Joint frequency analysis and uncertainty estimation of coupled rainfall–runoff series relying on historical and simulated data

Dodangeh, Esmaeel; Shahedi, Kaka; Pham, Binh Thai; Solaimani, Karim

doi:10.1080/02626667.2019.1704762

Cited by 5 publications

(5 citation statements)

References 58 publications

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“…Moreover, the results of the ARIMA model confirm that rainfall is an important variable in predicting streamflow and that accurate Q t prediction using only the lag times of the streamflow is impossible. Dodangeh et al (2018Dodangeh et al ( , 2019 predicted the streamflow of the Taleghan catchment using the lumped model IHACRES and the HSPF model with the same data as in the present study.…”

Section: Model Inputsmentioning

confidence: 84%

“…Also, in the current study, the effect of different input variables on the accuracy of predictions is investigated. Moreover, the results of the present study are compared with the results of Dodangeh et al (2018Dodangeh et al ( , 2019, who predicted streamflow using IHACRES (a lumped hydrological model) and Hydrologic Simulation Program-Fortran (HSPF) (a semidistributed hydrological model), and with those of Noor et al (2014), who used the Soil and Water Assessment Tool (SWAT; a semi-distributed hydrological model) with the same dataset. The outcomes of this study will bring new insights and possible improvements in the modelling of streamflow.…”

Section: Introductionmentioning

confidence: 87%

“…The mean annual temperature is 9.5°C, and the average maximum and minimum temperatures are 27.5° C and −11.3°C, respectively. The land use/land cover consists of poor rangeland (about 85%), dry farmland, orchards (close to the river), and bare land (Dodangeh et al 2019).…”

Section: Study Areamentioning

confidence: 99%

See 2 more Smart Citations

Improving daily stochastic streamflow prediction: comparison of novel hybrid data-mining algorithms

Khosravi

Booij

Barzegar

et al. 2021

Hydrological Sciences Journal

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In the current paper, the efficiency of three new standalone data-mining algorithms [M5 Prime (M5P), Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of bagging (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with an autoregressive integrated moving average (ARIMA) model as a benchmark. The models used precipitation (P) and streamflow (Q) data from the period 1979-2012 for training and validation (70% and 30% of data, respectively). Different input combinations were prepared using both P and Q with different lag times. The best input combination proved to be that in which all of the the data were used (i.e. R and Q -with lag times). Overall, employing Q with different lag times proved to be more effective than using only P as input for streamflow prediction. Although all models showed very good predictive power, BA-M5P outperformed the other models.

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Section: Model Inputsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 87%

See 1 more Smart Citation

Improving daily stochastic streamflow prediction: comparison of novel hybrid data-mining algorithms

Khosravi

Booij

Barzegar

et al. 2021

Hydrological Sciences Journal

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show abstract

“…From a computational point of view, NNs are powerful at solving high dimensional problems because of their processing capabilities in parallel [19,117,118]. Based on the various advantages mentioned previously, the NN model has been employed in the past for predicting the failures of structural elements [32][33][34][35][36][119][120][121][122].…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

Self Cite

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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“…It has high adaptability and is widely used in hydrological and water resources application fields, such as frequency analysis, risk analysis, stochastic simulation of rainfall, floods, droughts, etc. [19][20][21][22][23] Golian et al employed the copula method to study the joint probability distribution of rainfall depth and peak flow [24]; Bacchi and Balistrocchi proposed a criterion for deriving flood frequency curves from rainfall and duration distribution, and evaluated the return period of bivariate rainfall events [25]; Zhang et al utilized copula functions to focus on the relationship between groundwater depth changes and three selected control factors from a probability perspective, and compared the differences between the results of the two-dimensional and three-dimensional copula functions [18]; Requena et al applied the copula approach to perform multivariate flood frequency analyses, and the uncertainty involved in selecting copula to characterize the dependency structure of short data sequences is analyzed [26]; Chang et al constructed a two-dimensional copula drought risk model of drought duration and severity, and the drought risk of Weihe River Basin was fully assessed [27]; Li et al constructed a model of runoff and sediment over two stations in the Anning watershed 2010~2015 using the copula analysis method, analyzing the synchronous and asynchronous probabilities of runoff and sediment from both time and space perspectives [28]; Dodangeh et al established a joint probabilistic model of the extreme rainfall-runoff, using annual maximum precipitation, corresponding historical and simulated runoff data [29]; Salvadori et al introduced a framework for multivariate copula for handing multivariate disaster scenarios and evaluated the threat probability of natural disasters [30].…”

Section: Introductionmentioning

confidence: 99%

Characteristic Analysis and Uncertainty Assessment of the Joint Distribution of Runoff and Sediment: A Case Study of the Huangfuchuan River Basin, China

Huang

Qiu

2023

Water

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Exploring the relationship between runoff and sediment elements in a river basin is a prerequisite for realizing the scientific management scheme of runoff and sediment. In this study, six commonly applied probability distributions are utilized to fit the marginal distribution, and three Archimedes copulas are used to fit the joint distribution to build a joint probability distribution model of river runoff and sediment in sandy areas. The synchronous and asynchronous encounter probabilities of runoff and sediment are calculated. The uncertainties of marginal distribution, parameter estimation, and copula function in the process of constructing the joint distribution model framework are analyzed. The results indicate that: (1) The runoff and sediment series from 1954 to 2015 of the Huangfuchuan River basin are divided into three stages by using the cumulative anomaly method and the double mass curve method, and the runoff and sediment in the three stages have strong correlations. In the Ta (1954–1978) and Tb (1979–1996) stages, the optimal joint distribution functions of runoff and sediment are Gumbel, and in the Tc (1997–2015) stage the optimal joint distribution function is Clayton; (2) The synchronous probabilities of runoff and sediment series in the three stages are 69.84%, 84.82%, and 70.72%, respectively, which are much greater than the asynchronous frequencies of abundance and depletion, and this showed that the conditions of runoff and sediment in the river basin are consistent; (3) The joint distribution function is sensitive to the choice of marginal distributions, parameters, and copula functions, and the optimal marginal distribution function, optimal copula function, and the parameters selected by the maximum likelihood estimation method can better fit the runoff-sediment relationship in the river basin and reduce the process uncertainty.

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Joint frequency analysis and uncertainty estimation of coupled rainfall–runoff series relying on historical and simulated data

Cited by 5 publications

References 58 publications

Improving daily stochastic streamflow prediction: comparison of novel hybrid data-mining algorithms

Improving daily stochastic streamflow prediction: comparison of novel hybrid data-mining algorithms

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Characteristic Analysis and Uncertainty Assessment of the Joint Distribution of Runoff and Sediment: A Case Study of the Huangfuchuan River Basin, China

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