scholarly journals A combined numerical and neural technique for short term prediction of ocean currents in the Indian Ocean

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Dauji Saha ◽  
M. C. Deo ◽  
Sudheer Joseph ◽  
Kapilesh Bhargava
Keyword(s):  
Indian Ocean ◽  
Ocean Currents ◽  
Short Term ◽  
Term Prediction ◽  
The Indian Ocean ◽  
Short Term Prediction

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

scholarly journals Virus isolation studies suggest short-term variations in abundance in natural cyanophage populations of the Indian Ocean

2006 ◽  
Vol 86 (3) ◽  
pp. 499-505 ◽  
Author(s):  
Martha R.J. Clokie ◽  
Andrew D. Millard ◽  
Jaytry Y. Mehta ◽  
Nicholas H. Mann
Keyword(s):  
Indian Ocean ◽  
Surface Waters ◽  
Molecular Diversity ◽  
Virus Isolation ◽  
Short Term ◽  
Distinct Subgroup ◽  
Time Points ◽  
The Indian Ocean ◽  
Long Timescales ◽  
Time Point

Cyanophage abundance has been shown to fluctuate over long timescales and with depth, but little is known about how it varies over short timescales. Previous short-term studies have relied on counting total virus numbers and therefore the phages which infect cyanobacteria cannot be distinguished from the total count.In this study, an isolation-based approach was used to determine cyanophage abundance from water samples collected over a depth profile for a 24 h period from the Indian Ocean. Samples were used to infect Synechococcus sp. WH7803 and the number of plaque forming units (pfu) at each time point and depth were counted. At 10 m phage numbers were similar for most time-points, but there was a distinct peak in abundance at 0100 hours. Phage numbers were lower at 25 m and 50 m and did not show such strong temporal variation. No phages were found below this depth. Therefore, we conclude that only the abundance of phages in surface waters showed a clear temporal pattern over a short timescale. Fifty phages from a range of depths and time points were isolated and purified. The molecular diversity of these phages was estimated using a section of the phage-encoded psbD gene and the results from a phylogenetic analysis do not suggest that phages from the deeper waters form a distinct subgroup.

Short-term prediction of UT1-UTC by combination of the grey model and neural networks

2017 ◽  
Vol 59 (2) ◽  
pp. 524-531 ◽  
Author(s):  
Yu Lei ◽  
Min Guo ◽  
Dan-dan Hu ◽  
Hong-bing Cai ◽  
Dan-ning Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Grey Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction
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