A Statistical Approach in Determining the Electrical Short Term Demand in a Rapid Railway System

2011 ◽  
Author(s):  
Grant Manuel ◽  
Jan-Harm C. Pretorius
Keyword(s):  
Statistical Approach ◽  
Short Term ◽  
Railway System

scholarly journals A statistical approach to fast nowcasting of lightning potential fields

2020 ◽  
Vol 6 (2) ◽  
pp. 79-90
Author(s):  
Joshua North ◽  
Zofia Stanley ◽  
William Kleiber ◽  
Wiebke Deierling ◽  
Eric Gilleland ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Potential Fields ◽  
Short Term ◽  
Modeling Framework ◽  
Image Deformation ◽  
Atmospheric Research ◽  
Advection Scheme ◽  
Complex Image ◽  
Short Term Prediction ◽  
Deformation Approach

Abstract. Thunderstorms and associated hazards like lightning can pose a serious threat to people outside and infrastructure. Thus, very short-term prediction capabilities (called nowcasting) have been developed to capture this threat and aid in decision-making on when to bring people inside for safety reasons. The atmospheric research and operational communities have been developing and using nowcasting methods for decades, but most methods do not rely on formal statistical approaches. A novel and fast statistical approach to nowcasting of lightning threats is presented here that builds upon an integro-difference modeling framework. Inspiration from the heat equation is used to define a redistribution kernel, and a simple linear advection scheme is shown to work well for the lightning prediction example. The model takes only seconds to estimate and nowcast and is competitive with a more complex image deformation approach that is computationally infeasible for very short-term nowcasts.

Short-term responses to watershed logging on biomass mercury and methylmercury accumulation by periphyton in boreal lakes

2006 ◽  
Vol 63 (8) ◽  
pp. 1734-1745 ◽  
Author(s):  
Mélanie Desrosiers ◽  
Dolors Planas ◽  
Alfonso Mucci
Keyword(s):  
Total Mercury ◽  
Statistical Approach ◽  
Boreal Lakes ◽  
Short Term ◽  
Predictive Tool ◽  
Management Tools ◽  
Watershed Characteristics ◽  
Before And After ◽  
Periphyton Biomass ◽  
Shield Lakes

In the boreal forest, watershed logging may increase runoff, as well as chemical loading, including nutrient, dissolved organic carbon, and mercury, to lakes. Because they are exposed directly to nutrients and contaminants exported from the watershed, littoral communities such as periphyton may respond quickly to watershed disturbances. The objectives of this study were to evaluate the response of periphyton to watershed logging using a BACI (before–after control–impact) statistical approach and to develop a predictive tool to facilitate the elaboration of practical logging policies aimed at reducing Hg loading to lakes. In this study, we compare the periphyton biomass in 18 boreal Canadian Shield lakes, as well as their total mercury and methylmercury levels. During the ice-free season from 2000 to 2002, eight of these lakes were monitored before and after logging, with the other 10 lakes serving as controls. The BACI statistical analyses reveal a significant impact of logging on periphyton biomass (decrease; 0.6- to 1.5-fold) and methylmercury accumulation (increase; 2- to 9.6-fold). This study demonstrates that periphyton responds quickly to disturbances of the watershed. Our results suggest that the periphyton and watershed characteristics could serve as good management tools and that logging should be limited in watersheds with a mean slope below 7.0%.

scholarly journals Demonstrating the Utility of Egocentric Relational Event Modeling Using Focal Follow Data from Congolese BaYaka Children and Adolescents Engaging in Work and Play

Field Methods ◽  
2021 ◽  
pp. 1525822X2098707
Author(s):  
Kate Ellis-Davies ◽  
Sheina Lew-Levy ◽  
Eleanor Fleming ◽  
Adam H. Boyette ◽  
Thom Baguley
Keyword(s):  
Time Allocation ◽  
Statistical Approach ◽  
Child And Adolescent ◽  
Short Term ◽  
Work Activities ◽  
Event Modeling ◽  
Trade Offs ◽  
Activity Frequency ◽  
Temporal Aspects ◽  
Extended Work

Temporal aspects of child and adolescent time allocation in diverse cultural settings have been difficult to model using conventional statistical techniques. A new statistical approach, Egocentric Relational Event Modelling (EREM), allows for the simultaneous modelling of activity frequency, duration, and sequencing. Here, EREM is applied to a focal follow dataset of Congolese BaYaka forager child and adolescent play and work activities. Results show that, as children age, they engage in less frequent and extended play bouts and more frequent and extended work bouts. Bout frequency and duration were a more sensitive measure for early sex differences than overall time allocation. Sequential patterns of work and play suggest that these activities have short-term energetic trade-offs. This article demonstrates that EREM can reveal stable and variable patterns in child development.

Taylor series approximation and unbiased GM(1,1) based hybrid statistical approach for forecasting daily gold price demands

2019 ◽  
Vol 9 (1) ◽  
pp. 5-18 ◽  
Author(s):  
R.M. Kapila Tharanga Rathnayaka ◽  
D.M.K.N. Seneviratna
Keyword(s):  
Time Series ◽  
Taylor Series ◽  
Statistical Approach ◽  
Time Series Data ◽  
Gold Price ◽  
Series Data ◽  
Short Term ◽  
Content Type ◽  
Taylor Series Approximation ◽  
Series Approximation

Purpose The time series analysis is an essential methodology which comprises the tools for analyzing the time series data to identify the meaningful characteristics for making future ad-judgments. The purpose of this paper is to propose a Taylor series approximation and unbiased GM(1,1) based new hybrid statistical approach (HTS_UGM(1,1)) for forecasting time series data under the poor, incomplete and uncertain information systems in a short period of time manner. Design/methodology/approach The gray forecasting is a dynamical methodology which can be classified into different categories based on their respective functions. The new proposed methodology is made up of three different methodologies including the first-order unbiased GM(1,1), Markov chain and Taylor approximation. In addition to that, two different traditional gray operational mechanisms include GM(1,1) and unbiased GM(1,1) used as the comparisons. The main objective of this study is to forecast gold price demands in a short-term manner based on the data which were taken from the Central Bank of Sri Lanka from October 2017 to December 2017. Findings The error analysis results suggested that the new proposed HTS_UGM(1,1) is highly accurate (less than 10 percent) with lowest RMSE error values in a one head as well as weakly forecasting’s than separate gray forecasting methodologies. Originality/value The findings suggested that the new proposed hybrid approach is more suitable and effective way for forecasting time series indices than separate time series forecasting methodologies in a short-term manner.

scholarly journals Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mei-Quan Xie ◽  
Xia-Miao Li ◽  
Wen-Liang Zhou ◽  
Yan-Bing Fu
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Estimation Method ◽  
Transportation Systems ◽  
Divide And Conquer ◽  
Short Term ◽  
High Speed Railway ◽  
Matrix Estimation ◽  
Passenger Flow ◽  
Railway System

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.

Short-term forecasting of solar radiation: a statistical approach using satellite data

Solar Energy ◽  
1999 ◽  
Vol 67 (1-3) ◽  
pp. 139-150 ◽  
Author(s):  
A. Hammer ◽  
D. Heinemann ◽  
E. Lorenz ◽  
B. Lückehe
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Statistical Approach ◽  
Short Term ◽  
Short Term Forecasting
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