A real-time flood forecasting model based on maximum-entropy spectral analysis: II. Application

1993 ◽  
Vol 7 (2) ◽  
pp. 131-151 ◽  
Author(s):  
P. F. Krstanovic ◽  
V. P. Singh
Keyword(s):  
Spectral Analysis ◽  
Maximum Entropy ◽  
Real Time ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Maximum Entropy Spectral Analysis ◽  
Model Based

scholarly journals A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

2017 ◽  
Vol 88 ◽  
pp. 151-167 ◽  
Author(s):  
Xingya Xu ◽  
Xuesong Zhang ◽  
Hongwei Fang ◽  
Ruixun Lai ◽  
Yuefeng Zhang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Model Based ◽  
Filter Approach

Transient Rhythms in the Swimming Activity of Sarsia Tubulosa (Hydrozoa)

1982 ◽  
Vol 96 (1) ◽  
pp. 181-193
Author(s):  
JANET L. LEONARD
Keyword(s):  
Spectral Analysis ◽  
Maximum Entropy ◽  
Swimming Activity ◽  
Endogenous Rhythms ◽  
Maximum Entropy Spectral Analysis ◽  
Transient Nature ◽  
High Degree ◽  
Behavioural Mechanism

Maximum entropy spectral analysis (MESA) was used to assess the contribution of endogenous rhythms to the timing of swim bouts in a hydrozoan jellyfish, Sarsia tubulosa M. Sars. The results show that the high degree of variability in Sarsia swimming activity is due largely to the number of rhythms which may contribute to the behaviour and to the transient nature of these rhythms. I conclude that the ability to ‘choose’ among behavioural rhythms may be a widespread behavioural mechanism in cnidarians and I suggest that, in Sarsia, these transient behavioural rhythms may originate in activity of the marginal pacemaker system.

scholarly journals Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

2012 ◽  
Vol 12 (12) ◽  
pp. 3719-3732 ◽  
Author(s):  
L. Mediero ◽  
L. Garrote ◽  
A. Chavez-Jimenez
Keyword(s):  
Data Assimilation ◽  
Genetic Programming ◽  
Real Time ◽  
High Performance ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Distribution Functions ◽  
Model Parameters ◽  
Hydrological Process ◽  
Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

scholarly journals Optical Spectral Analysis of Real-Time TDDFT Using Maximum Entropy Method

2015 ◽  
Vol 14 (3) ◽  
pp. 71-73 ◽  
Author(s):  
Mitsuki TOOGOSHI ◽  
Satoru S. KANO ◽  
Yasunari ZEMPO
Keyword(s):  
Spectral Analysis ◽  
Maximum Entropy ◽  
Real Time ◽  
Maximum Entropy Method ◽  
Entropy Method
Sign in / Sign up

Export Citation Format

Share Document