scholarly journals Comment on Can assimilation of crowdsourced data in hydrological modelling improve flood prediction? by Mazzoleni et al. (2017)

2017 ◽  
Author(s):  
Daniele P. Viero
Keyword(s):  
Real Time ◽  
Data Uncertainty ◽  
Model Parameters ◽  
Design Strategies ◽  
Actual System ◽  
Recent Contribution ◽  
Flood Prediction ◽  
Irregular Frequency ◽  
Crowdsourced Data ◽  
The Impact

Abstract. In their recent contribution, Mazzoleni et al. (2017) investigated the integration of crowdsourced data (CSD) in hydrological models to improve the accuracy of real-time flood forecast. They showed that assimilation of CSD improves the overall model performance in all the considered case studies. The impact of irregular frequency of available crowdsourced data, and that of data uncertainty, were also deeply assessed. However, it has to be remarked that, in their work, the Authors used synthetic (i.e., not actually measured) crowdsourced data, because actual crowdsourced data were not available at the moment of the study. This point, briefly mentioned by the authors, deserves further discussion. In most real-world applications, rainfall-runoff models are calibrated using data from traditional sensors. Typically, CSD are collected at different locations, where semi-distributed models are not calibrated. In a context of equifinality and of poor identifiability of model parameters, the model internal states can hardly mimic the actual system states away from calibration points, thus reducing the chances of success in assimilating real (i.e., not synthetic) CSD. Additional criteria are given that are useful for the a-priori evaluation of crowdsourced data for real-time flood forecasting and, hopefully, to plan apt design strategies for both model calibration and collection of crowdsourced data.

scholarly journals Comment on “Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?” by Mazzoleni et al. (2017)

2018 ◽  
Vol 22 (1) ◽  
pp. 171-177 ◽  
Author(s):  
Daniele P. Viero
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Data Uncertainty ◽  
Model Structure ◽  
Hydrological Models ◽  
Flood Prediction ◽  
Irregular Frequency ◽  
Crowdsourced Data ◽  
Internal States ◽  
The Impact

Abstract. Citizen science and crowdsourcing are gaining increasing attention among hydrologists. In a recent contribution, Mazzoleni et al. (2017) investigated the integration of crowdsourced data (CSD) into hydrological models to improve the accuracy of real-time flood forecasts. The authors used synthetic CSD (i.e. not actually measured), because real CSD were not available at the time of the study. In their work, which is a proof-of-concept study, Mazzoleni et al. (2017) showed that assimilation of CSD improves the overall model performance; the impact of irregular frequency of available CSD, and that of data uncertainty, were also deeply assessed. However, the use of synthetic CSD in conjunction with (semi-)distributed hydrological models deserves further discussion. As a result of equifinality, poor model identifiability, and deficiencies in model structure, internal states of (semi-)distributed models can hardly mimic the actual states of complex systems away from calibration points. Accordingly, the use of synthetic CSD that are drawn from model internal states under best-fit conditions can lead to overestimation of the effectiveness of CSD assimilation in improving flood prediction. Operational flood forecasting, which results in decisions of high societal value, requires robust knowledge of the model behaviour and an in-depth assessment of both model structure and forcing data. Additional guidelines are given that are useful for the a priori evaluation of CSD for real-time flood forecasting and, hopefully, for planning apt design strategies for both model calibration and collection of CSD.

scholarly journals River flood prediction using fuzzy neural networks: an investigation on automated network architecture

2018 ◽  
Vol 2017 (1) ◽  
pp. 238-247 ◽  
Author(s):  
Usman T. Khan ◽  
Jianxun He ◽  
Caterina Valeo
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Possibility Theory ◽  
Flood Management ◽  
Fuzzy Neural Networks ◽  
Data Driven ◽  
Model Parameters ◽  
Flood Prediction ◽  
Fuzzy Neural ◽  
Estimation Of Model Parameters

Abstract Urban floods are one of the most devastating natural disasters globally and improved flood prediction is essential for better flood management. Today, high-resolution real-time datasets for flood-related variables are widely available. These data can be used to create data-driven models for improved real-time flood prediction. However, data-driven models have uncertainty stemming from a number of issues: the selection of input data, the optimisation of model architecture, estimation of model parameters, and model output. Addressing these sources of uncertainty will improve flood prediction. In this research, a fuzzy neural network is proposed to predict peak flow in an urban river. The network uses fuzzy numbers to account for the uncertainty in the output and model parameters. An algorithm that uses possibility theory is used to train the network. An adaptation of the automated neural pathway strength feature selection (ANPSFS) method is used to select the input features. A search and optimisation algorithm is used to select the network architecture. Data for the Bow River in Calgary, Canada are used to train and test the network.

scholarly journals Parametric Identification of the Models with Specified Quality Characteristics

