An Ensemble Flow Forecast Method Based on Autoregressive Model and Hydrological Uncertainty Processer

In the process of hydrological forecasting, there are uncertainties in data input, model parameters, and model structure, which cause a deterministic forecasting to fail to provide useful risk information to decision-makers. Therefore, the study of ensemble forecasting and the analysis of hydrological uncertainty are of great significance to guide the actual operation of reservoirs in the flood season. This study proposed a Bayesian ensemble forecast method, comprising of a Gaussian mixture model (GMM), a hydrological uncertainty processer (HUP), and an Autoregressive (AR) model. First, the GMM is selected as the marginal distribution function to estimate the uncertainty of observed and modelled data. Next, the AR model is used to correct the forecast rainfall data. Then, a modified HUP is used to deal with the uncertainty of hydrological model structure and rainfall input data. In the end, the ensemble flow forecast results are composed of the expected values of the posterior distribution obtained by HUP under different rainfall conditions. Taking the Three Gorges Reservoir (TGR) as a case study, the ensemble flow prediction in the forecast period is calculated by using the above method. Results show that the method proposed in this paper can improve the accuracy of runoff forecasts and reduce the uncertainty of the hydrological forecast.

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Interpreting the Estimates from the Full VECH Model with Asymmetry: The Case of US and Canadian Equity Prices

Accounting and Finance Research ◽

10.5430/afr.v6n4p236 ◽

2017 ◽

Vol 6 (4) ◽

pp. 236

Author(s):

Chikashi Tsuji

Keyword(s):

Parameter Estimates ◽

Model Parameters ◽

Model Structure ◽

Equity Prices ◽

Related Research ◽

Asymmetric Effects ◽

Equity Index ◽

Autoregressive Conditional Heteroscedasticity ◽

Full Vector

This paper attempts to derive careful interpretation of the parameter estimates from one of the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models, the full vector-half (VECH) model with asymmetric effects. We also consider and interpret the parameter estimates from a case study of US and Canadian equity index returns by applying this model. More specifically, we firstly inspect the model formula and derive general interpretation of the model parameters. We consider this is particularly useful for understanding not only the full VECH model structure but also similar MGARCH models. After the general considerations, we also interpret the case results that are derived from our application of the full VECH model to US and Canadian equity index returns. We consider that these concrete illustrations are also very helpful for future related research.

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Estratégia Metodológica para Zoneamento pelo Potencial de Perda de Solo na Bacia Hidrográfica Cachoeira Cinco Veados, RS-Brasil

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v14.4.p2391-2402 ◽

2021 ◽

Vol 14 (4) ◽

pp. 2391-2402

Author(s):

Elenice Broetto Weiler ◽

Jussara Cabral Cruz ◽

Marília Ferreira Ferreira ◽

José Miguel Reichert ◽

Bruno Campos Mantovanelli ◽

...

Keyword(s):

Regional Planning ◽

Methodological Approach ◽

Decision Makers ◽

Model Parameters ◽

Hierarchical Analysis ◽

Thematic Map ◽

Priority Order ◽

Map Analysis ◽

Potential Soil Loss

The aim of this study was to propose a methodological approach to determine the best land use based on USLE model parameters, using the watershed as planning unit. The model USLE parameters were spatialized using the software ArcGis 10.5, for the case study of the Cachoeira Cinco Veados watershed, RS-Brazil, and the erosive values were categorized according to the methodology of Ribeiro (2006). We reclassified the areas in “suitable” and “not suitable” to the tested use, according to two limit-criteria adopted as maximum acceptable soil losses (20 and 50 t ha-1 year-1). The methodology consists of constructing two strategies: the first is a construction of a thematic map, considering a priority order of uses in the watershed, where the most spendthrift use was analyzed first (Script of Hierarchical Analysis among Uses); and the second consists of the construction of maps that correspond to scenarios with watershed areas suitable to a given use, according to the classification criterion used, and their crossing with the current use map (Analysis Script by Use). The results show it is possible to classify the areas in “suitable” and “not suitable” for a given use, allowing with this organizational strategy to identify, quantify and spatialize the areas in accordance with the limit of potential soil loss and point out those that do not tolerate the tested use. This is a useful information for decision makers when studying regional planning.

