Abstract
This work analyzes the use of artificial neural networks in the shortterm streamflow forecasting for large interconnected hydropower systems. The stateoftheart optimization algorithms, activation functions, and weight initialization techniques are investigated together with classic methods. We present an algorithm to define the neural network inputs in large hydrosystems and apply it to create models for 55 major hydro plants located in the ParanÃ¡ Basin, which contribute to more than 30% of the total power generated in Brazil. The paper also compares the performance of the neural networks with the hydrological models that are currently used by the independent system operator to define the dispatch of the electric power generators. Our results show that, overall, the neural network models provide more accurate forecasts than the hydrological models used by the Brazilian System Operator. Finally, the paper discusses the contributions of historical rainfall information in the forecasting of streamflow while using neural network models.
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Abbreviations
 Adam:

Adaptive moment estimation
 ANN:

Artificial neural network
 AR:

Autoregressive
 ARMA:

Autoregressive moving average
 CPU:

Central processing unit
 GD:

Gradient descent
 GDM:

Gradient descent with momentum
 GPU:

Graphics processing unit
 ISO:

Independent system operator
 IVS:

Input variable selection
 MAPE:

Mean absolute percentage error
 MGBIPH:

Largescale hydrological model
 MLP:

Multilayer perceptron
 MSE:

Mean square error
 NSE:

Nashâ€“Sutcliffe efficiency
 OWI:

Optimal weight initialization
 PAR:

Periodic autoregressive
 PARMA:

Periodic autoregressive moving average
 ReLU:

Rectified linear unit
 RMSprop:

Rootmeansquare propagation
 SMAP:

Soil moisture accounting procedure
 SVM:

Support vector machine
 VELMA:

Visualizing ecosystem land management assessments
 WI:

Weight initialization
 \({\text{In}}\) :

Set of input candidates
 \({\text{In}}_{{\text{S}}}^{*}\) :

Best set of streamflow inputs used in a specific ANN
 \({\text{Is}}_{{t_{{\text{I}}} }}\) :

Input streamflow candidate (\(s_{{\text{I}}}\), \(t_{{\text{I}}}\))
 \({\text{Ip}}_{{t_{{\text{I}}} }}^{{D_{{{\text{AR}}}} }}\) :

Input rainfall candidate accumulated through \(D_{{{\text{AR}}}}\) days (\(p_{{\text{I}}}\), \(t_{{\text{I}}}\)); \(D_{{{\text{AR}}}} = 1\): nonaccumulated rainfall
 \({\text{Os}}_{{t_{{\text{o}}} }}\) :

Output streamflow (\(s_{{\text{O}}}\), \(t_{{\text{O}}}\))
 \(p_{{\text{I}}} \in {\text{In}}\_ p\) :

Set of rainfall stations considered for input candidates
 \(s_{{\text{I}}} \in {\text{In}}\_s\) :

Set of streamflow stations considered for input candidates
 \(s_{{\text{O}}}\) :

Output streamflow station
 \(t_{{\text{I}}}\) :

Number of days preceding the first day of forecast
 \(t_{{\text{O}}}\) :

Day of forecast
 \(C\) :

Component of the cost function that penalizes the difference between \(y^{\left( i \right)}\) and \(\hat{y}^{\left( i \right)}\)
 \({\text{Cr}}\left( {A,B} \right)\) :

Function that computes the correlation coefficient between the variables \(A\) and \(B\)
 \(E_{{{\text{dev}}}} \left( k \right)\) :

Error in the development set until the \(k{\text{th}}\) epoch simulation
 \(J\) :

Cost function with \(\ell_{2}\) regularization
 \(M2\) :

Cost function (28)
 \(R2\) :

Cost function (29)
 \(U\left( {a_{1} ,a_{2} } \right)\) :

Continuous uniform distribution in the interval [\(a_{1}\), \(a_{2}\)]
 \(U_{{\text{D}}} \left( {a_{1} ,a_{2} } \right)\) :

