scholarly journals Model complexity and choice of model approaches for practical simulations of CO2 injection, migration, leakage and long-term fate

2016 ◽  
Author(s):  
Michael A. Celia
Keyword(s):  
Model Complexity

scholarly journals A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting

2017 ◽  
Vol 11 (2) ◽  
pp. 343-389 ◽  
Author(s):  
Man Chung Fung ◽  
Gareth W. Peters ◽  
Pavel V. Shevchenko
Keyword(s):  
Monte Carlo ◽  
State Space ◽  
Stochastic Volatility ◽  
Model Complexity ◽  
Mortality Data ◽  
Period Effect ◽  
Male Mortality ◽  
Mortality Models ◽  
Volatility Model

AbstractThis paper explores and develops alternative statistical representations and estimation approaches for dynamic mortality models. The framework we adopt is to reinterpret popular mortality models such as the Lee–Carter class of models in a general state-space modelling methodology, which allows modelling, estimation and forecasting of mortality under a unified framework. We propose alternative model identification constraints which are more suited to statistical inference in filtering and parameter estimation. We then develop a class of Bayesian state-space models which incorporate a priori beliefs about the mortality model characteristics as well as for more flexible and appropriate assumptions relating to heteroscedasticity that present in observed mortality data. To study long-term mortality dynamics, we introduce stochastic volatility to the period effect. The estimation of the resulting stochastic volatility model of mortality is performed using a recent class of Monte Carlo procedure known as the class of particle Markov chain Monte Carlo methods. We illustrate the framework using Danish male mortality data, and show that incorporating heteroscedasticity and stochastic volatility markedly improves model fit despite an increase of model complexity. Forecasting properties of the enhanced models are examined with long-term and short-term calibration periods on the reconstruction of life tables.

scholarly journals A Mathematical Model of Gas and Water Flow in a Swelling Geomaterial—Part 1. Verification with Analytical Solution

Minerals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 30 ◽  
Author(s):  
Elias Ernest Dagher ◽  
Julio Ángel Infante Sedano ◽  
Thanh Son Nguyen
Keyword(s):  
Mathematical Model ◽  
Numerical Model ◽  
Analytical Solution ◽  
Analytical Solutions ◽  
Gas Flow ◽  
Numerical Models ◽  
Model Verification ◽  
Model Complexity ◽  
Gas Migration

Gas generation and migration are important processes that must be considered in a safety case for a deep geological repository (DGR) for the long-term containment of radioactive waste. Expansive soils, such as bentonite-based materials, are widely considered as sealing materials. Understanding their long-term performance as barriers to mitigate gas migration is vital in the design and long-term safety assessment of a DGR. Development and the application of numerical models are key to understanding the processes involved in gas migration. This study builds upon the authors’ previous work for developing a hydro-mechanical mathematical model for migration of gas through a low-permeable geomaterial based on the theoretical framework of poromechanics through the contribution of model verification. The study first derives analytical solutions for a 1D steady-state gas flow and 1D transient gas flow problem. Using the finite element method, the model is used to simulate 1D flow through a confined cylindrical sample of near-saturated low-permeable soil under a constant volume boundary stress condition. Verification of the numerical model is performed by comparing the pore-gas pressure evolution and stress evolution to that of the results of the analytical solution. The results of the numerical model closely matched those of the analytical solutions. Future studies will attempt to improve upon the model complexity and investigate processes and material characteristics that can enhance gas migration in a nearly saturated swelling geomaterial.

scholarly journals Understanding Crop Response to Climate Variability with Complex Agroecosystem Models

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Nathaniel K. Newlands ◽  
Gabriela Espino-Hernández ◽  
R. Scott Erickson
Keyword(s):  
Climate Variability ◽  
Crop Production ◽  
Crop Productivity ◽  
Data Availability ◽  
Prediction Skill ◽  
Model Complexity ◽  
Wheat Crop ◽  
Climate Conditions ◽  
Short Time

Agroecosystem modeling studies often rely on relatively short time-series historical records for training/tuning empirical parameters and to predict long-term variation in crop production associated with trends in climate and hydrological forcing. While ecosystem models may exhibit similar prediction skill in validation studies, their sensitivity to climate variability can differ significantly. Such discrepancy often arises due to the need to tradeoff model complexity with data availability. We examine the sensitivity in predicting spring wheat crop productivity across agricultural sites with differing soil and climate conditions where long-term agronomic and climate records are available. We report significant changes in the model sensitivity accompanying changing climatic regime. If not corrected for, this can lead to substantial predictive error when simulating across time and space. Our findings lend further support for a hierarchical (componentwise) approach for reducing model complexity and improving prediction skill.

