scholarly journals A Testable Worldwide Earthquake Faulting Mechanism Forecast Model

2020 ◽  
Author(s):  
Matteo Taroni ◽  
Jacopo Selva
Keyword(s):  
Forecast Model ◽  
Earthquake Faulting

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Global Forecast Model to Predict the Daily Dose of the Solar Erythemally Effective UV Radiation¶

2005 ◽  
Vol 81 (1) ◽  
pp. 154 ◽  
Author(s):  
Alois W. Schmalwieser ◽  
Günther Schauberger ◽  
Michal Janouch ◽  
Manuel Nunez ◽  
Tapani Koskela ◽  
...  
Keyword(s):  
Uv Radiation ◽  
Forecast Model ◽  
Daily Dose

scholarly journals Autism Tsunami: the Impact of Rising Prevalence on the Societal Cost of Autism in the United States

2021 ◽  
Author(s):  
Mark Blaxill ◽  
Toby Rogers ◽  
Cynthia Nevison
Keyword(s):  
Forecast Model ◽  
The United States ◽  
Population Projections ◽  
Base Case ◽  
Prevention Strategies ◽  
Total Base ◽  
Cost Categories ◽  
The Cost ◽  
The Impact ◽  
First Time

AbstractThe cost of ASD in the U.S. is estimated using a forecast model that for the first time accounts for the true historical increase in ASD. Model inputs include ASD prevalence, census population projections, six cost categories, ten age brackets, inflation projections, and three future prevalence scenarios. Future ASD costs increase dramatically: total base-case costs of $223 (175–271) billion/year are estimated in 2020; $589 billion/year in 2030, $1.36 trillion/year in 2040, and $5.54 (4.29–6.78) trillion/year by 2060, with substantial potential savings through ASD prevention. Rising prevalence, the shift from child to adult-dominated costs, the transfer of costs from parents onto government, and the soaring total costs raise pressing policy questions and demand an urgent focus on prevention strategies.

An Analysis of the Accuracy of 120-h Predictions by the National Meteorological Center's Medium-Range Forecast Model

1994 ◽  
Vol 9 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Mary A. Bedrick ◽  
Anthony J. Cristaldi ◽  
Stephen J. Colucci ◽  
Daniel S. Wilks
Keyword(s):  
Forecast Model ◽  
Medium Range Forecast ◽  
Medium Range

scholarly journals The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

2021 ◽  
Vol 13 (9) ◽  
pp. 1702
Author(s):  
Kévin Barbieux ◽  
Olivier Hautecoeur ◽  
Maurizio De Bartolomei ◽  
Manuel Carranza ◽  
Régis Borde
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Forecast Model ◽  
Low Earth Orbit ◽  
Reference Image ◽  
Motion Vectors ◽  
Infrared Imager ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Leo Satellite

Atmospheric Motion Vectors (AMVs) are an important input to many Numerical Weather Prediction (NWP) models. EUMETSAT derives AMVs from several of its orbiting satellites, including the geostationary satellites (Meteosat), and its Low-Earth Orbit (LEO) satellites. The algorithm extracting the AMVs uses pairs or triplets of images, and tracks the motion of clouds or water vapour features from one image to another. Currently, EUMETSAT LEO satellite AMVs are retrieved from georeferenced images from the Advanced Very-High-Resolution Radiometer (AVHRR) on board the Metop satellites. EUMETSAT is currently preparing the operational release of an AMV product from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. The main innovation in the processing, compared with AVHRR AMVs, lies in the co-registration of pairs of images: the images are first projected on an equal-area grid, before applying the AMV extraction algorithm. This approach has multiple advantages. First, individual pixels represent areas of equal sizes, which is crucial to ensure that the tracking is consistent throughout the processed image, and from one image to another. Second, this allows features that would otherwise leave the frame of the reference image to be tracked, thereby allowing more AMVs to be derived. Third, the same framework could be used for every LEO satellite, allowing an overall consistency of EUMETSAT AMV products. In this work, we present the results of this method for SLSTR by comparing the AMVs to the forecast model. We validate our results against AMVs currently derived from AVHRR and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The release of the operational SLSTR AMV product is expected in 2022.

scholarly journals Forecast and Analysis of Regional Energy Demand Based on Grey Linear Regression Forecast Model

2021 ◽  
Vol 645 ◽  
pp. 012047
Author(s):  
Mingli Zhang ◽  
Zhongwei Li ◽  
Kun Song ◽  
Duojiao Guan ◽  
Na Zhang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Energy Demand ◽  
Forecast Model ◽  
Regional Energy ◽  
Regional Energy Demand
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