The Experts below are selected from a list of 7665 Experts worldwide ranked by ideXlab platform
B. Papadopoulos - One of the best experts on this subject based on the ideXlab platform.
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A hybrid probabilistic bi-sector fuzzy regression based methodology for normal distributed hydrological variable
Evolving Systems, 2019Co-Authors: M. Spiliotis, P. Angelidis, B. PapadopoulosAbstract:An advantage of the probabilistic approach is the exploitation of the observed Probability values in order to test the goodness-of-fit for the examined Theoretical Probability distribution function (pdf). Since, in fact, the interest of the engineers is to achieve a relation between the hydrological variable and the corresponding Probability which corresponds to a selected return period, a fuzzy linear relation between the standardized normal variable Z and the examined hydrologic random variable is achieved in condition that the hydrological variable is normally distributed. In this article, primary, the implementation of the fuzzy linear regression of Tanaka is proposed regarding the annual cumulative precipitation. Thus, all the historical data will be included in the produced fuzzy band. However, since many times the question is about the inverse process, that is, the determination of the return period for a given hydrological value, then, for this purpose, a fuzzy bi-sector regression is developed. The proposed bi-sector fuzzy regression incorporates the inclusion property regarding the produced fuzzy band as the fuzzy regression of Tanaka does. The proposed innovative methodology provides the opportunity to achieve simultaneously a fuzzy assessment of the mean value and the standard deviation based on the solution of the fuzzy linear regression. To test the suitability of the produced fuzzy band, several measures are proposed which incorporate the magnitude of the produced fuzzy band and the comparison between the estimated fuzzy mean value and standard deviation with the unbiased crisp estimation of the same variables and the median of the sample.
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A Hybrid Fuzzy Regression-Based Methodology for Normal Distribution (Case Study: Cumulative Annual Precipitation)
2018Co-Authors: Mike Spiliotis, Panagiotis Angelidis, B. PapadopoulosAbstract:An advantage of the probabilistic approach is the exploitation of the observed Probability values in order to test the goodness-of-fit for the examined Theoretical Probability distribution function (pdf). Since in fact, the interest of the engineers is to determine the hydrological variable which corresponds to a selected return period, a fuzzy linear relation between the standardized normal variable Z and the examined hydrologic random variable is achieved in condition that the hydrological variable is normally distributed. In this work, for the first time, the implementation of the fuzzy linear regression of Tanaka is proposed, to achieve a fuzzy relation between the standardized variable Z and the annual cumulative precipitation. Thus, all the historical data are included in the produced fuzzy band. The proposed innovative methodology provides the opportunity to achieve simultaneously a fuzzy assessment of the mean value and the standard deviation based on the solution of the fuzzy linear regression. The suitability test of the examined Theoretical pdf is founded on the comparison of the spread of the fuzzy band and the distance between the achieved central values of the mean value and the standard deviation with the unbiased statistical estimation of the same variables.
J. Lavabre - One of the best experts on this subject based on the ideXlab platform.
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Coupled rainfall model and discharge model for flood frequency estimation
Water Resources Research, 2002Co-Authors: P. Arnaud, J. LavabreAbstract:A method was designed to study frequency distributions of hydrologic variables (rainfall and discharge) which combined a stochastic model for hourly rainfall with a general conceptual model for transforming rainfall into discharge. The model generates many different flood events over a given simulation period to evaluate hydrologic risks. The SHYPRE (Simulated HYdrographs for flood Probability Estimation) method's was based on the use of observations to describe phenomena and statistically reproduce them. Frequency distributions of hydrological variables are built empirically from simulated rainfall and flood events. Extrapolation of these frequency distributions towards rare frequencies is performed by generating very different events over a long simulation period, rather than by directly fitting a Theoretical Probability distribution on observed values. This method yields an original estimation of flood quantiles from common to rare frequencies and provides complete temporal data about these floods. In addition, the approach supplies more stable estimates of flood quantiles than statistical distributions fitted on observed values, even for frequent events. This is due to a better integration of rainfall data and the parametric design stability of the two models (rainfall model and rainfall-discharge model).
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Using a stochastic model for generating hourly rainfall and a rainfall-runoff transformation model for flood frequency estimation
Journal of Water Science Revue des Sciences de l'Eau, 2000Co-Authors: P. Arnaud, J. LavabreAbstract:A statistically-based approach has been developed to study frequency distributions of hydrologic variables which uses a stochastic model for generating hourly rainfall with a rainfall-runoff model. The method generates a lot of different flood events over a given simulation period to evaluate hydrologic risks. This method, named Simulated HYdrographs for flood Probability Estimation (SHYPRE) is based on the use of observations to describe hydrological phenomena and is able to reproduce them statistically. Frequency distributions of hydrological variables are built empirically from generated rainfall and flood events. Extrapolation of these frequency distributions towards rare frequencies is performed by simulations over a longer simulation period, rather than by directly fitting a Theoretical Probability distribution on observed values. This method yields an different estimation of flood quantiles from common to rare frequencies and provides complete temporal data about these floods. Moreover, the approach provides more stable estimates of flood quantiles than statistical distributions fitted on observed values, even for frequent events. This is due to a better use of rainfall data and to the parametric design stability of the two models (rainfall model and rainfall-runoff model).