Gaining local accuracy while not losing generality — extending the range of gap model applications

2009 ◽  
Vol 39 (6) ◽  
pp. 1092-1107 ◽  
Author(s):  
Markus Didion ◽  
Andrea D. Kupferschmid ◽  
Andreas Zingg ◽  
Lorenz Fahse ◽  
Harald Bugmann
Keyword(s):  
Leaf Area ◽  
Forest Succession ◽  
Light Availability ◽  
Predictive Ability ◽  
Swiss Alps ◽  
Model Complexity ◽  
Gap Model ◽  
Local Accuracy ◽  
Gap Models

For the study of long-term processes in forests, gap models generally sacrifice accuracy (i.e., simulating system behavior in a quantitatively accurate manner) for generality (i.e., representing a broad range of systems’ behaviors with the same model). We selected the gap model ForClim to evaluate whether the local accuracy of forest succession models can be increased based on a parsimonious modeling approach that avoids the additional complexity of a 3D crown model, thus keeping parameter requirements low. We improved the representation of tree crowns by introducing feedbacks between (i) light availability and leaf area per tree and (ii) leaf area per tree and diameter growth rate to account for the self-pruning in real stands. The local accuracy of the new model, ForClim v2.9.5, was considerably improved in simulations at three long-term forest research sites in the Swiss Alps, while its generality was maintained as shown in simulations of potential natural vegetation along a broad environmental gradient in Central Europe. We conclude that the predictive ability of a model does not depend on its complexity, but on the reproduction of patterns. Most importantly, model complexity should be consistent with the objectives of the study and the level of system understanding.

scholarly journals AI for Early Warning of Seasonal Infectious Disease: Shapely Additive Explanations Improves Prediction of Extraordinary West Nile virus Events in Europe

2020 ◽  
Author(s):  
Albert A Gayle
Keyword(s):  
Machine Learning ◽  
West Nile Virus ◽  
Early Warning ◽  
Complex Disease ◽  
Virus Disease ◽  
Resource Planning ◽  
Model Complexity ◽  
Maximum Temperature ◽  
West Nile

West Nile virus disease is a growing issue with devastating outbreaks and linkage to climate. It's a complex disease with many factors contributing to emergence and spread. High-performance machine learning models, such as XGBoost, hold potential for development of predictive models which performs well with complex diseases like West Nile virus disease. Such models furthermore allow for expanded ability to discover biological, ecological, social and clinical associations as well as interaction effects. In 1951, a deductive method based on cooperative game theory was introduced: Shapley values. The Shapley method has since been shown to be the only way to derive "true" effect estimations from complex systems. Up till recently, however, wide-scale application has been computationally prohibitive. Herein, we present a novel implementation of the Shapley method applied to machine learning to derive high-quality effect estimations. We set out to apply this method to study the drivers of and predict West Nile virus in Europe. Model validity was furthermore tested using observed information in the time periods following the prospective prediction window. We furthermore benchmarked results of XGBoost models against equivalently specified logistic regression models. High predictive performance was consistently observed. All models were statistically equivalent in terms of AUC performance (96.3% average). The top features across models were found to be vapor pressure, the autoregressive past year's feature, maximum temperature, wind speed, and local GNP. Moreover, when aggregated across quarters, we found that the effect of these features are broadly consistent across model configurations. We furthermore confirmed that for an equivalent level of model sophistication, XGBoost and logistic regressions performed similarly, with an advantage to XGBoost as model complexity increased. Our findings highlight the importance of ecological factors, such as climate, in determining outbreak risk of West Nile virus in Europe. We conclude by demonstrating the feasibility of same-year prospective early warning models that combine same-year observed climate with autoregressive geospatial covariates and long-term bioclimatic features. Scenario-based forecasts could likely be developed using similar methods, to provide for long-term intervention and resource planning, therefore increasing public health preparedness and resilience.

Therapy and prevention for mental health: What if mental diseases are mostly not brain disorders?

2019 ◽  
Vol 42 ◽  
Author(s):  
John P. A. Ioannidis
Keyword(s):  
Mental Health ◽  
Mental Disease ◽  
Brain Disorders ◽  
Social Stressors ◽  
Labor Education ◽  
Mental Diseases ◽  
Long Term Follow Up ◽  
Political Decisions

AbstractNeurobiology-based interventions for mental diseases and searches for useful biomarkers of treatment response have largely failed. Clinical trials should assess interventions related to environmental and social stressors, with long-term follow-up; social rather than biological endpoints; personalized outcomes; and suitable cluster, adaptive, and n-of-1 designs. Labor, education, financial, and other social/political decisions should be evaluated for their impacts on mental disease.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

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