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Simultaneously determining global sensitivities of model parameters and model structure

Hydrology and Earth System Sciences ◽

10.5194/hess-24-5835-2020 ◽

2020 ◽

Vol 24 (12) ◽

pp. 5835-5858

Author(s):

Juliane Mai ◽

James R. Craig ◽

Bryan A. Tolson

Keyword(s):

Sensitivity Analysis ◽

Hydrological Modeling ◽

Structural Model ◽

Hydrologic Model ◽

Model Parameters ◽

Model Structure ◽

Modeling Framework ◽

Model Structure Uncertainty ◽

Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply the extended Sobol' sensitivity analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that (1) the xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability, while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration and uncertainty analysis. (4) The analysis of time-dependent process sensitivities regarding simulated streamflow is a helpful tool for understanding model internal dynamics over the course of the year.

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Bayesian facies inversion on a partially dolomitized isolated carbonate platform. A case study from Central Luconia province, Malaysia.

Geophysics ◽

10.1190/geo2020-0351.1 ◽

2020 ◽

pp. 1-47

Author(s):

George Ghon ◽

Dario Grana ◽

Eugene C. Rankey ◽

Gregor T. Baechle ◽

Florian Bleibinhaus ◽

...

Keyword(s):

Seismic Data ◽

Reservoir Characterization ◽

Carbonate Platform ◽

Gaussian Mixture ◽

Model Parameters ◽

Elastic Model ◽

Probabilistic Prediction ◽

S Wave ◽

Porosity Distribution

We present a case study of geophysical reservoir characterization where we use elastic inversion and probabilistic prediction to predict 9 carbonate lithofacies and the associated porosity distribution. The study focuses on an isolated carbonate platform of middle Miocene age, offshore Sarawak in Malaysia, which has been partly dolomitized — a process that increased porosity and permeability of the prolific gas reservoir. The 9 lithofacies are defined from one reference core and include a range of lithologies and pore types, covering limestone and dolomitized limestone, each with vuggy varieties, as well as sucrosic and crystalline dolomites with intercrystalline porosity, and also argillaceous limestones, and shales. To predict lithofacies and porosity from geophysical data, we adopt a probabilistic algorithm that employs Bayesian theory with an analytical solution for conditional means and covariances of posterior probabilities, assuming a Gaussian mixture model. The inversion is a 2-step process, first solving for elastic model parameters P- and S-wave velocities and density from 2 partial seismic stacks. Subsequently, lithofacies and porosity are predicted from the elastic parameters in the borehole and across a 2-D inline. The final result is a model that consists of the pointwise posterior distributions of facies and porosity at each location where seismic data are available. The facies posterior distribution represents the facies proportions estimated from seismic data, whereas the porosity distribution represents the the probability density function at each location. These distributions provide the most likely model and its associated uncertainty for geological interpretations.

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Identification of LPV Models with Two Scheduling Variables Using Transition Test

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.1313 ◽

2014 ◽

Vol 543-547 ◽

pp. 1313-1317

Author(s):

Jiang Yin Huang ◽

Jing Zhao

Keyword(s):

Distillation Column ◽

Linear Models ◽

Batch Process ◽

Model Parameters ◽

Model Structure ◽

Model Accuracy ◽

Weighting Functions ◽

Identification Method ◽

Research Findings

This paper presents the research findings of identification method for LPV models with two scheduling variables using transition test. The LPV model is parameterized as blended linear models, which is also called as multi-model structure. Linear weighting functions are used as the local model weights and the Gauss-Newton method is used to optimize the nonlinear LPV model parameters. Usefulness of the method is verified by modeling a high purity distillation column, the case study shows that the multi-model LPV models can yield a better model accuracy with respect to simulation outputs. The identification method proposed in this paper can be used in batch process identification.

