Independent variable selection for regression modeling of the flow duration curve for ungauged basins in the United States

Fouad, Geoffrey; Loáiciga, Hugo A.

doi:10.1016/j.jhydrol.2020.124975

Cited by 15 publications

(4 citation statements)

References 45 publications

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“…Olson and Hawkins (2013) established an RF model to predict the continuous spatial variation of total P and total N in rivers of the western United States and predicted better than the previous physical model. Fouad and Loaiciga (2020) evaluated the regression models of the percentile ow in 918 basins of CONUS and believed that the prediction effect of the RF technique is superior to the baseline regression procedure.…”

Section: Bfi Prediction For Water Resource Planning and Managementmentioning

confidence: 99%

Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique

Huang

Dong

Zhang

et al. 2021

Stoch Environ Res Risk Assess

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Base ow plays a critical role in maintaining the aquatic environmental health. However, the driving factors and predictions of base ow have not been rigorously investigated on a large scale, partly preventing hydrologist from deeply understanding runoff generation. To this end, the Lyne-Hollick (LH) digital lter method and the automatic base ow identi cation technique (ABIT) were used to estimate the long-term and seasonal base ow index (BFI) of 619 catchments across Continental United States (CONUS) from 1981 to 2014. Six natural driving factors are selected from the 31 catchment attributes about topography and location, soil, geology, land cover, and climate characteristics. The Random Forest (RF) technique was used to predict the BFI with the selected six driving factors as predictors. Results show that the long-term average BFI was 0.49, and the BFI value was different in four seasons, with the highest value of 0.55 in winter and the lowest value of 0.46 in autumn. The forest fraction, clay proportion and snow fraction were the most powerful factors affecting the long-term average BFI. The RF technique predicts the BFI across the 619 sites in CONUS with a R 2 of 0.59 after Leave-One-Location cross-validation, which was more satisfactory than the multiple linear regression method. This study can provide a deep insight into the generation and variation of base ow and guide the annual base ow prediction for water resources management.

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Section: Bfi Prediction For Water Resource Planning and Managementmentioning

confidence: 99%

Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique

Huang

Dong

Zhang

et al. 2021

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…In that study, FS increased the interpretability of the final model while improving its performance. Seven FS methods were compared for river flow quantile estimation in ungauged basins (Fouad and Loáiciga, 2020). The authors found that the FS methods performed better than dimension reduction techniques (principal component analysis) to reduce multicollinearity in the feature subsets.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of feature selection methods for wind speed estimation at ungauged locations

Houndekindo

Ouarda

2023

Energy Conversion and Management

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“…Many researchers have addressed streamflow series tasks for different countries such as China, Canada, Ecuador, Iraq, Mozambique, United States, Serbia, Norway, Turkey, Sri-Lanka, and Brazil [2][3][4][5][6][7][8][9][10]. It highlights the importance of the problem to the global economy.…”

Section: Introductionmentioning

confidence: 99%

“…• Rendall et al [26]-extensive comparison of large scale data driven prediction methods based on VS and machine learning; • Marcjasz et al [27]-to electricity price forecasting; • Santi et al [28]-to predict mathematics scores of students; • Karim et al [29]-to predict post-operative outcomes of cardiac surgery patients; • Kim and Kang [30]-to faulty wafer detection in semiconductor manufacturing; • Furma ńczyk and Rejchel [31]-to high-dimensional binary classification problems; • Fouad and Loáiciga [5]-to predict percentile flows using inflow duration curve and regression models; • Ata Tutkun and Kayhan Atilgan [32]-investigated VS models in Cox regression, a multivariate model; • Mehmood et al [33]-compared several VS approaches in partial least-squares regression tasks;…”

mentioning

confidence: 99%

Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods

et al. 2020

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The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.

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Independent variable selection for regression modeling of the flow duration curve for ungauged basins in the United States

Cited by 15 publications

References 45 publications

Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique

Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique

Comparative study of feature selection methods for wind speed estimation at ungauged locations

Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods

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