scholarly journals Effect of tectonic setting on the fit and performance of a long-range earthquake forecasting model

2012 ◽  
Vol 2 (1) ◽  
pp. 3
Author(s):  
David Alan Rhoades ◽  
Paul G. Somerville ◽  
Felipe Dimer de Oliveira ◽  
Hong Kie Thio
Keyword(s):  
Long Range ◽  
Information Gain ◽  
Tectonic Setting ◽  
Depth Resolution ◽  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Testing Period ◽  
Crustal Earthquakes ◽  
Average Information ◽  
Better Than

The Every Earthquake a Precursor According to Scale (EEPAS) long-range earthquake forecasting model has been shown to be informative in several seismically active regions, including New Zealand, California and Japan. In previous applications of the model, the tectonic setting of earthquakes has been ignored. Here we distinguish crustal, plate interface, and slab earthquakes and apply the model to earthquakes with magnitude M≥4 in the Japan region from 1926 onwards. The target magnitude range is M≥ 6; the fitting period is 1966-1995; and the testing period is 1996-2005. In forecasting major slab earthquakes, it is optimal to use only slab and interface events as precursors. In forecasting major interface events, it is optimal to use only interface events as precursors. In forecasting major crustal events, it is optimal to use only crustal events as precursors. For the smoothed-seismicity component of the EEPAS model, it is optimal to use slab and interface events for earthquakes in the slab, interface events only for earthquakes on the interface, and crustal and interface events for crustal earthquakes. The optimal model parameters indicate that the precursor areas for slab earthquakes are relatively small compared to those for earthquakes in other tectonic categories, and that the precursor times and precursory earthquake magnitudes for crustal earthquakes are relatively large. The optimal models fit the learning data sets better than the raw EEPAS model, with an average information gain per earthquake of about 0.4. The average information gain is similar in the testing period, although it is higher for crustal earthquakes and lower for slab and interface earthquakes than in the learning period. These results show that earthquake interactions are stronger between earthquakes of similar tectonic types and that distinguishing tectonic types improves forecasts by enhancing the depth resolution where tectonic categories of earthquakes are vertically separated. However, when depth resolution is ignored, the model formed by aggregating the optimal forecasts for each tectonic category performs no better than the raw EEPAS model.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

scholarly journals Earthquake forecasting model for Nepal to improve prevention

2018 ◽  
Vol 7 (4) ◽  
pp. 700-701
Author(s):  
Brijesh Sathian ◽  
Edwin R Van Teijlingen
Keyword(s):  
Government Policy ◽  
Service Providers ◽  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Policy Makers ◽  
International Agencies ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
Current Scenario ◽  
Per Se

There is an urgent need of earthquake forecasting model for Nepal in this current scenario. It can be developed by the scientists of Nepal with the help of experienced international scientists. This will help the Nepalese to take timely and necessary precautions. We would argue that above all we need to use earthquake prediction knowledge to improve the disaster prepardness in local communities, service providers (hospitals, Non-Governmental Organizations, police, etc.), government policy-makers and international agencies. On the whole, both seismology and public health are most successful when focusing on  prevention not on prediction per se. J Epidemiol. 2017;7(4); 700-701.

scholarly journals Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Zhizhen Liu ◽  
Hong Chen ◽  
Yan Li ◽  
Qi Zhang
Keyword(s):  
Meteorological Factors ◽  
Essential Elements ◽  
Forecasting Model ◽  
Trajectory Data ◽  
Weather Factors ◽  
Demand Prediction ◽  
Combination Forecasting ◽  
Combination Forecasting Model ◽  
The Relationship ◽  
Better Than

Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on historical taxi trajectory data. In this study, we detected hotspots and proposed three methods to predict the taxi demand in hotspots. Next, we compared the predictive effect of the random forest model (RFM), ridge regression model (RRM), and combination forecasting model (CFM). Thereafter, we considered environmental and meteorological factors to predict the taxi demand in hotspots. Finally, the importance of indicators was analyzed, and the essential elements were the time, temperature, and weather factors. The results indicate that the prediction effect of CFM is better than those of RFM and RRM. The experiment obtains the relationship between taxi demand and environment and is helpful for taxi dispatching by considering additional factors, such as temperature and weather.

scholarly journals Realized Variance Modeling: Decoupling Forecasting from Estimation*

2020 ◽  
Vol 18 (3) ◽  
pp. 532-555
Author(s):  
Fabrizio Cipollini ◽  
Giampiero M Gallo ◽  
Alessandro Palandri
Keyword(s):  
Long Range ◽  
Long Range Dependence ◽  
Realized Variance ◽  
Variance Modeling ◽  
Out Of Sample ◽  
Arma Modeling ◽  
Better Than

Abstract This article evaluates the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and functions delivering estimates (estimation criteria). Our empirical findings highlight that: independently of the econometrician’s forecasting loss (FL) function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts than the Heterogeneous Autoregressive (HAR) family, for any of the FL functions considered; the (2, 1) parameterizations with negative lag-2 coefficient emerge as the benchmark specifications generating the best forecasts and approximating long-range dependence as does the HAR family.

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