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Simultaneously Determining Global Sensitivities of Model Parameters and Model Structure

10.5194/hess-2020-215 ◽

2020 ◽

Author(s):

Juliane Mai ◽

James R. Craig ◽

Bryan A. Tolson

Keyword(s):

Sensitivity Analysis ◽

Hydrological Modeling ◽

Structural Model ◽

Hydrologic Model ◽

Model Parameters ◽

Model Structure ◽

Modeling Framework ◽

Model Structure Uncertainty ◽

Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply an Extended Sobol' Sensitivity Analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that: (1) The xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration. (4) The analysis of time dependent process sensitivities regarding simulated streamflow is a helpful tool to understand model internal dynamics over the course of the year.

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Integrated Impact Assessment for Sustainable Hydropower Planning in the Vjosa Catchment (Greece, Albania)

Sustainability ◽

10.3390/su13031514 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1514

Author(s):

Rebecca Peters ◽

Jürgen Berlekamp ◽

Ana Lucía ◽

Vittoria Stefani ◽

Klement Tockner ◽

...

Keyword(s):

Impact Assessment ◽

Planning Process ◽

Renewable Energy Sources ◽

Decision Makers ◽

Catchment Scale ◽

Northern Greece ◽

Hydropower Plants ◽

Hydropower Planning

Mitigating climate change, while human population and economy are growing globally, requires a bold shift to renewable energy sources. Among renewables, hydropower is currently the most economic and efficient technique. However, due to a lack of impact assessments at the catchment scale in the planning process, the construction of hydropower plants (HPP) may have unexpected ecological, socioeconomic, and political ramifications in the short and in the long term. The Vjosa River, draining parts of Northern Greece and Albania, is one of the few predominantly free-flowing rivers left in Europe; at the same time its catchment is identified an important resource for future hydropower development. While current hydropower plants are located along tributaries, planned HPP would highly impact the free-flowing main stem. Taking the Vjosa catchment as a case study, the aim of this study was to develop a transferable impact assessment that ranks potential hydropower sites according to their projected impacts on a catchment scale. Therefore, we integrated established ecological, social, and economic indicators for all HPP planned in the river catchment, while considering their capacity, and developed a ranking method based on impact categories. For the Vjosa catchment, ten hydropower sites were ranked as very harmful to the environment as well as to society. A sensitivity analysis revealed that this ranking is dependent upon the selection of indicators. Small HPP showed higher cumulative impacts than large HPP, when normalized to capacity. This study empowers decision-makers to compare both the ranked impacts and the generated energy of planned dam projects at the catchment scale.

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Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research

Journal of Empirical Research on Human Research Ethics ◽

10.1177/15562646211002744 ◽

2021 ◽

pp. 155626462110027

Author(s):

Gabrielle Samuel ◽

Jenn Chubb ◽

Gemma Derrick

Keyword(s):

Artificial Intelligence ◽

Health Research ◽

Decision Makers ◽

Societal Impact ◽

The Past ◽

Research And Innovation ◽

The United Kingdom ◽

Responsible Research ◽

Past Half

The governance of ethically acceptable research in higher education institutions has been under scrutiny over the past half a century. Concomitantly, recently, decision makers have required researchers to acknowledge the societal impact of their research, as well as anticipate and respond to ethical dimensions of this societal impact through responsible research and innovation principles. Using artificial intelligence population health research in the United Kingdom and Canada as a case study, we combine a mapping study of journal publications with 18 interviews with researchers to explore how the ethical dimensions associated with this societal impact are incorporated into research agendas. Researchers separated the ethical responsibility of their research with its societal impact. We discuss the implications for both researchers and actors across the Ethics Ecosystem.

