•A novel dynamical-statistical approach for probabilistic drought forecasting is presented.•Initial condition uncertainty is explicitly characterized through ensemble DA.•A multivariate approach ...using copula functions is coupled with the ensemble DA.
In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initial condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The wind-excited vibrations of structures induce fluctuating stresses that may result in the accumulation of fatigue damage, ultimately posing a risk of structural failure. This paper presents the ...findings of a research program assessing the fatigue life of a 30 m high slender and tapered lightning pole under wind induced vibrations. A three-stage study has been conducted to understand the causes of fatigue damage. In the first step, a hybrid numerical and full-scale experimental investigation was carried out to identify the natural frequencies, modal shapes and modal damping ratios. In the second step, results from the dynamic identification test were used to estimate vortex shedding induced vibrations. Critical resonant conditions on the first and second vibration modes have been investigated, adopting standards and calculation techniques from literature. Finally, in the third step, the fatigue damage induced by vortex shedding vibrations was estimated. The findings demonstrate that the fatigue issues of the lightning rod are mainly related to the wind induced stress at the base of the pole, highlighting the contribution of vortex shedding resonant with the second vibration mode. The paper also discusses the large uncertainties affecting the analysis, showing that errors in parameter estimates give rise to very large scatter in the fatigue damage assessment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Real-time monitoring, is now the integral component in smart manufacturing with the rapid application of Artificial Intelligence (AI) in manufacturing. Machine Learning (ML) algorithms and Internet ...of things (IoT) make the volatility, uncertainty, complexity, and ambiguity world (VUCA) more reliable and resilient with the stable industrial environment. In this study, two machine learning algorithms such as K-mean clustering and support vector, are used in combination with IoT-enabled embedded devices to design, deploy and test the effectiveness of the vehicle assembly process in the VUCA context. To accomplish this, the design includes both real-time data and training vector data, which were collected from IoT-enabled devices and evaluated using ML algorithms leading to the novel element called Smart Safe Factor (SSF), a critical threshold indicator that helps in limiting different units in assembly line-ups from excess wastages and energy losses in real-time. Test results highlight the impact of AI in enhancing the productivity and efficiency. Using SSF, 21.84% of energy is saved during the entire assembly process and 8% of excess stocks in storage have been curtailed for monetary benefits. This study deliberates the applications of AI and ML algorithms in a Vehicle Assembly (VA) model, connecting critical parameters such as cost, performance, energy, and productivity.
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BFBNIB, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The increased penetration of Renewable Energy Sources (RES) as part of a decentralized and distributed power system makes net-load forecasting a critical component in the planning and operation of ...power systems. However, compared to the transmission level, producing accurate short-term net-load forecasts at the distribution level is complex due to the small number of consumers. Moreover, owing to the stochastic nature of RES, it is necessary to quantify the uncertainty of the forecasted net-load at any given time, which is critical for the real-world decision process. This work presents parameterized deep quantile regression for short-term probabilistic net-load forecasting at the distribution level. To be precise, we use a Deep Neural Network (DNN) to learn both the quantile fractions and quantile values of the quantile function. Furthermore, we propose a scoring metric that reflects the trade-off between predictive uncertainty performance and forecast accuracy. We evaluate the proposed techniques on historical real-world data from a low-voltage distribution substation and further assess its robustness when applied in real-time. The experiment's outcomes show that the resulting forecasts from our approach are well-calibrated and provide a desirable trade-off between forecasting accuracies and predictive uncertainty performance that are very robust even when applied in real-time.
•Model personalisation requires more than anatomical personalisation.•Model uncertainty and sensitivity are important considerations for clinical interpretation.•Model verification and validation are ...critical for trust underpinning clinical decision support.•The cardiovascular digital twin will support diagnosis and prognosis by responding continuously to increasing volumes of information collected as the individual goes about their daily life.
