Abstract
Hydrological forecasts have been developed since the earliest civilizations allowing to plan actions such as agriculture, grain storage, and the construction of reservoirs to supply water during long periods of drought. These forecasts are becoming increasingly essential given the growing dependence on water resources in the most diverse activities such as hydroelectric power generation. In this study, we develop a hybrid approach to forecasting the daily flow of the Zambezi River at the Cahora Bassa dam in Mozambique. These forecasts use daily historical data on flow, evaporation, relative humidity, and rainfall to predict the weather one day in advance. The model employs the seven past days as inputs to the Group Method of Data Handling (GMDH) algorithm optimized by the Particle Swarm Optimization (PSO) algorithm. GMDH is a neural network composed of neurons arranged in several layers, consisting of polynomial functions with two variables combined in a cascade to produce the output at the end of the network. The PSO promotes a search for optimal GMDH parameters to minimize the error values between the predicted and the actual river flow observed values. The simulations are performed 25 times to reduce the effects of the random values characteristic of the tested models. The results obtained by the proposed approach are compared with the other neural networks, such as extreme learning machine (ELM) and Multi-layer perceptron (MLP). The models’ performances were compared using five metrics, statistical tests, and uncertainty analysis. These results show that the GMDH model produced better flow prediction capability than the other two models.
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Abbreviations
- AI:
-
Artificial intelligence
- ANFIS:
-
Adaptive neuro-fuzzy inference systems
- ANN:
-
Artificial neural networks
- ANOVA:
-
Analysis of variance
- ARIMA:
-
Autoregressive integrated moving average
- ELM:
-
Extreme learning machine
- GLSSVM:
-
Group Method of Data Handling algorithm + Least Squares Support Vector Machine
- GMDH:
-
Group Method of Data Handling algorithm
- GP:
-
Gaussian process
- GS-GMDH:
-
Generalized Structure of Group Method of Data Handling algorithm
- KGE:
-
Kling–Gupta efficiency
- LSSVM:
-
Least squares support vector machine
- MAD:
-
Mean absolute deviation
- MAE:
-
Mean absolute error
- MLP:
-
Multi-layer perceptron regress
- MSE:
-
Mean square error
- NSE:
-
Nash–Sutcliffe efficiency
- PSO:
-
Particle swarm optimization
- RF:
-
Random forest
- RMSE:
-
Root-mean-square error
- SARIMA:
-
Seasonal autoregressive integrated moving average
- SVR:
-
Support vector regressor
- VKG:
-
Volterra–Kolmogorov–Gabo
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Funding
This work were supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Grant no. 001), Universidade Federal de Juiz de Fora, CNPq (Grant no. 429639/2016), Fundação de Amparo a Pesquisa do Estado de Minas Gerais (Grant no. APQ-00334/18) and GCUB/PROAFRI (Grant no. 001/2018).
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Souza, D.P.M., Martinho, A.D., Rocha, C.C. et al. Hybrid particle swarm optimization and group method of data handling for short-term prediction of natural daily streamflows. Model. Earth Syst. Environ. 8, 5743–5759 (2022). https://doi.org/10.1007/s40808-022-01466-8
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DOI: https://doi.org/10.1007/s40808-022-01466-8