2019 ◽  
Vol 29 (4) ◽  
pp. 480-495
Author(s):  
Olga G. Kantor ◽  
Semen I. Spivak ◽  
Nikolay D. Morozkin
Keyword(s):  
Experimental Data ◽  
Factor Model ◽  
Parametric Identification ◽  
Reaction Rate Constant ◽  
Quality Characteristics ◽  
Data Uncertainty ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Chemical Reaction Rate Constant ◽  
The Impact

Introduction. The model of a given structure should be identified based on the results of solving the problem of parametric identification. This model should provide the best possible the database development reproduction of the experimental data. The concept of “best” is not strictly structured. Therefore, the procedure for identifying such a model is subject to natural logic and includes the stages of data a determination of a set of acceptable models and subsequent selection of the best of them. If the set of acceptable models is large, the procedure for determining the best one can be time-consuming. In this regard, the development of methods for parametric identification, which at the stage of creating a set of acceptable models allows taking into account the qualitative aspects of the identified dependence, which are of interest to the researcher, is of particular importance. Materials and Methods. The set of acceptable methods in the problems of parametric identification largely depends on the type of the experimental data. Uncertainty for example, probabilistic and statistical methods are useful if the observed factors are random and subject to any law of probability distribution. If the conditions for the use of such methods are not met, it may be useful to present an approach based on identifying the boundaries of location of the model parameters that ensure the achievement of specified levels of quality characteristics. Results. The procedure of parametric identification of models is formalized. It is based on the use of maximum permissible parameter estimates and allows one to determining the set of parameter values that guarantee the achievement of the required qualitative level of experimental data description, including from the standpoint of analyzing the impact of changes in accord with requirements to the accuracy of their reproduction. The approbation of the developed method on the example of the construction of a one-factor model of chemical kinetics is presented. Discussion and Conclusion. It is shown that the obtained value of the chemical reaction rate constant, in accordance with the introduced criteria, provides acceptable accuracy, adequacy, and stability of the identified kinetic model. At the same time, the results of calculations revealed the information that can form the basis for planning experiments carried out in order to improve the accuracy of the experimental data.

scholarly journals Personalization of electro-mechanical models of the pressure-overloaded left ventricle: fitting of Windkessel-type afterload models

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190342 ◽  
Author(s):  
Laura Marx ◽  
Matthias A. F. Gsell ◽  
Armin Rund ◽  
Federica Caforio ◽  
Anton J. Prassl ◽  
...  
Keyword(s):  
Clinical Data ◽  
Strong Dependence ◽  
Pressure Overload ◽  
Left Ventricular ◽  
Data Uncertainty ◽  
Model Parameters ◽  
Input Uncertainty ◽  
Mechanical Models ◽  
Local Optimizer ◽  
The Impact

Computer models of left ventricular (LV) electro-mechanics (EM) show promise as a tool for assessing the impact of increased afterload upon LV performance. However, the identification of unique afterload model parameters and the personalization of EM LV models remains challenging due to significant clinical input uncertainties. Here, we personalized a virtual cohort of N  = 17 EM LV models under pressure overload conditions. A global–local optimizer was developed to uniquely identify parameters of a three-element Windkessel (Wk3) afterload model. The sensitivity of Wk3 parameters to input uncertainty and of the EM LV model to Wk3 parameter uncertainty was analysed. The optimizer uniquely identified Wk3 parameters, and outputs of the personalized EM LV models showed close agreement with clinical data in all cases. Sensitivity analysis revealed a strong dependence of Wk3 parameters on input uncertainty. However, this had limited impact on outputs of EM LV models. A unique identification of Wk3 parameters from clinical data appears feasible, but it is sensitive to input uncertainty, thus depending on accurate invasive measurements. By contrast, the EM LV model outputs were less sensitive, with errors of less than 8.14% for input data errors of 10%, which is within the bounds of clinical data uncertainty. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

scholarly journals Real-Time Closed-Loop Detection Method of vSLAM Based on a Dynamic Siamese Network

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7612
Author(s):  
Quande Yuan ◽  
Zhenming Zhang ◽  
Yuzhen Pi ◽  
Lei Kou ◽  
Fangfang Zhang
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Detection Method ◽  
Model Parameters ◽  
Training Strategy ◽  
Key Frame ◽  
Siamese Network ◽  
Localization And Mapping ◽  
Loop Detection ◽  
The Impact

As visual simultaneous localization and mapping (vSLAM) is easy disturbed by the changes of camera viewpoint and scene appearance when building a globally consistent map, the robustness and real-time performance of key frame image selections cannot meet the requirements. To solve this problem, a real-time closed-loop detection method based on a dynamic Siamese networks is proposed in this paper. First, a dynamic Siamese network-based fast conversion learning model is constructed to handle the impact of external changes on key frame judgments, and an elementwise convergence strategy is adopted to ensure the accurate positioning of key frames in the closed-loop judgment process. Second, a joint training strategy is designed to ensure the model parameters can be learned offline in parallel from tagged video sequences, which can effectively improve the speed of closed-loop detection. Finally, the proposed method is applied experimentally to three typical closed-loop detection scenario datasets and the experimental results demonstrate the effectiveness and robustness of the proposed method under the interference of complex scenes.

