scholarly journals Using GENIE to study a tipping point in the climate system

2008 ◽  
Vol 367 (1890) ◽  
pp. 871-884 ◽  
Author(s):  
Timothy M Lenton ◽  
Richard J Myerscough ◽  
Robert Marsh ◽  
Valerie N Livina ◽  
Andrew R Price ◽  
...  
Keyword(s):  
Thermohaline Circulation ◽  
Time Series Data ◽  
Three Dimensional ◽  
Tipping Point ◽  
Climate System ◽  
Climate Forcing ◽  
Series Data ◽  
Model Parameters ◽  
System Modelling ◽  
Stable States

We have used the Grid ENabled Integrated Earth system modelling framework to study the archetypal example of a tipping point in the climate system; a threshold for the collapse of the Atlantic thermohaline circulation (THC). eScience has been invaluable in this work and we explain how we have made it work for us. Two stable states of the THC have been found to coexist, under the same boundary conditions, in a hierarchy of models. The climate forcing required to collapse the THC and the reversibility or irreversibility of such a collapse depends on uncertain model parameters. Automated methods have been used to assimilate observational data to constrain the pertinent parameters. Anthropogenic climate forcing leads to a robust weakening of the THC and increases the probability of crossing a THC tipping point, but some ensemble members collapse readily, whereas others are extremely resistant. Hence, we test general methods that have been developed to directly diagnose, from time-series data, the proximity of a ‘tipping element’, such as the THC to a bifurcation point. In a three-dimensional ocean–atmosphere model exhibiting THC hysteresis, despite high variability in the THC driven by the dynamical atmosphere, some early warning of an approaching tipping point appears possible.

Container port throughput analysis and active management using control theory

2021 ◽  
pp. 147509022110208
Author(s):  
Cuong Truong Ngoc ◽  
Xiao Xu ◽  
Hwan-Seong Kim ◽  
Duy Anh Nguyen ◽  
Sam-Sang You
Keyword(s):  
Control Theory ◽  
Time Series Data ◽  
Sliding Mode ◽  
Container Terminal ◽  
Three Dimensional ◽  
Maritime Transportation ◽  
Container Terminals ◽  
Active Management ◽  
Dynamical Analysis ◽  
Series Data

This paper deals with three-dimensional (3D) model of competitive Lotka-Volterra equation to investigate nonlinear dynamics and control strategy of container terminal throughput and capacity. Dynamical behaviors are intensely explored by using eigenvalue evaluation, bifurcation analysis, and time-series data. The dynamical analysis is to show the stability with bifurcation of the competition and collaboration of multiple container terminals in the maritime transportation. Based on the chaotic analysis, the sliding mode control theory has been utilized for optimization of port operations under disruptions. Extensive numerical simulations have been conducted to validate the efficacy and reliability of the presented control algorithms. Particularly, the closed-loop system has been assessed through chaotic suppression and synchronization strategies for port management. Finally, the presented fundamental techniques can be utilized to provide managerial insights and solutions on efficient seaport operations that allow more timely and cost-effective decision making for port authorities in such a highly competitive environment.

scholarly journals A river system modelling platform for Murray-Darling Basin, Australia

2012 ◽  
Vol 15 (4) ◽  
pp. 1109-1120 ◽  
Author(s):  
Ang Yang ◽  
Geoff Podger ◽  
Shane Seaton ◽  
Robert Power
Keyword(s):  
Time Series Data ◽  
Local Development ◽  
Global Climate ◽  
Sustainable Use ◽  
River System ◽  
Series Data ◽  
System Modelling ◽  
Hydrological Simulation ◽  
Modelling Framework ◽  
Murray Darling Basin

Global climate change and local development make water supply one of the most vulnerable sectors in Australia. The Australian government has therefore commissioned a series of projects to evaluate water availability and the sustainable use of water resources in Australia. This paper discusses a river system modelling platform that has been used in some of these nationally significant projects. The platform consists of three components: provenance, modelling engine and reporting database. The core component is the modelling engine, an agent-based hydrological simulation system called the Integrated River System Modelling Framework (IRSMF). All configuration information and inputs to IRSMF are recorded in the provenance component so that modelling processes can be reproduced and results audited. The reporting database is used to store key statistics and raw output time series data for selected key parameters. This river system modelling platform has for the first time modelled a river system at the basin level in Australia. It provides practitioners with a unique understanding of the characteristics and emergent behaviours of river systems at the basin level. Although the platform is purpose-built for the Murray-Darling Basin, it would be easy to apply it to other basins by using different river models to model agent behaviours.

