scholarly journals LONG-TERM PREDICTION OF CASPIAN SEA LEVEL UNDER CMIP6 SCENARIOS USING ARTIFICIAL NEURAL NETWORKS

2020 ◽  
pp. 5
Author(s):  
S. Mahya Hoseini ◽  
Mohsen Soltanpour
Keyword(s):  
Sea Level ◽  
Caspian Sea ◽  
Coupled Model ◽  
Model Intercomparison ◽  
Climate Change Projections ◽  
Fluctuation Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Long Term Prediction

Artificial Neural Network (ANN) is employed to predict the long-term Caspian Sea level (CSL). 114-year observed CSL data (1900-2014) and the precipitation and temperature of historical and future scenarios of Coupled Model Intercomparison Phase 6 (CMIP6) are used to predict the future fluctuations of CSL (2015-2050). The values of the statistical indices in training, validating and testing periods (1900-2014) indicate the efficiency of the ANN in reconstruction of the CSL. Considering the outputs of different climate change projections (CMIP6) and excluding the human interventions, the study predicts the CSL fluctuation range of -28 m to -26m until 2050.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/Kfj-gr65TR8

scholarly journals Experimental protocol for sealevel projections from ISMIP6 standalone ice sheet models

2020 ◽  
Author(s):  
Sophie Nowicki ◽  
Antony J. Payne ◽  
Heiko Goelzer ◽  
Helene Seroussi ◽  
William H. Lipscomb ◽  
...  
Keyword(s):  
Sea Level ◽  
Climate Models ◽  
Ice Sheet ◽  
Coupled Model ◽  
Sea Level Change ◽  
Model Intercomparison ◽  
Level Change ◽  
Ice Sheet Modeling ◽  
Emissions Scenarios ◽  
Intercomparison Project

Abstract. Projection of the contribution of ice sheets to sea-level change as part of the Coupled Model Intercomparison Project – phase 6 (CMIP6) takes the form of simulations from coupled ice-sheet-climate models and standalone ice sheet models, overseen by the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). This paper describes the experimental setup for process-based sea-level change projections to be performed with standalone Greenland and Antarctic ice sheet models in the context of ISMIP6. The ISMIP6 protocol relies on a suite of polar atmospheric and oceanic CMIP-based forcing for ice sheet models, in order to explore the uncertainty in projected sea-level change due to future emissions scenarios, CMIP models, ice sheet models, and parameterizations for ice-ocean interactions. We describe here the approach taken for defining the suite of ISMIP6 standalone ice sheet simulations, document the experimental framework and implementation, as well as present an overview of the ISMIP6 forcing to be used by participating ice sheet modeling groups.

On the Analysis of Turbulent Flow Signals by Artificial Neural Networks and Adaptive Techniques

2007 ◽  
Author(s):  
F. Lo´pez Pen˜a ◽  
F. Bellas ◽  
R. J. Duro ◽  
P. Farin˜as
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Irregular Sampling ◽  
Linear Filter ◽  
Signal Characteristics ◽  
Prediction Mode ◽  
Sampled Signal ◽  
Artificial Neural ◽  
Long Term Prediction

Artificial Neural Networks (ANNs) and evolution are applied to the analysis of turbulent signals. In a first instance, a new trainable delay based artificial neural network is used to analyze Hot Wire Anemometer (HW) signals obtained at different positions within the wake of a circular cylinder with Reynolds number values ranging from 2000 to 8000. Results show that these networks are capable of performing accurate short term predictions of the turbulent signal. In addition, the ANNs can be set in a long term prediction mode resulting in a sort of non linear filter able to extract the features having to do with the larger eddies and coherent structures. In a second stage these networks are used to reconstruct a regularly sampled signal straight from the irregularly sampled one provided by a Laser Doppler Anemometer (LDA). The irregular sampling dynamics of the LDA signals is governed by the arrival of the seeding particles, superimposing the already complex turbulent signal characteristics. To cope with this complexity, an evolutionary based strategy is used to perform an adaptive and continuous online training of the ANNs. This approach permits obtaining a regularly sampled signal not by interpolating the original one, as it is often done, but by modeling it.

