A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling

2014 ◽  
Vol 54 ◽  
pp. 211-221 ◽  
Author(s):  
Haw Yen ◽  
Xiuying Wang ◽  
Darrell G. Fontane ◽  
R. Daren Harmel ◽  
Mazdak Arabi
Keyword(s):  
Data Model ◽  
Input Data ◽  
Watershed Modeling ◽  
Model Structure ◽  
Validation Data ◽  
Propagation Of Uncertainty

scholarly journals Identification of behavioural model input data sets for WWTP uncertainty analysis

2019 ◽  
Vol 81 (8) ◽  
pp. 1558-1568 ◽  
Author(s):  
E. Lindblom ◽  
U. Jeppsson ◽  
G. Sin
Keyword(s):  
Uncertainty Analysis ◽  
Data Model ◽  
Input Data ◽  
Treatment Plant ◽  
Data Sets ◽  
Model Structure ◽  
Sources Of Uncertainty ◽  
Behavioural Model ◽  
Plant Data ◽  
Parameter Values

Abstract Uncertainty analysis is important for wastewater treatment plant (WWTP) model applications. An important aspect of uncertainty analysis is the identification and proper quantification of sources of uncertainty. In this contribution, a methodology to identify an ensemble of behavioural model representations (combinations of input data, model structure and parameter values) is presented and evaluated. The outcome is a multivariate conditional distribution of input data that is used for generating samples of likely inputs (such as Monte Carlo input samples) to perform WWTP model uncertainty analysis. This article presents an approach to verify uncertainty distributions of input data (otherwise often assumed) by using historical observations and actual plant data.

scholarly journals Data management for developing digital twin ontology model

2020 ◽  
pp. 095440542097811
Author(s):  
Sumit Singh ◽  
Essam Shehab ◽  
Nigel Higgins ◽  
Kevin Fowler ◽  
Dylan Reynolds ◽  
...  
Keyword(s):  
Data Management ◽  
Data Model ◽  
Domain Knowledge ◽  
Conceptual Knowledge ◽  
Current Approach ◽  
Model Structure ◽  
Digital Twin ◽  
Ontology Model ◽  
Minimum Data ◽  
Novel Concept

Digital Twin (DT) is the imitation of the real world product, process or system. Digital Twin is the ideal solution for data-driven optimisations in different phases of the product lifecycle. With the rapid growth in DT research, data management for digital twin is a challenging field for both industries and academia. The challenges for DT data management are analysed in this article are data variety, big data & data mining and DT dynamics. The current research proposes a novel concept of DT ontology model and methodology to address these data management challenges. The DT ontology model captures and models the conceptual knowledge of the DT domain. Using the proposed methodology, such domain knowledge is transformed into a minimum data model structure to map, query and manage databases for DT applications. The proposed research is further validated using a case study based on Condition-Based Monitoring (CBM) DT application. The query formulation around minimum data model structure further shows the effectiveness of the current approach by returning accurate results, along with maintaining semantics and conceptual relationships along DT lifecycle. The method not only provides flexibility to retain knowledge along DT lifecycle but also helps users and developers to design, maintain and query databases effectively for DT applications and systems of different scale and complexities.

scholarly journals A Harmonized Data Model for Noise Simulation in the EU

2020 ◽  
Vol 9 (2) ◽  
pp. 121 ◽  
Author(s):  
Kavisha Kumar ◽  
Hugo Ledoux ◽  
Richard Schmidt ◽  
Theo Verheij ◽  
Jantien Stoter
Keyword(s):  
Real World ◽  
Data Model ◽  
Input Data ◽  
Assessment Methods ◽  
Research Centre ◽  
The European Union ◽  
Joint Research ◽  
Schema Design ◽  
Noise Assessment ◽  
The Eu

This paper presents our implementation of a harmonized data model for noise simulations in the European Union (EU). Different noise assessment methods are used by different EU member states (MS) for estimating noise at local, regional, and national scales. These methods, along with the input data extracted from the national registers and databases, as well as other open and/or commercially available data, differ in several aspects and it is difficult to obtain comparable results across the EU. To address this issue, a common framework for noise assessment methods (CNOSSOS-EU) was developed by the European Commission’s (EC) Joint Research Centre (JRC). However, apart from the software implementations for CNOSSOS, very little has been done for the practical guidelines outlining the specifications for the required input data, metadata, and the schema design to test the real-world situations with CNOSSOS. We describe our approach for modeling input and output data for noise simulations and also generate a real world dataset of an area in the Netherlands based on our data model for simulating urban noise using CNOSSOS.

