Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Ebtehaj, Isa; Bonakdari, Hossein; Deo, Ravinesh C.; Mehr, Ali Danandeh; Mohtar, Wan Hanna Melini Wan; Diop, Lamine; El-Shafie, Ahmed; Singh, Vijay P.

doi:10.1016/j.jhydrol.2017.09.007

Cited by 219 publications

(68 citation statements)

References 93 publications

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“…Their application at the scale of the upper Senegal and Gambia River Basins is difficult due to the complexity of the hydrogeological context, as well as the scarcity of hydrogeological data. New soft computing techniques [49] may be an alternative to improve streamflow modeling and forecasting in these areas. It is hoped that future research efforts will focus in these directions to explore the application of these different methods to see if they can bring added value in the simulation of flows.…”

Section: Discussionmentioning

confidence: 99%

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

et al. 2018

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This research investigated the effect of climate change on the two main river basins of Senegal in West Africa: the Senegal and Gambia River Basins. We used downscaled projected future rainfall and potential evapotranspiration based on projected temperature from six General Circulation Models (CanESM2, CNRM, CSIRO, HadGEM2-CC, HadGEM2-ES, and MIROC5) and two scenarios (RCP4.5 and RCP8.5) to force the GR4J model. The GR4J model was calibrated and validated using observed daily rainfall, potential evapotranspiration from observed daily temperature, and streamflow data. For the cross-validation, two periods for each river basin were considered: 1961-1982 and 1983-2004 for the Senegal River Basin at Bafing Makana, and 1969Makana, and -1985Makana, and and 1986Makana, and -2000 for the Gambia River Basin at Mako. Model efficiency is evaluated using a multi-criteria function (F agg ) which aggregates Nash and Sutcliffe criteria, cumulative volume error, and mean volume error. Alternating periods of simulation for calibration and validation were used. This process allows us to choose the parameters that best reflect the rainfall-runoff relationship. Once the model was calibrated and validated, we simulated streamflow at Bafing Makana and Mako stations in the near future at a daily scale. The characteristic flow rates were calculated to evaluate their possible evolution under the projected climate scenarios at the 2050 horizon. For the near future (2050 horizon), compared to the 1971-2000 reference period, results showed that for both river basins, multi-model ensemble predicted a decrease of annual streamflow from 8% (Senegal River Basin) to 22% (Gambia River Basin) under the RCP4.5 scenario. Under the RCP8.5 scenario, the decrease is more pronounced: 16% (Senegal River Basin) and 26% (Gambia River Basin). The Gambia River Basin will be more affected by the climate change.

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Section: Discussionmentioning

confidence: 99%

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

et al. 2018

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“…An integration of the classical multilayer perceptron with FFA for pan evaporation process is given in [47]. River flow forecasting for tropical environment was established using the ANFIS-FFA model [48]. On the same region of the case study implemented in this research, the water quality index prediction model was developed based on the hybridization of the support vector machine model with the firefly algorithm [49].…”

Section: Complexitymentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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e capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. e classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. is current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. e results are evaluated based on several statistical measures and graphical presentations. e river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

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“…where O i : observed DO, O m : average of observed DO, X i : predicated value of DO, X m : average predicated value of DO, N: number of datapoints, MAE: mean absolute error, RMSE: root-mean-square error, and R: determination of coefficient [76,77]. Table 3 presents the correlation coefficient between DO and other water quality parameters.…”

Section: Case Studymentioning

confidence: 99%

“…Heddam and Kisi et al (2017) showed that the regression methods without accurate estimation of some unknown parameters in their structure have a weaker performance compared with other AI methods such as ANN. Hence, the regression methods can be improved by the optimization of model parameters [77][78][79]. Other studies in the literature have also showed that the application of regression models with heuristic methods can improve model performance [80].…”

Section: Sensitivity Analysis Of Bat Algorithm Parametersmentioning

confidence: 99%

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ehteram

Sharafati

et al. 2018

Water

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Abstract:The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.

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Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

Cited by 219 publications

References 93 publications

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

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