Analysis of Trends in Low-Flow Time Series of Canadian Rivers

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

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Blending neighbor-based and climate-based information to obtain robust low-flow estimates from short time series

Water Resources Research ◽

10.1002/2012wr012940 ◽

2013 ◽

Vol 49 (12) ◽

pp. 8017-8025 ◽

Cited By ~ 3

Author(s):

Pierre Nicolle ◽

Vazken Andréassian ◽

Eric Sauquet

Keyword(s):

Time Series ◽

Low Flow ◽

Short Time Series ◽

Short Time

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Wavelet neural network model for river flow time series

Proceedings of the Institution of Civil Engineers - Water Management ◽

10.1680/wama.10.00092 ◽

2012 ◽

Vol 165 (8) ◽

pp. 425-439 ◽

Cited By ~ 3

Author(s):

Budu Krishna ◽

Yellamelli Ramji Satyaji Rao ◽

Purna Chandra Nayak

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

River Flow ◽

Flow Time ◽

Wavelet Neural Network

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Abstract 19091: Prognosis of Patients With Out-of-hospital Refractory Cardiac Arrest Referred for Extracorporeal Life Support: An Observational Study

Circulation ◽

10.1161/circ.132.suppl_3.19091 ◽

2015 ◽

Vol 132 (suppl_3) ◽

Author(s):

Cecile Ursat ◽

Marie-Ange Tilliette ◽

Charles Groizard ◽

Margot Cassuto ◽

Anna Ozguler ◽

...

Keyword(s):

Cardiac Arrest ◽

Life Support ◽

Extracorporeal Life Support ◽

Spontaneous Circulation ◽

Flow Time ◽

Low Flow ◽

Time Interval ◽

Specific Situation ◽

Time Intervals ◽

Paris Area

Introduction: In case of no return of spontaneous circulation (ROSC) after conventional cardio-pulmonary resuscitation (CPR), out-of-hospital cardiac arrest (OHCA) patients could be referred for extracorporeal life support (ECLS). Guidelines have been published concerning this specific situation (1). The aim of our study was to describe the prognosis of OHCA patients and verify if referral to ECLS was compliant with these recommendations mainly studying time intervals (no-flow < 5 min, low-flow < 100 min). Methods: A prospective survey on OHCA referred to ECLS was implemented from 03/01/12 until 06/11/15 in an Emergency Medical Service (EMS) located in Paris area (France). This survey included 43 patients referred to hospital for ECLS. Variables were given as means and percentages. Results: Patients referred to ECLS were more often men (77%), with a mean age of 51 years old. Most of 43 OHCA occurred at home (51%), although 26% occurred on public area and 16% at workplace. In 40% of cases, CPR was performed by a witness and in 33% by a health professional. A first Basic Life Support ambulance arrived on scene within 7 min 50 sec, whereas EMS ambulance arrived on scene within 18 min 27 sec after OHCA. At EMS arrival on scene, patients were on asystole (44%), ventricular fibrillation (37%), and on spontaneous circulation (12%). The no-flow time interval was 4 min 10 sec on average (6 patients had a no-flow over 5 min) with 43% of patients with no no-flow. Low-flow time-interval was 44 min. External electric shock was delivered before EMS arrival on 21% of cases, and EMS itself delivered a shock in 40% of cases. Epinephrine was used for all patients, 10.35 mg on average. No patient survived OHCA after referral to ECLS. Discussion: Although this is a small series of 43 patients, no OHCA patient referred to ECLS survived. These results are mainly due to a non-shockable initial condition or too long no-flow time intervals. In order to improve the outcome and bring benefit to the proper expected patients through a cost-effective pathway, we released a reminder of the right recommendations in our EMS. (1) Riou B., Adnet F., Baud F et al. A. Recommandation sur les indications de l’assistance circulatoire dans le traitement des arrêts cardiaques réfractaires. Ann Fr Anesth Réanim 2009 ; 28 : 182-6.

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