The aim of this position paper is to provide a brief overview of the current status of cardiovascular modelling and of the processes required and some of the challenges to be addressed to see wider exploitation in both personal health management and clinical practice. In most branches of engineering the concept of the digital twin, informed by extensive and continuous monitoring and coupled with robust data assimilation and simulation techniques, is gaining traction: the Gartner Group listed it as one of the top ten digital trends in 2018. The cardiovascular modelling community is starting to develop a much more systematic approach to the combination of physics, mathematics, control theory, artificial intelligence, machine learning, computer science and advanced engineering methodology, as well as working more closely with the clinical community to better understand and exploit physiological measurements, and indeed to develop jointly better measurement protocols informed by model-based understanding. Developments in physiological modelling, model personalisation, model outcome uncertainty, and the role of models in clinical decision support are addressed and ‘where-next’ steps and challenges discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The objective of this study is to systematically attribute sources of evapotranspiration uncertainty in a hydrologic model and accordingly propose a remote sensing‐based solution. Using Soil and ...Water Assessment Tool (SWAT) for three U.S. watersheds, representing different geophysical settings, this study first addresses the effects of parameter equifinality, energy‐related weather input uncertainty, and limited process representation on evapotranspiration simulation. Remotely sensed 8‐day total actual evapotranspiration (AET) from Moderate Resolution Imaging Spectroradiometer (MODIS) is used as a reference to evaluate the model outcome. Results indicate the likelihood of a pseudo‐accurate model showing high streamflow prediction skill despite severely erroneous spatiotemporal dynamics of AET. As a remedial measure, a hybrid daily potential evapotranspiration (PET) estimate, derived from MODIS, is directly ingested at each hydrologic response unit of the model to create a new configuration called SWAT‐PET. A key contribution is the modified SWAT source code that integrates the model (i.e., SWAT‐PET) with an automatic remote sensing data processor. The underlying notion is that remotely sensed PET works as a surrogate of actual vegetation dynamics, biophysical processes, and energy balance, without overruling the model's built‐in soil moisture accounting. Noticeably, increased accuracy of soil moisture, AET, and streamflow in SWAT‐PET, compared to independent sources of observations/reference estimates (i.e., field sensor, satellite, and gauge stations), approves the efficacy of the proposed approach toward improved physical consistency of hydrologic modeling. While the idea is tested for a past period, the ultimate goal is to improve near‐real‐time hydrologic forecasting once such PET estimates become available.
Key Points
It is highly likely for a hydrologic model to be pseudo‐accurate, with good streamflow accuracy but erroneous evapotranspiration and water balance
We developed an integrated framework to directly ingest remotely sensed potential evapotranspiration (PET) in the Soil and Water Assessment Tool (SWAT)
Remotely sensed PET works as a surrogate for actual vegetation and energy balance to noticeably improve SWAT's overall physical consistency and accuracy
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
False positive findings are a growing problem in many research literatures. We argue that excessive false positives often stem from model uncertainty. There are many plausible ways of specifying a ...regression model, but researchers typically report only a few preferred estimates. This raises the concern that such research reveals only a small fraction of the possible results and may easily lead to nonrobust, false positive conclusions. It is often unclear how much the results are driven by model specification and how much the results would change if a different plausible model were used. Computational model robustness analysis addresses this challenge by estimating all possible models from a theoretically informed model space. We use large-scale random noise simulations to show (1) the problem of excess false positive errors under model uncertainty and (2) that computational robustness analysis can identify and eliminate false positives caused by model uncertainty. We also draw on a series of empirical applications to further illustrate issues of model uncertainty and estimate instability. Computational robustness analysis offers a method for relaxing modeling assumptions and improving the transparency of applied research.
