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Meta-learner methods in forecasting regulated and natural river flow

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Abstract

Monthly river flow forecasting has a vital role in many water resource management activities, especially in extreme events such as flood and drought. Therefore, experts need a reliable and precise model for forecasting. The ensemble machine learning (EML) models can provide more accurate results by combining coupled models. To this end, the chief aim of this study is to present multiple individual and integrated approaches to achieve more precise predictions. This study investigates the adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron neural network (MLPNN), and group method of data handling (GMDH) models along with EML models including stacking and averaging techniques for forecasting regulated and natural river flow. Multiple linear regression (MLR) and support vector regression (SVR) strategies are applied as meta-learner EMLs as well as simple and weighted averaging. Monthly river flow datasets were collected from the Nashua River and the St. John River in the USA evaluating all models. The models’ performances are compared using five standard evaluation indices such as the root mean square error, Nash–Sutcliffe efficiency, mean absolute error, mean index of agreement, and coefficient of determination (R2). In both modeling scenarios, the predicative stacking-SVR model followed by the simple averaging, weighted averaging, GMDH, and stacking-MLR acted appropriately with R2 of 0.996 in the Nashua River (regulated) and 0.909 in the St. John River (natural flow). The prediction results of the MLPNN and ANFIS models were almost similar. Overall, the EMLs produce more precise and reliable predictions, demonstrating the superiority of EML methodologies over individual models.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural networks

EFA:

Exploratory factor analysis

EGB:

Extreme gradient boosting

EML:

Ensemble machine learning

ENR:

Elastic net regression

GMDH:

Group method of data handling

IA:

Index of agreement

MAE:

Mean absolute error

ML:

Machine learning

MLPNN:

Multi-layer perceptron neural network

MLR:

Multiple linear regression

NSE:

Nash–Sutcliffe efficiency

Q :

Discharge

R 2 :

Coefficient of determination

RF:

Random forest

SA:

Simple averaging

SEM:

Stacking ensemble model

t :

Time

WA:

Weighted averaging

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Authors and Affiliations

  1. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Sareh Sayari & Mohammad Zounemat-Kermani

  2. Institute of Hydro-Engineering, Polish Academy of Sciences, Gdańsk, Poland

    Amin Mahdavi Meymand

  3. Telecommunication Engineering Department, College of Engineering, Ahlia University, Manama, 10878, Bahrain

    Ammar Aldallal

  4. Center of Excellence in Hydroinformatics, University of Tabriz, 29 Bahman Ave, Tabriz, Iran

    Mohammad Zounemat-Kermani

Authors
  1. Sareh Sayari
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  2. Amin Mahdavi Meymand
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  3. Ammar Aldallal
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  4. Mohammad Zounemat-Kermani
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Correspondence to Mohammad Zounemat-Kermani.

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Responsible Editor: Broder J. Merkel

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Sayari, S., Meymand, A.M., Aldallal, A. et al. Meta-learner methods in forecasting regulated and natural river flow. Arab J Geosci 15, 1051 (2022). https://doi.org/10.1007/s12517-022-10274-4

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