Modeling Interactive Behaviors While Learning With Digitized Objects in Virtual Reality Environments

2020 ◽  
pp. 215-234
Author(s):  
Eric Poitras ◽  
Kirsten R. Butcher ◽  
Matthew P. Orr
Keyword(s):  
Learning Environments ◽  
Time Series Data ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Virtual Learning ◽  
Virtual Learning Environments ◽  
Series Data ◽  
Student Behaviors ◽  
Virtual Models ◽  
Interaction Behaviors

This chapter outlines a framework for automated detection of student behaviors in the context of virtual learning environments. The components of the framework establish several parameters for data acquisition, preprocessing, and processing as a means to classify different types of behaviors. The authors illustrate these steps in training and evaluating a detector that differentiates between students' observations and functional behaviors while students interact with three-dimensional (3D) virtual models of dinosaur fossils. Synthetic data were generated in controlled conditions to obtain time series data from different channels (i.e., orientation from the virtual model and remote controllers) and modalities (i.e., orientation in the form of Euler angles and quaternions). Results suggest that accurate detection of interaction behaviors with 3D virtual models requires smaller moving windows to segment the log trace data as well as features that characterize orientation of virtual models in the form of quaternions. They discuss the implications for personalized instruction in virtual learning environments.

A Novel Method to Improve the Accuracy of Grey Prediction Model in Economic Prediction

2013 ◽  
Vol 347-350 ◽  
pp. 3331-3335
Author(s):  
Qian Ru Wang ◽  
Xi Wei Chen ◽  
Da Shi Luo ◽  
Yu Feng Wei ◽  
Li Ya Jin ◽  
...  
Keyword(s):  
System Theory ◽  
Time Series Data ◽  
Grey System Theory ◽  
Series Data ◽  
Model Parameters ◽  
Economic Data ◽  
Grey System ◽  
Forecasting Accuracy ◽  
Highly Nonlinear ◽  
The Impact

Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular and non-stationary. Many models based on grey system theory could adapt to various economic time series data. However, some of these models didnt consider the impact of the model parameters, or only considered a simple change of the model parameters for the prediction. In this paper, we proposed the PSO based GM (1, 1) model using the optimized parameters in order to improve the forecasting accuracy. The experiment shows that PSO based GM (1, 1) gets much better forecasting accuracy compared with other widely used grey models on the actual chaotic economic data.

Cosinor Analysis for Temperature Time Series Data of Long Duration

2007 ◽  
Vol 9 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Nikhil S. Padhye ◽  
Sandra K. Hanneman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Model Fit ◽  
Series Data ◽  
Model Parameters ◽  
Cosinor Analysis ◽  
Long Time ◽  
Improved Model ◽  
Data Length ◽  
Cosinor Models

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

Relationship between Environmental Degradation, Resource Scarcity, and Civil Conflicts in Somalia

2019 ◽  
Vol 10 (3) ◽  
pp. 640 ◽  
Author(s):  
Abdinur Ali MOHAMED ◽  
Ahmed Ibrahim NAGEYE
Keyword(s):  
Environmental Degradation ◽  
Time Series Data ◽  
Arable Land ◽  
Civil Conflict ◽  
Resource Scarcity ◽  
Least Square ◽  
Series Data ◽  
Model Parameters ◽  
Explanatory Variables ◽  
Civil Conflicts

The purpose of this study was to examine relationship between environmental degradation, resource scarcity, and civil conflict in Somalia. Environmental degradation is disposed to increase the number of disputes emerging from duel over the scarce resources. Consequently, it makes the society such offensive that it is inclined to armed conflict. In this study we investigated five variables in which civil conflict was the dependent variable. Population growth, land degradation, water resource and the climate change were explanatory variables. Time series data, 1990-2015, from various sources was employed. Regression methods, Ordinary Least Square was used to estimate the model parameters. Augmented Dickey-Fuller test was used to examine stationary of the data as Johansen cointegration was used to detect the long run relation between the study variables. The study found that one million increase of the rural population will lead the likelihood of the civil conflicts by about 1.04%. The decline of every one hector of arable land will cause the likelihood of the civil conflict to increase by about 1.5%. The rise of the one kilometer cubic of fresh water decreases the likelihood of the civil conflicts to about 4.49%. Rise of the temperature came to be insignificant and has no contribution to the civil conflicts in Somalia.

