Modelling uncertainty of flood quantile estimations at ungauged sites by Bayesian networks

2013 ◽  
Vol 16 (4) ◽  
pp. 822-838 ◽  
Author(s):  
D. Santillán ◽  
L. Mediero ◽  
L. Garrote
Keyword(s):  
Bayesian Networks ◽  
Explanatory Variables ◽  
Modelling Uncertainty ◽  
Ungauged Sites ◽  
Estimation Uncertainty ◽  
Linear Relationships ◽  
Flood Quantiles ◽  
Quantile Estimations ◽  
Probabilistic Nature

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, some of these models assume linear relationships between variables and prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks (BNs) were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharge as a result. The proposed BN model can be applied to supply the estimation uncertainty in national flood discharge mappings. The methodology was applied to a case study in the Tagus basin in Spain.

scholarly journals Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models

2010 ◽  
Vol 7 (4) ◽  
pp. 5413-5440 ◽  
Author(s):  
B. A. Botero ◽  
F. Francés
Keyword(s):  
Return Period ◽  
Robustness Analysis ◽  
Distribution Functions ◽  
Statistical Estimator ◽  
Systematic Information ◽  
Estimation Uncertainty ◽  
High Return ◽  
Probable Maximum Flood ◽  
Flood Quantiles

Abstract. This paper proposes the estimation of high return period quantiles using upper bounded distribution functions with Systematic and additional Non-Systematic information. The aim of the developed methodology is to reduce the estimation uncertainty of these quantiles, assuming the upper bound parameter of these distribution functions as a statistical estimator of the Probable Maximum Flood (PMF). Three upper bounded distribution functions, firstly used in Hydrology in the 90's (referred to in this work as TDF, LN4 and EV4), were applied at the Jucar River in Spain. Different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood (ML) method. Results show that it is possible to obtain a statistical estimate of the PMF value and to establish its associated uncertainty. The behaviour for high return period quantiles is different for the three evaluated distributions and, for the case study, the EV4 gave better descriptive results. With enough information, the associated estimation uncertainty is considered acceptable, even for the PMF estimate. From the robustness analysis, EV4 distribution function appears to be more robust than the GEV and TCEV unbounded distribution functions in a typical Mediterranean river and Non-Systematic information availability scenario.

IMF Stabilization Programs, Policy Conduct and Macroeconomic Outcomes: A Case Study of Pakistan

2006 ◽  
Vol 11 (1) ◽  
pp. 35-62
Author(s):  
Nawaz A. Hakro ◽  
Wadho Waqar Ahmed
Keyword(s):  
Current Account ◽  
Inflation Rate ◽  
Current Account Balance ◽  
Macroeconomic Policies ◽  
Explanatory Variables ◽  
Fiscal Balance ◽  
Macroeconomic Impacts ◽  
The Individual ◽  
The Imf

This study is designed to assess the macroeconomic performance of fund-supported programs, and the sequencing and ordering of macroeconomic policies in the context of the Pakistan economy. The generalized evaluation estimator technique has been used to assess the macroeconomic impacts of the IMF supported programs. GDP growth, inflation rate, current account balance, fiscal balance and unemployment are used as the target variables in order to gauge economic performance during the program years. The vector of policy variables (that might have been adopted in the absence of programs) and the vector of foreign exogenous variables are also taken as explanatory variables in the model, so that the individual effect of the IMF supported programs could be assessed. The result suggests that as the IMF prescriptions were applied, the current account balance has worsened, the unemployment rate has significantly increased, and the inflation rate has increased during the years of fund-supported programs. Only the budget balance has shown signs of improvement. Furthermore an inadequate sequencing of reforms has contributed to the further worsening of the economic scenario during the program period.

