when the predicted PGA exceeds the threshold in successive time windows, is presented, and the performance of the proposed LSTM model when different PGA thresholds are considered is also discussed.

The large deep-seated landslide induced by the March 12th, 2012 rainfall event in the city of Azazga, Northern Algeria: deformation characteristics and failure mechanisms

Abstract: A rainfall-induced deep large landslide occurred on March 12, 2012, in the city of Azazga (Northern Algeria), causing heavy damages to residential buildings and public infrastructures. In this work, we investigate the inventory mapping, the deformation characteristics, the controlling factors, and the failure mechanisms of this landslide based on the field investigation, aerial photographs and satellite images interpretation, deep boreholes equipped with piezometers and inclinometers, as well as subsurface geophysical imaging by electric resistivity tomography (ERT). The obtained landslide inventory map shows that the landslide of March 2012 affected an area of 0.40 km2. This landslide is considered as a partial reactivation of a large pre-existing one (0.606 km2), which represents 6.65% of the total urban area. Moreover, the analyses identified two types of causative factors: (1) the triggering factor related to the high intensity and antecedent rainfall as well as human activity through slope excavations and embankments; and (2) the susceptibility factors related to the lithological nature and the internal structure of the flysch deposits, their weak mechanical resistance characteristics, the presence of shallow aquifers, and basal undercutting erosion of the Iazoughen and Aboud river torrents. The inclinometer measurements and ERT imaging reveal a complex, deep-seated and rapidly moving landslide whose failure surfaces are located at a depth of 11–29 m with an average velocity of 1–29 cm year−1. The entire slip surface is located along the geotechnical interface between the flysch bedrock and the overlying scree. This comprehensive study provides useful information on rainfall-induced landslides and may constitute guidance for landslide hazard mitigation and prevention.