M. Spiliotis - One of the best experts on this subject based on the ideXlab platform.
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A hybrid probabilistic bi-sector fuzzy regression based methodology for normal distributed hydrological variable
Evolving Systems, 2019Co-Authors: M. Spiliotis, P. Angelidis, B. PapadopoulosAbstract:An advantage of the probabilistic approach is the exploitation of the observed Probability values in order to test the goodness-of-fit for the examined Theoretical Probability distribution function (pdf). Since, in fact, the interest of the engineers is to achieve a relation between the hydrological variable and the corresponding Probability which corresponds to a selected return period, a fuzzy linear relation between the standardized normal variable Z and the examined hydrologic random variable is achieved in condition that the hydrological variable is normally distributed. In this article, primary, the implementation of the fuzzy linear regression of Tanaka is proposed regarding the annual cumulative precipitation. Thus, all the historical data will be included in the produced fuzzy band. However, since many times the question is about the inverse process, that is, the determination of the return period for a given hydrological value, then, for this purpose, a fuzzy bi-sector regression is developed. The proposed bi-sector fuzzy regression incorporates the inclusion property regarding the produced fuzzy band as the fuzzy regression of Tanaka does. The proposed innovative methodology provides the opportunity to achieve simultaneously a fuzzy assessment of the mean value and the standard deviation based on the solution of the fuzzy linear regression. To test the suitability of the produced fuzzy band, several measures are proposed which incorporate the magnitude of the produced fuzzy band and the comparison between the estimated fuzzy mean value and standard deviation with the unbiased crisp estimation of the same variables and the median of the sample.
J. Olsson - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of a scaling cascade model for temporal rain- fall disaggregation
Hydrology and Earth System Sciences Discussions, 1998Co-Authors: J. OlssonAbstract:The possibility of modelling the temporal structure of rainfall in southern Sweden by a simple cascade model is tested. The cascade model is based on exact conservation of rainfall volume and has a branching number of 2. The weights associated with one branching are 1 and 0 with Probability P(1/0), 0 and 1 with P(0/1), and Wx/x, and 1 - Wx/x, 0 < Wx/x, < 1, with P(x/x), where Wx/x is associated with a Theoretical Probability distribution. Furthermore, the probabilities p are assumed to depend on two characteristics of the rainy time period (wet box) to be branched: rainfall volume and position in the rainfall sequence. In the first step, analyses of 2 years of 8-min data indicates that the model is applicable between approximately 1 hour and 1 week with approximately uniformly distributed Wx/x values. The probabilities P show a clear dependence on the box characteristics and a slight seasonal nonstationarity. In the second step, the model is used to disaggregate the time series from 17- to 1-hour resolution. The model-generated data reproduce well the ratio between rainy and nonrainy periods and the distribution of individual volumes. Event volumes, event durations, and dry period lengths are fairly well reproduced, but somewhat underestimated, as was the autocorrelation. From analyses of power spectrum and statistical moments the model preserves the scaling behaviour of the data. The results demonstrate the potential of scaling-based approaches in hydrological applications involving rainfall disaggregation.
P. Arnaud - One of the best experts on this subject based on the ideXlab platform.
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Coupled rainfall model and discharge model for flood frequency estimation
Water Resources Research, 2002Co-Authors: P. Arnaud, J. LavabreAbstract:A method was designed to study frequency distributions of hydrologic variables (rainfall and discharge) which combined a stochastic model for hourly rainfall with a general conceptual model for transforming rainfall into discharge. The model generates many different flood events over a given simulation period to evaluate hydrologic risks. The SHYPRE (Simulated HYdrographs for flood Probability Estimation) method's was based on the use of observations to describe phenomena and statistically reproduce them. Frequency distributions of hydrological variables are built empirically from simulated rainfall and flood events. Extrapolation of these frequency distributions towards rare frequencies is performed by generating very different events over a long simulation period, rather than by directly fitting a Theoretical Probability distribution on observed values. This method yields an original estimation of flood quantiles from common to rare frequencies and provides complete temporal data about these floods. In addition, the approach supplies more stable estimates of flood quantiles than statistical distributions fitted on observed values, even for frequent events. This is due to a better integration of rainfall data and the parametric design stability of the two models (rainfall model and rainfall-discharge model).
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Using a stochastic model for generating hourly rainfall and a rainfall-runoff transformation model for flood frequency estimation
Journal of Water Science Revue des Sciences de l'Eau, 2000Co-Authors: P. Arnaud, J. LavabreAbstract:A statistically-based approach has been developed to study frequency distributions of hydrologic variables which uses a stochastic model for generating hourly rainfall with a rainfall-runoff model. The method generates a lot of different flood events over a given simulation period to evaluate hydrologic risks. This method, named Simulated HYdrographs for flood Probability Estimation (SHYPRE) is based on the use of observations to describe hydrological phenomena and is able to reproduce them statistically. Frequency distributions of hydrological variables are built empirically from generated rainfall and flood events. Extrapolation of these frequency distributions towards rare frequencies is performed by simulations over a longer simulation period, rather than by directly fitting a Theoretical Probability distribution on observed values. This method yields an different estimation of flood quantiles from common to rare frequencies and provides complete temporal data about these floods. Moreover, the approach provides more stable estimates of flood quantiles than statistical distributions fitted on observed values, even for frequent events. This is due to a better use of rainfall data and to the parametric design stability of the two models (rainfall model and rainfall-runoff model).