Probabilistic nature of storage delay parameter of the hydrologic model RORB: a case study for the Cooper's Creek catchment in Australia

2014 ◽  
Vol 46 (3) ◽  
pp. 400-410 ◽  
Author(s):  
Hitesh Patel ◽  
Ataur Rahman
Keyword(s):  
Hydrologic Model ◽  
Exceedance Probability ◽  
Design Flood ◽  
Flood Peak ◽  
Monte Carlo Simulation Technique ◽  
South Wales ◽  
Accurate Design ◽  
High Degree ◽  
Probabilistic Nature

In rainfall–runoff modeling, Design Event Approach is widely adopted in practice, which assumes that the rainfall depth of a given annual exceedance probability (AEP), can be converted to a flood peak of the same AEP by assuming a representative fixed value for the other model inputs/parameters such as temporal pattern, losses and storage-delay parameter of the runoff routing model. This paper presents a case study which applies Monte Carlo simulation technique (MCST) to assess the probabilistic nature of the storage delay parameter (kc) of the RORB model for the Cooper's Creek catchment in New South Wales, Australia. It has been found that the values of kc exhibit a high degree of variability, and different sets of plausible values of kc result in quite different flood peak estimates. It has been shown that a stochastic kc in the MCST provides more accurate design flood estimates than a fixed representative value of kc. The method presented in this study can be adapted to other catchments/countries to derive more accurate design flood estimates, in particular for important flood study projects, which require a sensitivity analysis to investigate the impacts of parameter uncertainty on design flood estimates.

Building Bayesian networks for legal evidence with narratives: a case study evaluation

2014 ◽  
Vol 22 (4) ◽  
pp. 375-421 ◽  
Author(s):  
Charlotte S. Vlek ◽  
Henry Prakken ◽  
Silja Renooij ◽  
Bart Verheij
Keyword(s):  
Bayesian Networks ◽  
Legal Evidence ◽  
Study Evaluation

scholarly journals Risk Modelling with Bayesian Networks - Case Study: Construction of Tunnel under the Dead Vistula River in Gdansk

2017 ◽  
Vol 196 ◽  
pp. 585-591 ◽  
Author(s):  
Marian W. Kembłowski ◽  
Beata Grzyl ◽  
Adam Kristowski ◽  
Agata Siemaszko
Keyword(s):  
Bayesian Networks ◽  
Risk Modelling ◽  
Vistula River ◽  
The Dead

Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment

2018 ◽  
Vol 233 (4) ◽  
pp. 593-604
Author(s):  
Amin Moniri-Morad ◽  
Mohammad Pourgol-Mohammad ◽  
Hamid Aghababaei ◽  
Javad Sattarvand
Keyword(s):  
Complex Systems ◽  
Cox Regression ◽  
Reliability Evaluation ◽  
Distribution Functions ◽  
Heterogeneous Environment ◽  
Mining Equipment ◽  
Data Set ◽  
Explanatory Variables ◽  
The Impact

Operational heterogeneity and harsh environment lead to major variations in production system performance and safety. Traditional probabilistic model is dealt with time-to-event data analysis, which does not have the capability of quantifying and simulation of these types of complexities. This research proposes an integrated methodology for analyzing the impact of dominant explanatory variables on the complex system reliability. A flexible parametric proportional hazards model is developed by focusing on standard parametric Cox regression model for reliability evaluation in complex systems. To achieve this, natural cubic splines are utilized to create a smooth and flexible baseline hazards function where the standard parametric distribution functions do not fit into the failure data set. A real case study is considered to evaluate the reliability for multi-component mechanical systems such as mining equipment. Different operational and environmental explanatory variables are chosen for the analysis process. Research findings revealed that precise estimation of the baseline hazards function is a major part of the reliability evaluation in heterogeneous environment. It is concluded that an appropriate maintenance strategy potentially mitigate the equipment failure intensity.

scholarly journals Predictive risk mapping of an environmentally-driven infectious disease using spatial Bayesian networks: A case study of leptospirosis in Fiji

2018 ◽  
Vol 12 (10) ◽  
pp. e0006857 ◽  
Author(s):  
Helen J. Mayfield ◽  
Carl S. Smith ◽  
John H. Lowry ◽  
Conall H. Watson ◽  
Michael G. Baker ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Bayesian Networks ◽  
Risk Mapping ◽  
Predictive Risk ◽  
Predictive Risk Mapping

scholarly journals Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

2019 ◽  
Vol 86 ◽  
pp. 276-286 ◽  
Author(s):  
Jinxin Wang ◽  
Zhongwei Wang ◽  
Viacheslav Stetsyuk ◽  
Xiuzhen Ma ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Diesel Fuel ◽  
Fuel Injection ◽  
Fault Isolation ◽  
Injection System ◽  
Fuel Injection System ◽  
Diesel Fuel Injection
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