scholarly journals Short-term earthquake forecast

2021 ◽  
Vol 3 (1) ◽  
pp. 92-99
Author(s):  
Vladislav Voleysho
Keyword(s):  
Electromagnetic Field ◽  
Geographical Location ◽  
Earthquake Source ◽  
Source Zone ◽  
Seismogenic Structure ◽  
Short Term ◽  
Earthquake Forecast ◽  
Strong Earthquakes ◽  
Term Prediction ◽  
Short Term Prediction

In the manuscript, a tectonomagnetic model of forming the source zone of a strong earthquake is presented from the position of the electromagnetic field of Earth. The model is based on the idea of magnetic interaction between geological blocks screening, when the bond to each other by adhesion, a flux of abyssal fluids with the formation of a seismogenic structure. The source zone of strong earthquakes formed inside the seismogenic structure is followed by the development of an anomalous electromagnetic field. The existence of the deterministic cause-and-effect relationship between anomalous electromagnetic field inside the formed earthquake source and a change in atmospheric pressure determines the possibilities of conducting short-term prediction of time, place, and force of the earthquake. Registration of the earthquake source zone by barometric method during hydrogeodynamic monitoring makes it possible to make short-term predictions of it by time, place, and force. The substantiation and examples are given for short-term prediction of time, geographical location, and force of strong earthquakes in basic seismically active regions of Russia.

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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