scholarly journals Towards a reasoned 1D river model calibration

2005 ◽  
Vol 7 (2) ◽  
pp. 91-104 ◽  
Author(s):  
Jean-Philippe Vidal ◽  
Sabine Moisan ◽  
Jean-Baptiste Faure ◽  
Denis Dartus
Keyword(s):  
Model Calibration ◽  
Expert Knowledge ◽  
Fixed Bed ◽  
Rainfall Runoff ◽  
Simulation Code ◽  
Hydraulic Simulation ◽  
Flow Equations ◽  
Knowledge Based ◽  
River Hydraulics ◽  
Knowledge Levels

Model calibration remains a critical step in numerical modelling. After many attempts to automate this task in water-related domains, questions about the actual need for calibrating physics-based models are still open. This paper proposes a framework for good model calibration practice for end-users of 1D hydraulic simulation codes. This framework includes a formalisation of objects used in 1D river hydraulics along with a generic conceptual description of the model calibration process. It was implemented within a knowledge-based system integrating a simulation code and expert knowledge about model calibration. A prototype calibration support system was then built up with a specific simulation code solving subcritical unsteady flow equations for fixed-bed rivers. The framework for model calibration is composed of three independent levels related, respectively, to the generic task, to the application domain and to the simulation code itself. The first two knowledge levels can thus easily be reused to build calibration support systems for other application domains, like 2D hydrodynamics or physics-based rainfall–runoff modelling.

scholarly journals Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration

2013 ◽  
Vol 10 (12) ◽  
pp. 14801-14855 ◽  
Author(s):  
S. Gharari ◽  
M. Hrachowitz ◽  
F. Fenicia ◽  
H. Gao ◽  
H. H. G. Savenije
Keyword(s):  
Parameter Identification ◽  
Expert Knowledge ◽  
Model Development ◽  
Distributed Model ◽  
Hydrological Process ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Knowledge Based ◽  
Runoff Modeling ◽  
Functional Units

Abstract. Conceptual environmental systems models, such as rainfall runoff models, generally rely on calibration for parameter identification. Increasing complexity of this type of model for better representation of hydrological process heterogeneity typically makes parameter identification more difficult. Although various, potentially valuable, strategies for better parameter identification were developed in the past, strategies to impose general conceptual understanding regarding how a catchment works into the process of parameterizing a conceptual model has still not been fully explored. In this study we assess the effect of imposing semi-quantitative, relational expert knowledge into the model development and parameter selection, efficiently exploiting the complexity of a semi-distributed model formulation. Making use of a topography driven rainfall-runoff modeling (FLEX-TOPO) approach, a catchment was delineated into three functional units, i.e. wetland, hillslope and plateau. Ranging from simplicity to complexity, three model set-ups, FLEXA, FLEXB and FLEXC have been developed based on these functional units. While FLEXA is a lumped representation of the study catchment, the semi-distributed formulations FLEXB and FLEXC introduce increasingly more complexity by distinguishing 2 and 3 functional units, respectively. In spite of increased complexity, FLEXB and FLEXC allow modelers to compare parameters as well as states and fluxes of their different functional units to each other. Based on these comparisons, expert knowledge based, semi-quantitative relational constraints have been imposed on three models structures. More complexity of models allows more imposed constraints. It was shown that a constrained but uncalibrated semi-distributed model, FLEXC, can predict runoff with similar performance than a calibrated lumped model, FLEXA. In addition, when constrained and calibrated, the semi-distributed model FLEXC exhibits not only higher performance but also reduced uncertainty for prediction, compared to the calibrated, lumped FLEXA model.

Survey of the State-of-the-Art Expert/Knowledge Based Systems in Civil Engineering.