Discrete uniform distribution in the interval [\(a_{1}\), \(a_{2}\)]
 \({\text{Var}}\left[ {w^{\ell } } \right]\) :

Variance of the weights in the \(\ell {\text{th}}\) layer
 \(w_{2}^{2}\) :

Square of the \(\ell_{2}\) norm of the weight matrixes
 \(\sigma \left( x \right)\) :

Logistic sigmoid activation function
 \(b\) :

Bias vector
 \(C_{{\text{IS/OS}}}\) :

Lowest correlation coefficient between the streamflow input candidates and output streamflow
 \(C_{{{\text{IR}}}}\) :

Maximum correlation coefficient for the input rainfall candidates
 \(C_{{\text{IR/OS}}}\) :

Lowest correlation coefficient between the rainfall input candidates and output streamflow
 \(C_{{{\text{IS}}}}\) :

Maximum correlation coefficient for the input streamflow candidates
 \(db\) :

Gradients of the bias vector
 \(dw\) :

Gradients of the weights
 \(D_{{{\text{AR}}}}\) :

Number of days lag that rainfall is accumulated
 \(D_{{{\text{IS}}}}\) :

Number of days lag of input streamflow investigated
 \(D_{{{\text{OS}}}}\) :

Number of days of output streamflow
 \(D_{{{\text{IR}}}}\) :

Number of days lag of input rainfall investigated
 \(\overline{{{\text{Epoch}}}}\) :

Maximum number of epochs
 \(k_{{{\text{prob}}}}\) :

Regularization hyperparameter in dropout
 \(m\) :

Number of output variables
 \(n\) :

Number of output examples
 \(n_{\ell }\) :

Number of neurons in the \(\ell {\text{th}}\) layer
 \(N_{{{\text{Hyp}}}}\) :

Number of times that different hyperparameters are tested
 \(N_{{{\text{RC}}}}\) :

Number of times that different \(C_{{\text{IR/OS}}}\) and \(C_{{{\text{IR}}}}\) correlation coefficients are tested
 \(N_{{{\text{SC}}}}\) :

Number of times that different \(C_{{\text{IS/OS}}}\) and \(C_{{{\text{IS}}}}\) correlation coefficients are tested
 \(w\) :

Weights
 \(\overline{{y^{\left( i \right)} }}\) :

Average streamflow for the \(i{\text{th}}\) example
 \(y^{{\left( {i, t} \right)}}\) :

Estimate for the \(t{\text{th}}\) output variable (day of forecast) in the \(i{\text{th}}\) example
 \(\hat{y}^{{\left( {i,t} \right)}}\) :

Expected value for the \(t{\text{th}}\) output variable (day of forecast) in the \(i{\text{th}}\) example
 \(\alpha\) :

Learning rate variable
 \(\lambda\) :

\(\ell_{2}\) Regularization hyperparameter
 \(\mu_{0}\) :

Mean of the output variables \(\hat{y}\)
 \(\sigma_{0}\) :

Standard deviation of the output variables \(\hat{y}\)
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Acknowledgements
The authors would like to express their gratitude to AES TietÃª for the financial support of the ANEEL Strategic R&D Project 00641052/2017. The authors also would like to thank Eduardo H.S. Silva, LÃvia M.P. Gazzi, Eliude P. Ferro, and Lanai Torres for technical discussions during the course of this project.
Funding
This research was funded by AES TietÃª Brazil under the ANEEL Strategic R&D Project 00641052/2017.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by VF and AdQ. The first draft of the manuscript was written by VF, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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de Faria, V.A.D., de Queiroz, A.R., Lima, L.M. et al. An assessment of multilayer perceptron networks for streamflow forecasting in largescale interconnected hydrosystems. Int. J. Environ. Sci. Technol. 19, 5819â€“5838 (2022). https://doi.org/10.1007/s1376202103565y
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DOI: https://doi.org/10.1007/s1376202103565y