Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent

2021 ◽  
pp. 104075
Author(s):  
Kehan Yang ◽  
Keith N. Musselman ◽  
Karl Rittger ◽  
Steven A. Margulis ◽  
Thomas H. Painter ◽  
...  
Author(s):  
Lahcen El Mentaly ◽  
Abdellah Amghar ◽  
Hassan Sahsah

Abstract In this work we have presented a generalization of the Temperature Parametric (TP) Method which is based on the detection of the maximum power point by the prediction of the corresponding optimal voltage. This operating voltage is determined by the continuous measurement of the ambient temperature and solar irradiation. This new approach is based on a 3D linear regression model linking these quantities and which allows to our method to realize the maximum power point tracking in real time. The simulation shows that this new technique has a better MPPT efficiency compared to Hill Climbing technique.


2011 ◽  
Vol 130-134 ◽  
pp. 2072-2076
Author(s):  
Xin Ping Xiao ◽  
Yi Chen Hu ◽  
Huan Guo

This paper expects to get the real-time changes of traffic flow by researching the relationships among the three basic parameters flow, speed and occupancy. we firstly carries out the statistical correlation analysis and grey relational analysis to study the connection between the traffic flow parameters flow and speed, flow and occupancy to get a conclusion that there doesn’t exist a significant linear relationship between neither of the comparisons, and the influence of speed on the flow is a little bigger than that of occupancy. Then we try to establish the binary linear regression model without the intercept and GM (0,3) model to do the data fitting, the final simulation results illustrate that the two methods has effects in some extent, also explain the complexity of traffic flow.


2010 ◽  
Vol 76 (24) ◽  
pp. 8019-8025 ◽  
Author(s):  
Leslie Ogorzaly ◽  
Isabelle Bertrand ◽  
Myriam Paris ◽  
Armand Maul ◽  
Christophe Gantzer

ABSTRACT Detection of specific genetic markers can rapidly identify the presence of enteric viruses in groundwater. However, comparison of stability characteristics between genetic and infectivity markers is necessary to better interpret molecular data. Human adenovirus serotype 2 (HAdV2), in conjunction with MS2 phages or GA phages, was spiked into raw groundwater microcosms. Viral stability was periodically assessed by both infectivity and real-time PCR methods. The results of this yearlong study suggest that adenoviruses have the most stable persistence profile and an ability to survive for a long time in groundwater. According to a linear regression model, infectivity reductions of HAdV2 ranged from 0.0076 log10/day (4°C) to 0.0279 log10/day (20°C) and were significantly lower than those observed for phages. No adenoviral genome degradation was observed at 4°C, and the reduction was estimated at 0.0036 log10/day at 20°C. Occurrence study showed that DNA of human adenoviruses could be observed in groundwater from a confined aquifer (7 of the 60 samples were positive by real-time PCR), while no fecal indicators were detected. In agreement with the persistence of genetic markers, the presence of adenoviral DNA in groundwater may be misleading in term of health risk, especially in the absence of information on the infective status.


2005 ◽  
Vol 20 (4) ◽  
pp. 688-699 ◽  
Author(s):  
John A. Knaff ◽  
Charles R. Sampson ◽  
Mark DeMaria

Abstract The current version of the Statistical Typhoon Intensity Prediction Scheme (STIPS) used operationally at the Joint Typhoon Warning Center (JTWC) to provide 12-hourly tropical cyclone intensity guidance through day 5 is documented. STIPS is a multiple linear regression model. It was developed using a “perfect prog” assumption and has a statistical–dynamical framework, which utilizes environmental information obtained from Navy Operational Global Analysis and Prediction System (NOGAPS) analyses and the JTWC historical best track for development. NOGAPS forecast fields are used in real time. A separate version of the model (decay-STIPS) is produced that accounts for the effects of landfall by using an empirical inland decay model. Despite their simplicity, STIPS and decay-STIPS produce skillful intensity forecasts through 4 days, based on a 48-storm verification (July 2003–October 2004). Details of this model’s development and operational performance are presented.


2000 ◽  
Vol 1727 (1) ◽  
pp. 120-126 ◽  
Author(s):  
Yinhai Wang ◽  
Nancy L. Nihan

Traffic speed is one of the most important indicators for traffic control and management. Unfortunately, speed cannot be measured directly from single inductance loops, the most commonly used detectors. To calculate space-mean speed, a constant, g, is often adopted to convert lane occupancy to traffic density. However, as illustrated by data from the present study, such a formula consistently underestimates speed whenever a significant number of trucks or other longer vehicles are present. This is because g is actually not a constant but, rather, a function of vehicle length. To calculate the value of g suitably, one needs to know the percentage of long vehicles or the mean vehicle length in real time. However, such information is not directly available from single-loop outputs. It is shown how the occupancy variance obtained from single-loop data can be used to estimate the percentage of long vehicles and how a log-linear regression model for mean vehicle length estimation based only on single-loop outputs can be developed. The estimated mean vehicle length is used to calculate the corresponding g-value in real-time to estimate speed more accurately. The speed estimations with corrected g-values are very close to the speeds observed by the speed trap in the present study.


2018 ◽  
Vol 7 (3.12) ◽  
pp. 722 ◽  
Author(s):  
S Mohanavalli ◽  
S Karthika ◽  
Srividya . ◽  
K R.Uthayan ◽  
N Sandya

Twitter is a micro-blogging site that facilitates users to exchange short messages. Twitter is predominantly used in fields like business, healthcare, education and nation security. Twitter is being used by a large number of users for updating real time information and sentiment expression. The objective of this paper is to automate the classification of tweets into particular category using various machine learning algorithms like naïve bayes, SVM, and linear regression model. The proposed ensemble model aims to improve performance metrics of these algorithms. A comparative study of the algorithms used for tweet classification is done and results are discussed in the paper.  


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