Application of Artificial Neural Network (ANN) to improve forecasting of sea level

2012 ◽  
Vol 55 ◽  
pp. 101-110 ◽  
Author(s):  
Alessandro Filippo ◽  
Audálio Rebelo Torres ◽  
Björn Kjerfve ◽  
André Monat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sea Level ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Modelling of the evolution of iron passivity: solving the moving boundaries problem

2012 ◽  
Vol 1475 ◽  
Author(s):  
Frantz A. Martin ◽  
Christian Bataillon
Keyword(s):  
Experimental Data ◽  
Corrosion Rate ◽  
Coupled Model ◽  
Oxide Thickness ◽  
Reaction Rates ◽  
Numerical Code ◽  
Fully Implicit ◽  
Geological Repository ◽  
Long Term Prediction

ABSTRACTIn the framework of long term prediction of corrosion in French geological repository systems, the modelling of the time evolution of the corrosion rate of iron over centuries is of high matter. The DPCM (Diffusion Poisson Coupled Model), implemented with the fully implicit CALIPSO numerical code can give access to the evolution of the oxide thickness grown on iron and the resulting corrosion rate. DPCM parameters for outer interfacial reactions have been set to fit to XANES experimental data provided by literature, and then the parameters for inner reaction rates have been evaluated from fitting to ellipsometry results taken from literature. The result is a good average fitting of the model to the experimental data.

AN ARTIFICIAL NEURAL NETWORK APPROACH TO HOTEL EMPLOYEE SATISFACTION: THE CASE OF CHINA

2008 ◽  
Vol 36 (4) ◽  
pp. 467-482 ◽  
Author(s):  
Xizhou Tian ◽  
Yongjian Pu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Employee Satisfaction ◽  
High Rate ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Hotel Employee ◽  
And Gender ◽  
Artificial Neural Network Ann

At present, the hotel employment sector in China has a high rate of employee turnover compared to other services. This is not unlike other countries. The reason for the turnover among hotel employees may be lower worker satisfaction resulting in decreased – or no – loyalty to employers. This study was based on an Artificial Neural Network (ANN). The factors influencing employee satisfaction were examined and the impacts of demographic characteristics on hotel employee satisfaction were analyzed. Results show that hotel employee satisfaction in China is low, hotel employee satisfaction differs by age and gender, and that professional development opportunities for employees and the long-term growth prospects of the hotels themselves are the most important contributors to employee satisfaction. On the basis of these findings, several recommendations for improving employee satisfaction, thereby sustaining the long-term economic health of China's hospitality industry, are provided.

Observational and Model Estimates of Cloud Amount Feedback over the Indian and Pacific Oceans

2014 ◽  
Vol 27 (2) ◽  
pp. 925-940 ◽  
Author(s):  
Katinka Bellomo ◽  
Amy C. Clement ◽  
Joel R. Norris ◽  
Brian J. Soden
Keyword(s):  
Coupled Model ◽  
Cloud Amount ◽  
Western Indian Ocean ◽  
Model Intercomparison ◽  
Central Pacific ◽  
Radiative Impact ◽  
Southern Central ◽  
Intercomparison Project ◽  
Very High

AbstractConstraining intermodel spread in cloud feedback with observations is problematic because available cloud datasets are affected by spurious behavior in long-term variability. This problem is addressed by examining cloud amount in three independent ship-based [Extended Edited Cloud Reports Archive (EECRA)] and satellite-based [International Satellite Cloud Climatology Project (ISCCP) and Advanced Very High Resolution Radiometer Pathfinder Atmosphere–Extended (PATMOS-X)] observational datasets, and models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The three observational datasets show consistent cloud variability in the overlapping years of coverage (1984–2007). The long-term cloud amount change from 1954 to 2005 in ship-based observations shares many of the same features with the multimodel mean cloud amount change of 42 CMIP5 historical simulations, although the magnitude of the multimodel mean is smaller. The radiative impact of cloud changes is estimated by computing an observationally derived estimate of cloud amount feedback. The observational estimates of cloud amount feedback are statistically significant over four regions: the northeast Pacific subtropical stratocumulus region and equatorial western Pacific, where cloud amount feedback is found to be positive, and the southern central Pacific and western Indian Ocean, where cloud amount feedback is found to be negative. Multimodel mean cloud amount feedback is consistent in sign but smaller in magnitude than in observations over these four regions because models simulate weaker cloud changes. Individual models, however, can simulate cloud amount feedback of the same magnitude if not larger than observed. Focusing on the regions where models and observations agree can lead to improved understanding of the mechanisms of cloud amount changes and associated radiative impact.

scholarly journals Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work

2021 ◽  
Vol 12 ◽  
Author(s):  
Marco Iosa ◽  
Edda Capodaglio ◽  
Silvia Pelà ◽  
Benedetta Persechino ◽  
Giovanni Morone ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Return To Work ◽  
Measurement Unit ◽  
Walking Ability ◽  
Double Support ◽  
Important Input ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke.

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