River flow modelling using nonparametric functional data analysis

Quintela-del-Rı́o, Alejandro; Francisco‐Fernández, Mario

doi:10.1111/jfr3.12282

Cited by 5 publications

(2 citation statements)

References 55 publications

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“…Having sampled such data type over time and space, they can be considered functional data and can be analyzed, modeled, visualized, and predicted via Functional Data Analysis (FDA) techniques (Quintela-del-Río & Francisco-Fernandez 2018). As a new area in Statistics, the FDA considers only data presented as curves.…”

Section: Research Motivation and Enthusiasmmentioning

confidence: 99%

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

Beyaztaş¹,

Shang²,

Yaseen³

2021

Preprint

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In this research, a functional time series model was introduced to predict future realizations of river flow time series. The proposed model was constructed based on a functional time series's correlated lags and the essential exogenous climate variables. Rainfall, temperature, and evaporation variables were hypothesized to have substantial functionality in river flow simulation. Because an actual time series model is unspecified and the input variables' significance for the learning process is unknown in practice, it was employed a variable selection procedure to determine only the significant variables for the model. A nonparametric bootstrap model was also proposed to investigate predictions' uncertainty and construct pointwise prediction intervals for the river flow curve time series. Historical datasets at three meteorological stations (Mosul, Baghdad, and Kut) located in the semi-arid region, Iraq, were used for model development. The prediction performance of the proposed model was validated against existing functional and traditional time series models. The numerical analyses revealed that the proposed model provides competitive or even better performance than the benchmark models. Also, the incorporated exogenous climate variables have substantially improved the modeling predictability performance. Overall, the proposed model indicated a reliable methodology for modeling river flow within the semi-arid region.

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Section: Research Motivation and Enthusiasmmentioning

confidence: 99%

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

Beyaztaş¹,

Shang²,

Yaseen³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, they are excellent for modeling the deterministic and algebraic parts of these variables, but they are not so useful for modeling the stochastic part of streamflow because of its nonlinear behavior. To overcome this issue, it would be worthwhile for experts to endeavor to use linear and non-linear time-series models (e.g., AR, ARMA, ARIMA, ARCH, Smooth Transition Autoregressive (STAR), Self-Exciting Threshold Autoregressive (SETAR), Bilinear models) to define the stochastic parts of models [23][24][25][26][27], which are widely used in predicting hydrological variables like precipitation [28,29], streamflow [30,31], and runoff [32,33].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

et al. 2020

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Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid “ARCH-DDM” models outperformed standalone models in predicting monthly streamflow.

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Analysis of milk production and greenhouse gas emissions as a function of extreme variations in forage production among French dairy farms

Kiessé¹,

Corson²,

Wilfart³

2022

Journal of Environmental Management

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River flow modelling using nonparametric functional data analysis

Cited by 5 publications

References 55 publications

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

Analysis of milk production and greenhouse gas emissions as a function of extreme variations in forage production among French dairy farms

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