scholarly journals Road Artery Traffic Light Optimization with Use of the Reinforcement Learning

2014 ◽  
Vol 26 (2) ◽  
pp. 101-108 ◽  
Author(s):  
Rok Marsetič ◽  
Darja Šemrov ◽  
Marijan Žura
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Heuristic Method ◽  
Model Parameters ◽  
Traffic Light ◽  
Q Learning ◽  
Traffic Lights ◽  
Algorithm Comparison ◽  
Actuated Signal ◽  
The Impact

The basic principle of optimal traffic control is the appropriate real-time response to dynamic traffic flow changes. Signal plan efficiency depends on a large number of input parameters. An actuated signal system can adjust very well to traffic conditions, but cannot fully adjust to stochastic traffic volume oscillation. Due to the complexity of the problem analytical methods are not applicable for use in real time, therefore the purpose of this paper is to introduce heuristic method suitable for traffic light optimization in real time. With the evolution of artificial intelligence new possibilities for solving complex problems have been introduced. The goal of this paper is to demonstrate that the use of the Q learning algorithm for traffic lights optimization is suitable. The Q learning algorithm was verified on a road artery with three intersections. For estimation of the effectiveness and efficiency of the proposed algorithm comparison with an actuated signal plan was carried out. The results (average delay per vehicle and the number of vehicles that left road network) show that Q learning algorithm outperforms the actuated signal controllers. The proposed algorithm converges to the minimal delay per vehicle regardless of the stochastic nature of traffic. In this research the impact of the model parameters (learning rate, exploration rate, influence of communication between agents and reward type) on algorithm effectiveness were analysed as well.

scholarly journals Comparing impacts of parameter and spatial data uncertainty for a grid-based distributed watershed model

2016 ◽  
Vol 18 (6) ◽  
pp. 961-974 ◽  
Author(s):  
Younggu Her ◽  
Conrad Heatwole
Keyword(s):  
Land Cover ◽  
Parameter Uncertainty ◽  
Input Data ◽  
Hydrologic Model ◽  
Data Uncertainty ◽  
Model Parameters ◽  
Model Output ◽  
Model Errors ◽  
Land Cover Data ◽  
The Impact

Parameter uncertainty in hydrologic modeling is commonly evaluated, but assessing the impact of spatial input data uncertainty in spatially descriptive ‘distributed’ models is not common. This study compares the significance of uncertainty in spatial input data and model parameters on the output uncertainty of a distributed hydrology and sediment transport model, HYdrology Simulation using Time-ARea method (HYSTAR). The Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm was used to quantify parameter uncertainty of the model. Errors in elevation and land cover layers were simulated using the Sequential Gaussian/Indicator Simulation (SGS/SIS) techniques and then incorporated into the model to evaluate their impact on the outputs relative to those of the parameter uncertainty. This study demonstrated that parameter uncertainty had a greater impact on model output than did errors in the spatial input data. In addition, errors in elevation data had a greater impact on model output than did errors in land cover data. Thus, for the HYSTAR distributed hydrologic model, accuracy and reliability can be improved more effectively by refining parameters rather than further improving the accuracy of spatial input data and by emphasizing the topographic data over the land cover data.

The impact of real-time continuous glucose monitoring on metabolic control in children with type 1 diabetes

2020 ◽  
Author(s):  
Ruxandra Calapod Ioana ◽  
Irina Bojoga ◽  
Duta Simona Gabriela ◽  
Ana-Maria Stancu ◽  
Amalia Arhire ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Metabolic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
The Impact

THE IMPACT OF TV ADVERTISING ON VIRAL: ANALYSIS USING REAL-TIME AD RATINGS

2019 ◽  
Vol 2019 ◽  
pp. 790-791
Author(s):  
Cunhyeong Ci ◽  
◽  
Hyo-Gyoo Kim ◽  
Seungbae Park ◽  
Heebok Lee
Keyword(s):  
Real Time ◽  
Tv Advertising ◽  
The Impact

778-P: The Impact of Self-Management Blood Glucose (SMBG) Real-Time Decision Support on Glycemic Control in Adult with T1D in China

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 778-P
Author(s):  
ZIYU LIU ◽  
CHAOFAN WANG ◽  
XUEYING ZHENG ◽  
SIHUI LUO ◽  
DAIZHI YANG ◽  
...  
Keyword(s):  
Decision Support ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Real Time ◽  
Self Management ◽  
The Impact
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