Monireh Shamshiri; Mahdi Akhondzadeh Hanzaei
Abstract
Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which ...
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Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which are recognizable by remote measurements (such as using Global Positioning System) are known as earthquake ionospheric precursors. In this study, two data sets from the ionospheric Total Electron Content (TEC) derived from the GPS data processing by Bernese software were used for two studies, Ahar earthquake, East Azerbaijan (2012/08/11) and Kaki earthquake,Bushehr (2013/4/9), and the results were compared with data obtained from the global stations. Because of the nonlinear behavior of TEC changes, in order to predict and detect its changes, integration of neural network (using multilayer Perceptron (MLP)) with particle swarm optimization algorithm (PSO) was used. Particle Swarm Optimization algorithm with a performance based on the population can be effective in improving estimatedweight by artificial neural network. By analyzing the causes of ionospheric anomalies including the geomagnetic fields and solar activities and their removal from the processes, the results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms were able to have appropriate efficiency for the prediction of nonlinear time series. The output resulted from the integration of artificial neural network and PSO shows that both positive and negative anomalies occur. The anomalies before earthquakes often occur close to the epicenter of the earthquake and are visible 3 days before the Ahar earthquake and 2 to 6 days before the Kaki earthquake are.
Madjid Montazeri; Leyla Dadkhah
Volume 22, SEPEHR , July 2013, , Pages 89-91
Abstract
Dust has always been one of the most important environmental hazards and it leaves adverse environmental consequences. The present article seeks to identify and analyze dusty days’ trend in Bushehr station during the last 55 years.
In this regard, monthly and annual statistical data of dusty days ...
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Dust has always been one of the most important environmental hazards and it leaves adverse environmental consequences. The present article seeks to identify and analyze dusty days’ trend in Bushehr station during the last 55 years.
In this regard, monthly and annual statistical data of dusty days in Bushehr station between 1951 and 2005 was applied. First, normality test was performed using Ncss and homogeneity test was performed using Runs Test. After proving data abnormality, nonparametric test of Mann-Kendall was chosen.
Findings indicate that except for June, other months show an increasing trend of dusty days even in annual scale. Noteworthy, the increasing trend in cold months is more obvious than warm months of the year so that March and November with respectively 3.71 and 4.4 show an increasing trend in 99.9 percent significant level.