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Group method of data handling to forecast the daily water flow at the Cahora Bassa Dam

Abstract

The Zambezi watershed is essential for water supply, irrigation, fishing activities, and river transport of the populations of Southern Africa. The importance and variability of these water resources make it necessary to develop studies that may help understand and manage them. Despite this need, water resources studies for this region are still scarce. Therefore, the present work aims to present a strategy for forecasting the daily water flow of the Zambezi River in the Cahora Bassa dam, located in Mozambique, an important energy producer in the country and the fourth largest dam in Africa. Historical rainfall, evaporation, and humidity records collected from 2003 to 2011 are used for training and testing a model that forecasts water flow using the Group Method of Data Handling algorithm. The results achieved were compared, through error metrics, with those of other models to prove the effectiveness of the assembled model. They revealed that the proposed model achieves a satisfactory performance for the forecast horizon and could become a helpful tool in monitoring hydrographic basins and forecasting their daily streamflow values.

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Code Availability

Code can be obtained upon request from the authors.

Availability of data and materials

Data and materials can be obtained upon request from the authors.

Abbreviations

ANFIS:

Adaptive Neuro-Fuzzy Inference Systems

AI:

Artificial Intelligence

ARIMA:

Autoregressive Integrated Moving Average

ANN:

Artificial Neural Networks

E:

Evaporation

ELM:

Extreme Learning Machine

GEP:

Gene Expression Programming

GMDH:

Group Method of Data Handling algorithm

GP:

Gaussian Process

H:

Relative humidity

KGE:

Kling-Gupta Efficiency

MART:

Multiple Additive Regression Trees

MGN:

Morgan-Granger-Newbold

MLP:

Multi-layer Perceptron Regress

NSE:

Nash-Sutcliff Efficiency

Q:

Water Flow

R:

Rainfall

RF:

Random Forest

RMSE:

Root Mean Square Error

SARIMA:

Sazonal Autoregressive Integrated Moving Average

SVR:

Support Vector regressor

VKG:

Volterra-Kolmogorov-Gabo

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Funding

The authors acknowledge the support of the Computational Modeling Graduate Program at Federal University of Juiz de Fora (UFJF) and the Brazilian funding agencies CNPq - CoNSElho Nacional de Desenvolvimento Científico e Tecnológico (grants 429639/2016 and 401796/2021-3), FAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas Gerais (grant number APQ-00334/18), PROGRAD/UFJF - Pró-Reitoria de Graduação, GCUB/PROAFRI - Grupo Coimbra Universidades Brasileiras/Programa de formação de professores de educação superior de países africanos (finance code 001/2018) and CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, (finance code 001).

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Correspondence to Danilo P. M. Souza.

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Edited by Dr. Michael Nones (CO-EDITOR-IN-CHIEF).

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Souza, D.P.M., Martinho, A.D., Rocha, C.C. et al. Group method of data handling to forecast the daily water flow at the Cahora Bassa Dam. Acta Geophys. 70, 1871–1883 (2022). https://doi.org/10.1007/s11600-022-00834-3

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  • DOI: https://doi.org/10.1007/s11600-022-00834-3

Keywords

  • Group Modeling Data Handling
  • Water flow forecasting
  • Zambezi Basin
  • Machine Learning