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BFBNIB, INZLJ, NMLJ, NUK, OILJ, PNG, SAZU, UKNU, UL, UM, UPUK, ZRSKP
Asset Score is United States’ national standardized rating system and tool to assess a building's energy-related systems. The tool models building energy use under standard operating conditions to ...enable fair comparisons of buildings. With basic building characteristics entered by users, the tool creates simplified EnergyPlus models. However, even with a reduced set of model inputs, data collection still remains a challenge for widespread adoption of this rating system. The commercial building market demands an even more simplified entry point to energy efficiency evaluation. This paper discusses a hybrid method that combines regression models with real-time simulations to allow users to enter as few as seven building characteristics to quickly assess the building performance before a full-scale analysis. Built upon large-scale building stock simulations, a Random Forest approach was used to develop a set of regression models for various building use types. The majority of the Asset Score inputs were sampled extensively and fed into regression models. Based on the minimum user inputs, the Asset Score tool infers the remaining building characteristics and queries a large set of energy use intensity (EUI) values to create a distribution of possible EUIs for the building using the regression analysis. The regression model also takes a user's confidence level into account by allowing user to modify or verify the defaults, if known. With additional user inputs, a regression model can be transferred to an energy model for a full-scale energy simulation. This streamlined assessment provides an easy entry point to Asset Score. It also enables users who manage a large number of buildings to screen and prioritize buildings that can benefit most from a more detailed evaluation and possible energy efficiency upgrades without intensive data collection.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The aim of this article is to argue about the transition from the risk society to the uncertainty society. In view of the fact that the pandemic from Covid 19 has shown that vulnerability could ...potentially become a permanent condition, it is appropriate to try to configure uncertainty by choosing a different epistemological key, capable, both to question some of the paradigms on which the organization of the current economic and social systems in industrialized countries insists and to define meanings usefull to build new models of global behavior. Without incurring the error of associating uncertainty with indeterminacy, the challenge inherent in the proposal of a sociology of uncertainty consists in a proof of refutability towards any kind of functionalist logic. Both with respect to analyzes supported by causal relationships and with reference to forms of cognitive rationality focused on the automatic absolutism of numbers, the sociology of uncertainty represents the heuristic bet in opposition to the determinism of any "simple" typology of rational thinking. Through the critical review of the dialectic within which risk sociology has elaborated most of its key concepts and suggested them to other disciplines, the sociology of uncertainty acquires an interesting interdisciplinary value. In addition to providing a meaningful and dynamic interpretation of reality, its interdisciplinary value is essential for assigning a specialized role to social research. Especially with regard to applied sociology, the issue of uncertainty allows to broaden the heuristic horizon and to combine sociology and economy to adopt an approach capable of keeping together the analysis of forms and processes of socialization with that of environmental problems and territorial, and to address the issue of the reduction of inequalities through solutions that guarantee the widening of participation and the increasing of deliberative practices. Upon a methodological approach based on much more awareness, the goal is the promotion of social learnig. This last is foundamental to allows sociology of uncertianty is the management of vulnerabilities in the view either of understanding and interpretation of social phenomena and of defining of local and global policies
Projections of precipitation and temperature from Global Climate Models (GCMs) are generally the basis for assessment of the impact of climate change on water resources. The reliability of such ...assessments, however, is questionable, since GCM projections are subject to uncertainties arising from inaccuracies in the models, greenhouse gas emission scenarios, and initial conditions (or ensemble runs) used. The purpose of the present study is to quantify these sources of uncertainties in future precipitation and temperature projections from GCMs. To this end, we propose a method to estimate a measure of the associated uncertainty (or error), the square root of error variance (SREV), that varies with space and time as a function of the GCM being assessed. The method is applied to estimate uncertainty in monthly precipitation and temperature outputs from six GCMs for the period 2001–2099. The results indicate that, for both precipitation and temperature, uncertainty due to model structure is the largest source of uncertainty. Scenario uncertainly increases, especially for temperature, in future due to divergence of the three emission scenarios analyzed. It is also found that ensemble run uncertainty is more important in precipitation simulation than in temperature simulation. Estimation of uncertainty in both space and time sheds lights on the spatial and temporal patterns of uncertainties in GCM outputs. The generality of this error estimation method also allows its use for uncertainty estimation in any other output from GCMs, providing an effective platform for risk‐based assessments of any alternate plans or decisions that may be formulated using GCM simulations.
Key Points
An error model is developed for future GCM projections
Uncertainity of GCM precipitation and temperature outputs were determined
GCM model uncertainity was found to be the largest source of uncertainity
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