scholarly journals Pemodelan Harga Beras di Pulau Sumatera dengan Menggunakan Model Generalized Space Time ARIMA

2018 ◽  
Vol 2 (2) ◽  
pp. 49-57
Author(s):  
Dwi Yulianti ◽  
I Made Sumertajaya ◽  
Itasia Dina Sulvianti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Space Time ◽  
Series Data ◽  
Model Parameters ◽  
Autoregressive Integrated Moving Average ◽  
Sumatra Island ◽  
Rice Price ◽  
Multiple Variables

Generalized space time autoregressive integrated  moving average (GSTARIMA) model is a time series model of multiple variables with spatial and time linkages (space time). GSTARIMA model is an extension of the space time autoregressive integrated moving average (STARIMA) model with the assumption that each location has unique model parameters, thus GSTARIMA model is more flexible than STARIMA model. The purposes of this research are to determine the best model and predict the time series data of rice price on all provincial capitals of Sumatra island using GSTARIMA model. This research used weekly data of rice price on all provincial capitals of Sumatra island from January 2010 to December 2017. The spatial weights used in this research are the inverse distance and queen contiguity. The modeling result shows that the best model is GSTARIMA (1,1,0) with queen contiguity weighted matrix and has the smallest MAPE value of 1.17817 %.

scholarly journals Characterizing Sparse Asteroid Light Curves with Gaussian Processes

2021 ◽  
Vol 163 (1) ◽  
pp. 29
Author(s):  
Christina Willecke Lindberg ◽  
Daniela Huppenkothen ◽  
R. Lynne Jones ◽  
Bryce T. Bolin ◽  
Mario Jurić ◽  
...  
Keyword(s):  
Gaussian Processes ◽  
Time Series Data ◽  
Rotation Period ◽  
Light Curves ◽  
Series Data ◽  
Model Parameters ◽  
Wide Field ◽  
Pulse Profile ◽  
Rotation Periods ◽  
Period Detection

Abstract In the era of wide-field surveys like the Zwicky Transient Facility and the Rubin Observatory’s Legacy Survey of Space and Time, sparse photometric measurements constitute an increasing percentage of asteroid observations, particularly for asteroids newly discovered in these large surveys. Follow-up observations to supplement these sparse data may be prohibitively expensive in many cases, so to overcome these sampling limitations, we introduce a flexible model based on Gaussian processes to enable Bayesian parameter inference of asteroid time-series data. This model is designed to be flexible and extensible, and can model multiple asteroid properties such as the rotation period, light-curve amplitude, changing pulse profile, and magnitude changes due to the phase-angle evolution at the same time. Here, we focus on the inference of rotation periods. Based on both simulated light curves and real observations from the Zwicky Transient Facility, we show that the new model reliably infers rotational periods from sparsely sampled light curves and generally provides well-constrained posterior probability densities for the model parameters. We propose this framework as an intermediate method between fast but very limited-period detection algorithms and much more comprehensive but computationally expensive shape-modeling based on ray-tracing codes.

scholarly journals Control of Chaos in the Rössler System

1997 ◽  
Vol 50 (2) ◽  
pp. 263 ◽  
Author(s):  
Stuart Corney
Keyword(s):  
Periodic Orbits ◽  
Time Series Data ◽  
Control Method ◽  
Three Dimensional ◽  
Dynamical Variable ◽  
Series Data ◽  
External Parameter ◽  
Unstable Periodic Orbits ◽  
System A ◽  
Linear Differential

The control method of Ott, Grebogi and Yorke (1990) as applied to the Rössler system, a set of three-dimensional non-linear differential equations, is examined. Using numerical time series data for a single dynamical variable the method was successfully employed to control several of the unstable periodic orbits in a three-dimensional embedding of the data. The method also failed for a number of unstable periodic orbits due to difficulties in linearising about the orbit or the tangential coincidence of the stable manifold and the motion of the orbit with external parameter.

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