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Do different types of organizational trust matter to employees’ commitment? A case study from Oman

Human Systems Management ◽

10.3233/hsm-201065 ◽

2020 ◽

pp. 1-11

Author(s):

Moaz Gharib ◽

Kamaal Allil ◽

Omar Durrah ◽

Mohammed Alsatouf

Keyword(s):

Online Survey ◽

Employee Commitment ◽

Decision Makers ◽

Design Strategies ◽

Food Sector ◽

Final Sample ◽

Organisational Trust ◽

Positive Effect ◽

Practical Implications

PURPOSE: Trust is vital to all positive relationships. This empirical study explores the effect of three facets of organisational trust (trust in supervisors, in co-workers and in the organisation) on employee commitment in Salalah Mills Co. in the food industry in the Sultanate of Oman. METHODOLOGY: Data were collected via an online survey sent to all employees working in Salalah Mills Co., Oman. The final sample consisting of 102 responses with a response rate of 54 percent were analysed using multiple regression analysis. RESULTS: The findings revealed that two facets of organisational trust (trust in co-workers and trust in supervisors) were found to have a significant positive effect on employee commitment, while trust in the organisation was found to have no significant effect. PRACTICAL IMPLICATIONS: Trust in supervisors and trust in co-workers directly affect employee commitment. Therefore, managers should consider promoting both of these forms of trust to enhance employee commitment. VALUE: Although previous studies have examined the link between organisational trust and employee commitment, a focus on Oman and the food sector has been particularly rare, so this study offers new insights. The findings will help decision-makers on design strategies and policies to improve employee commitment through trust.

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Conformity of the NASADEM_HGT and ALOS AW3D30 DEM with the Altitude from the Brazilian Geodetic Reference Stations: A Case Study from Brazilian Cerrado

Sensors ◽

10.3390/s21092935 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2935

Author(s):

Giovana Maranhão Bettiol ◽

Manuel Eduardo Ferreira ◽

Luiz Pacheco Motta ◽

Édipo Henrique Cremon ◽

Edson Eyji Sano

Keyword(s):

Decision Makers ◽

Surface Model ◽

Brazilian Cerrado ◽

Digital Elevation ◽

Class B ◽

Crucial Information ◽

Error Standard Deviation ◽

Elevation Model ◽

Main Region

The Brazilian Cerrado (tropical savanna) is the second largest biome in South America and the main region in the country for agricultural production. Altitude is crucial information for decision-makers and planners since it is directly related to temperature that conditions, for example, the climatic risk of rainfed crop plantations. This study analyzes the conformity of two freely available digital elevation models (DEMs), the NASADEM Merged Digital Elevation Model Global 1 arc second (NASADEM_HGT) version 1 and the Advanced Land Observing Satellite Global Digital Surface Model (ALOS AW3D30), version 3.1, with the altitudes provided by 1695 reference stations of the Brazilian Geodetic System. Both models were evaluated based on the parameters recommended in the Brazilian Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD), which defines error tolerances according to eight different scales (from 1:1000 to 1:250,000) and classes A (most strict tolerance, for example, 0.17 m for 1:1000 scale), B, C, and D (least strict tolerance, for example, 50 m for 1:250,000 scale). Considering the class A, the NASADEM_HGT meets 1:250,000 and lower scales, while AW3D30 meets 1:100,000 and lower scales; for class B, NASADEM_HGT meets 1:100,000 scale and AW3D30 meets 1:50,000. AW3D30 presented lower values of root mean square error, standard deviation, and bias, indicating that it presents higher accuracy in relation to the NASADEM_HGT. Within eight of Cerrado’s municipalities with the highest grain production, the differences between average altitudes, measured by the Cohen’s effect size, were statistically insignificant. The results obtained by the PEC-PCD for the Cerrado biome indicate that both models can be employed in different DEM-dependent applications over this biome.

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