An Earthquake Prediction Algorithm for the Pamir and Tien Shan Region Based on a Combination of Long-Range Aftershocks and Quiescent Periods

2013 ◽  
pp. 31-35 ◽  
Author(s):  
A. G. Prozorov
Keyword(s):  
Long Range ◽  
Earthquake Prediction ◽  
Tien Shan ◽  
Prediction Algorithm

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

scholarly journals Long-range terrestrial laser scanning measurements of annual and intra-annual mass balances for Urumqi Glacier No. 1, eastern Tien Shan, China

2019 ◽  
Vol 13 (9) ◽  
pp. 2361-2383 ◽  
Author(s):  
Chunhai Xu ◽  
Zhongqin Li ◽  
Huilin Li ◽  
Feiteng Wang ◽  
Ping Zhou
Keyword(s):  
Mass Balance ◽  
Long Range ◽  
Tien Shan ◽  
Glacier Mass Balance ◽  
Surface Mass Balance ◽  
Mass Balances ◽  
Surface Mass ◽  
Mass Conversion ◽  
Geodetic Mass Balance

Abstract. The direct glaciological method provides in situ observations of annual or seasonal surface mass balance, but can only be implemented through a succession of intensive in situ measurements of field networks of stakes and snow pits. This has contributed to glacier surface mass-balance measurements being sparse and often discontinuous in the Tien Shan. Nevertheless, long-term glacier mass-balance measurements are the basis for understanding climate–glacier interactions and projecting future water availability for glacierized catchments in the Tien Shan. Riegl VZ®-6000 long-range terrestrial laser scanner (TLS), typically using class 3B laser beams, is exceptionally well suited for repeated glacier mapping, and thus determination of annual and seasonal geodetic mass balance. This paper introduces the applied TLS for monitoring summer and annual surface elevation and geodetic mass changes of Urumqi Glacier No. 1 as well as delineating accurate glacier boundaries for 2 consecutive mass-balance years (2015–2017), and discusses the potential of such technology in glaciological applications. Three-dimensional changes of ice and firn–snow bodies and the corresponding densities were considered for the volume-to-mass conversion. The glacier showed pronounced thinning and mass loss for the four investigated periods; glacier-wide geodetic mass balance in the mass-balance year 2015–2016 was slightly more negative than in 2016–2017. Statistical comparison shows that agreement between the glaciological and geodetic mass balances can be considered satisfactory, indicating that the TLS system yields accurate results and has the potential to monitor remote and inaccessible glacier areas where no glaciological measurements are available as the vertical velocity component of the glacier is negligible. For wide applications of the TLS in glaciology, we should use stable scan positions and in-situ-measured densities of snow–firn to establish volume-to-mass conversion.

Testing an earthquake prediction algorithm

1997 ◽  
Vol 149 (1) ◽  
pp. 219-232 ◽  
Author(s):  
Vladimir G. Kossobokov ◽  
John H. Healy ◽  
James W. Dewey
Keyword(s):  
Earthquake Prediction ◽  
Prediction Algorithm

scholarly journals A proposal of regionalization for the application of the CN earthquake prediction algorithm to the Italian territory

1999 ◽  
Vol 42 (5) ◽  
Author(s):  
A. Peresan ◽  
G. Costa ◽  
G. F. Panza
Keyword(s):  
Earthquake Prediction ◽  
Kinematic Model ◽  
Spatial Uncertainty ◽  
Prediction Algorithm ◽  
Tool Selection ◽  
Strong Earthquakes ◽  
The North ◽  
Central Regions ◽  
Cn Algorithm ◽  
Italian Territory

A regionalization of the Italian territory, strictly based on seismotectonic zoning and the main geodynamic features of the Italian area, is proposed for intermediate-term earthquake prediction with CN algorithm. Three regions, composed of adjacent zones with the same seismogenic behaviour or with transitional properties, are selected for the north, centre and south of Italy, compatibly with the kinematic model. This regionalization allows us an average reduction of the spatial uncertainty of about 35% for the northern and central regions, and of about 70% for the southern region in comparison with previous studies. A general reduction of the percentage of total TIPs, with respect to the results obtained neglecting the seismotectonic zoning, has been observed as well. Therefore, it seems that the seismotectonic model is a useful tool selection of the fault systems involved in the preparation of strong earthquakes. The successful attempt of catalogue upgrading, accomplished using the NEIC Preliminary Determinations of Epicentres, appears to substantiate the robustness of the algorithm against changes in the catalogue.

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