scholarly journals The Branching Data Model as a New Level of Abstraction over the Relational Database

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 13
Author(s):  
H. Paul Zellweger
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Input Data ◽  
Tree Structure ◽  
Relational Data ◽  
Level Of Abstraction ◽  
Data Content

Information is often stored in the relational database. This technology is now fifty years old, but there remain patterns of relational data that have not yet been studied. The abstract presents a new data pattern called the branching data model (BDM). It represents the pure alignment of the table’s schema, its data content, and the operations on these two structures. Using a well-defined SELECT statement, an input data condition and its output values form a primitive tree structure. While this relationship is formed outside of the query, the abstract shows how we can view it as a tree structure within the table. Using algorithms, including AI, it goes on to show how this data model connects with others, within the table and between them, to form a new, uniform level of abstraction over the data throughout the database.

scholarly journals MODEL ESTIMASI DATA INTENSITAS RADIASI MATAHARI UNTUK WILAYAH BANTEN

2020 ◽  
Vol 21 (2) ◽  
pp. 53-61
Author(s):  
Munawar Munawar ◽  
Adi Mulsandi ◽  
Anistia Malinda Hidayat
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Data Model ◽  
Input Data ◽  
Mean Square ◽  
Environmental Prediction

Data intensitas radiasi matahari (Rs, MJ/m2/day) memiliki peran yang sangat penting dalam pemodelan cuaca dan iklim guna mengkuantifikasi panas yang dipertukarkan antara permukaan dan atmosfer. Namun, keterbatasan jumlah titik pengamatan intensitas radiasi matahari menjadikan pemodelan sebagai alternatif solusi yang relatif mudah dan murah untuk pengambilan data intensitas radiasi. Penelitian ini bertujuan untuk mengevaluasi performa model dalam mengestimasi nilai intensitas radiasi matahari di wilayah penelitian menggunakan dua pendekatan model yang berbeda, yaitu model empiris oleh Keiser, Arkansas (AR) dan model deterministik. Tiga variabel utama cuaca yang digunakan sebagai input data model adalah curah hujan (mm), suhu maksimum (°C), dan suhu minimum (°C). Kedua model tersebut dipilih karena dapat diterapkan dengan hanya melibatkan variabel utama atmosfer yang tersedia dalam waktu yang panjang di lokasi penelitian. Hasil prediksi yang dilakukan dengan model kemudian dibandingkan dengan data reanalisis National Centers for Environmental Prediction (NCEP) pada titik koordinat wilayah Stasiun Klimatologi Pondok Betung. Hasilnya menunjukkan performa model empirik lebih baik dalam menggambarkan variasi temporal dan prediksi variabel intensitas matahari dibandingkan model deterministik. Hal tersebut ditunjukkan dengan nilai korelasi yang cukup baik, yakni mencapai 0,72 (korelasi kuat) dan nilai Root Mean Square Error (RMSE) 2,0. Atas dasar hasil pemodelan yang cukup representatif di lokasi penelitian, analisis secara spasial kemudian diterapkan untuk skala wilayah yang lebih luas, yaitu Provinsi Banten. Berdasarkan tinjauan secara spasial di wilayah kajian, model empirik memiliki performa yang bervariasi di wilayah Provinsi Banten. Hasil prediksi intensitas radiasi matahari di wilayah bagian barat memiliki performa yang lebih baik dibandingkan wilayah bagian timur.  

scholarly journals Uncertainty in rainfall input data in a conceptual water balance model: effects on outputs and implications for predictability

2014 ◽  
Vol 18 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Enrique Muñoz ◽  
Pedro Tume ◽  
Gabriel Ortíz
Keyword(s):  
Input Data ◽  
Hydrological Model ◽  
Balance Model ◽  
Model Structure ◽  
Hydrological Models ◽  
Water Resources Planning ◽  
Levels Of Detail ◽  
Output Uncertainty ◽  
Main Input ◽  
Resources Planning

