Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Özgür; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

doi:10.1016/j.jhydrol.2016.09.035

Cited by 278 publications

(123 citation statements)

References 97 publications

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“…Estimating rainfall-runoff relationship and streamflow accurately is a significant element which should be considered for managing water resources effectively [1,2]. Hydrologic practices, including water supply and allocation, reservoir planning and operation, flood and drought management, and other hydrological applications, can be conducted successfully only when the rainfall-runoff relationship and streamflow behavior in a river watershed are estimated accurately.…”

Section: Introductionmentioning

confidence: 99%

“…The process-driven models are based on the physical interpretation of watershed system. These models are formulated utilizing complex physical equations and parametric assumptions [2]. Contrastively, the data-driven models characterize the relationship between input and output, not describing the natural watershed process [2,5].…”

Section: Introductionmentioning

confidence: 99%

“…These models are formulated utilizing complex physical equations and parametric assumptions [2]. Contrastively, the data-driven models characterize the relationship between input and output, not describing the natural watershed process [2,5]. The modeling simplicity and high accuracy have increased hydrologists' attention for rainfall-runoff and streamflow modeling based on the data-driven models.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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“…An extremely large smoothing factor induces oversmoothing and will typically cause most of the input patterns to appear similar, and an extremely small smoothing factor cannot provide a smooth regression surface (Yaseen et al 2016). In our study, an interactive algorithm was used to determine the intermediate value of the smoothing factor within the range of 0.01-1 (Šiljić et al 2015).…”

Section: Results Of Grnn Modelmentioning

confidence: 99%

“…The main difference between GRNN and traditional BPNN is the fixed architecture of the former for a given input-output data set and the requirement of determining an optimal number of hidden neurons in the latter (Yaseen et al 2016). GRNN can be …”

Section: General Regression Neural Networkmentioning

confidence: 99%

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Shang

Dahlgren

et al. 2017

Environ Sci Pollut Res

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Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the ruralsuburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

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The ecological–hydrological regime of the Han River basin under changing conditions: The coupled influence of human activities and climate change

Wang,

Yuan,

Yang

et al. 2024

Ecohydrology

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Because of the confluence of human activities and climate change, the hydrological regime in the Han River basin has substantially evolved, necessitating a multi‐faceted, quantitative analysis of the causative factors. Employing cross‐wavelet analysis, we examined nonlinear relationships between runoff and meteorological variables. Additionally, we assessed hydrological indicators via the IHA index and RVA, then quantified the drivers of runoff variations across different time scales using the Budyko hypothesis and a Generalized Regression Neural Network (GRNN) model. The findings reveal the presence of sustained resonance periods within the climate‐runoff system, notably concentrated in 9‐ to 15‐month intervals during the years 1985–1994, 1995–2012, and 2014–2018, with a confidence level of 95%. Overall, the basin exhibited moderate change (41.66%), with 15 indicators displaying varying degrees of moderate to high transformation. These shifts underscore significant ecosystem transformations. The influence of driving factors on runoff varies across temporal scales. On an annual scale, human activities predominantly shape runoff changes (52.35%), while meteorological factors contribute significantly (47.65%). Conversely, at the monthly scale, climate change emerges as the dominant influence on runoff patterns in June and September, with human activities maintaining a principal role in other months, notably exceeding 90% even in November.

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Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

Cited by 278 publications

References 97 publications

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

The ecological–hydrological regime of the Han River basin under changing conditions: The coupled influence of human activities and climate change

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