13,492 research outputs found
Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas
Parts of Texas, Oklahoma, and Kansas have experienced increased rates of
seismicity in recent years, providing new datasets of earthquake recordings to
develop ground motion prediction models for this particular region of the
Central and Eastern North America (CENA). This paper outlines a framework for
using Artificial Neural Networks (ANNs) to develop attenuation models from the
ground motion recordings in this region. While attenuation models exist for the
CENA, concerns over the increased rate of seismicity in this region necessitate
investigation of ground motions prediction models particular to these states.
To do so, an ANN-based framework is proposed to predict peak ground
acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake
source-to-site distance, and shear wave velocity. In this framework,
approximately 4,500 ground motions with magnitude greater than 3.0 recorded in
these three states (Texas, Oklahoma, and Kansas) since 2005 are considered.
Results from this study suggest that existing ground motion prediction models
developed for CENA do not accurately predict the ground motion intensity
measures for earthquakes in this region, especially for those with low
source-to-site distances or on very soft soil conditions. The proposed ANN
models provide much more accurate prediction of the ground motion intensity
measures at all distances and magnitudes. The proposed ANN models are also
converted to relatively simple mathematical equations so that engineers can
easily use them to predict the ground motion intensity measures for future
events. Finally, through a sensitivity analysis, the contributions of the
predictive parameters to the prediction of the considered intensity measures
are investigated.Comment: 5th Geotechnical Earthquake Engineering and Soil Dynamics Conference,
Austin, TX, USA, June 10-13. (2018
Modelling and trading the Greek stock market with gene expression and genetic programing algorithms
This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter
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