<p>As hydrological models become more prevalent in water resources planning and management, increasing levels of detail and precision are needed. Currently, reliable models that simulate the hydrological behavior of a basin are indispensable; however, it is also necessary to know the limits of the predictability and reliability of the model outputs. The present study evaluates the influence of uncertainty in the main input variable of the model, rainfall, on the output uncertainty of a hydrological model. Using concepts of identifiability and sensitivity, the uncertainty in the model structure and parameters was estimated. Then, the output uncertainty caused by uncertainties in i) the rainfall amounts and ii) the periods of the rainfall was determined. The main conclusion is that uncertainty in rainfall estimation during rainy periods produces greater output uncertainty. However, in non-rainy periods, the output uncertainty is not very sensitive to the uncertainty in rainfall. Finally, uncertainties in rainfall during the basin filling and emptying periods (Apr. – Jun. and Sep. – Nov., respectively) alter the uncertainty in subsequent periods. Therefore, uncertainties in these periods could result in limited ranges of model predictability.</p><p> </p><p><strong>Resumen</strong></p><p>Los modelos hidrológicos se han vuelto cada vez más necesarios en la planificación y gestión de recursos hídricos, donde un aumento en los niveles de detalle y precisión es necesario. Actualmente disponer de modelos para simular el comportamiento hidrológico de una cuenca resulta indispensable, sin embargo, también es necesario conocer los límites de predictibilidad y de confiabilidad de las salidas de un modelo. En este estudio se evalúan la influencia de la incertidumbre en la principal entrada de un modelo, la precipitación, sobre la incertidumbre de las salidas de un modelo hidrológico. Utilizando conceptos de identificabilidad y sensibilidad se estima la incertidumbre de los parámetros y estructura de un modelo. Luego, la incertidumbre en las salidas causadas por incertidumbre en i) los montos de precipitación, ii) los períodos de precipitación fue calculada. Como conclusiones se obtuvo que la incertidumbre en la estimación de la precipitación en períodos de lluvia produce mayor incertidumbre sobre las salidas. En períodos no lluviosos, la incertidumbre de las salidas es poco sensible a incertidumbre sobre las precipitaciones. Finalmente, incertidumbres en periodos de llenado y vaciado (Abril-Junio y Septiembre-Noviembre respectivamente) afectan la incertidumbre en las salidas en los períodos subsecuentes. Por lo tanto incertidumbres en aquellos períodos pueden resultar en rangos limitados de predictibilidad de un modelo.</p>

scholarly journals Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code

2020 ◽  
Author(s):  
Wouter Edeling ◽  
Arabnejad Hamid ◽  
Robert Sinclair ◽  
Diana Suleimenova ◽  
Krishnakumar Gopalakrishnan ◽  
...  
Keyword(s):  
Parametric Uncertainty ◽  
Model Structure ◽  
Validation Data ◽  
Government Interventions ◽  
Rapid Spread ◽  
Imperfect Knowledge ◽  
Model Structure Uncertainty ◽  
Scenario Uncertainty ◽  
The Uk ◽  
The Impact

Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus has rapidly spread worldwide since December 2019, and early modelling work of this pandemic has assisted in identifying effective government interventions. The UK government relied in part on the CovidSim model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London, to model various non-pharmaceutical intervention strategies, and guide its government policy in seeking to deal with the rapid spread of the COVID-19 pandemic during March and April 2020. CovidSim is subject to different sources of uncertainty, namely parametric uncertainty in the inputs, model structure uncertainty (i.e., missing epidemiological processes) and scenario uncertainty, which relates to uncertainty in the set of conditions under which the model is applied. We have undertaken an extensive parametric sensitivity analysis and uncertainty quantification of the current CovidSim code. From the over 900 parameters that are provided as input to CovidSim, we identified a key subset of 19 parameters to which the code output is most sensitive. We find that the uncertainty in the code is substantial, in the sense that imperfect knowledge in these inputs will be magnified to the outputs, up to the extent of ca. 300%. Most of this uncertainty can be traced back to the sensitivity of three parameters. Compounding this, the model can display significant bias with respect to observed data, such that the output variance does not capture this validation data with high probability. We conclude that quantifying the parametric input uncertainty is not sufficient, and that the effect of model structure and scenario uncertainty cannot be ignored when validating the model in a probabilistic sense.

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