1986 ◽  
Author(s):  
Simon S. Kim ◽  
Mary Lou Maher ◽  
Raymond E. Levitt ◽  
Martin F. Rooney ◽  
Thomas J. Siller
Keyword(s):  
Civil Engineering ◽  
Expert Knowledge ◽  
State Of The Art ◽  
The State ◽  
Knowledge Based Systems ◽  
Knowledge Based

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

scholarly journals Validity of the Diabetes, Hypertension and Hyperlipidaemia (DHL) Knowledge Instrument among Medical Students of Karachi

2016 ◽  
Vol 9 (1) ◽  
pp. 265
Author(s):  
Muhammad Bilal ◽  
Abdul Haseeb ◽  
Aleena Zehra Merchant ◽  
Muhammad Ahad Sher Khan ◽  
Arsalan Majeed Adam ◽  
...  
Keyword(s):  
Medical Education ◽  
Medical Students ◽  
Knowledge Score ◽  
Educational Status ◽  
Educational Programs ◽  
P Value ◽  
Medical College ◽  
Knowledge Based ◽  
The Mean ◽  
Knowledge Levels

BACKGROUND: While there have been a number of studies on DM, hypertension and hyperlipidaemia, an instrument which assesses knowledge based on all three conditions has neither been established nor authorized in Pakistan. Hence, the focus of this study was to establish a pre- tested extensive questionnaire to evaluate medical students’ understanding of DM, hypertension, hyperlipidaemia and their medications for use.METHODS: A pre-validated and pre-tested DHL instrument was employed on 250 students of Dow Medical and Sindh Medical College and on 45 physicians working in a leading teaching hospital of Karachi. The DHL knowledge instrument was then distributed a second time to the very same set of students, after a period of 2 months, at the end of the foundation module, once they had received some basic formal medical education including diabetes and CVS diseases.RESULTS: The overall internal consistency for the DHL instrument failed to comply with the set standard of more than or equal to 0.7 as our results yielded Cronbach’s α of 0.6. Overall the average difficulty factor of 28 questions is 0.41, which highlighted that the instrument was moderately tough. The mean scores for all domains were substantially lower in the students section in comparison to that of the professional section, which had remarkable impact on the overall mean(SD) knowledge score (40.58 ± 14.63 vs. 63.49 ± 06.67 ; p value = 0.00).CONCLUSION: The instrument can be used to recognize people who require educational programs and keep an account of the changes with the passage of time as it could help in differentiating the knowledge levels among its participants based on their educational status.

Heuristic Design of a Material Processing Component

1990 ◽  
Author(s):  
Vikram R. Jamalabad ◽  
Noshir A. Langrana ◽  
Yogesh Jaluria
Keyword(s):  
Heuristic Search ◽  
Expert Knowledge ◽  
Object Oriented ◽  
Engineering Process ◽  
Efficient Design ◽  
Cross Sectional ◽  
Design Algorithm ◽  
Initial Design ◽  
Knowledge Based ◽  
Processing Component

Abstract The main thrust of this research is in developing a knowledge-based system for the design of a mechanical engineering process. The study concentrates on developing methodologies for initial design and redesign in a qualitative format. The component selected is a die for plastic extrusion. A design algorithm using best first heuristic search and expert knowledge, both in procedural and declarative form, forms the core of the process. Initial design and redesign methodologies are presented that can enable efficient design of a component using expert knowledge. Some generality has been accomplished by the implementation of the techniques to dies of different cross sectional shapes. The software is written in Lisp within an object oriented software package using analysis modules written in C.

Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hossein Mokhtarian ◽  
Hesam Jafarian ◽  
Saoussen Dimassi ◽  
Shahriar Bakrani-Balani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Expert Knowledge ◽  
Fused Deposition ◽  
Knowledge Based ◽  
Deposition Modeling ◽  
Artificial Neural

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

Expert Knowledge-Based Traction Control of a Truck Using Fuzzy Logic

1996 ◽  
Vol 29 (1) ◽  
pp. 7867-7872
Author(s):  
Ka C. Cheok ◽  
Kazuyuki Kobayashi ◽  
Francis B. Hoogterp
Keyword(s):  
Fuzzy Logic ◽  
Expert Knowledge ◽  
Traction Control ◽  
Knowledge Based
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