Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus

The target of the current paper was to examine the performance of three Markovian and seasonal based artificial neural network (ANN) models for one-step ahead and three-step ahead prediction of monthly precipitation which is the most important parameter of any hydrological study. The models proposed here are feed forward neural network (FFNN, as a classic ANN-based models), Wavelet-ANN (WANN, as a hybrid model), and Emotional-ANN (EANN, as a modern generation of ANN-based models). The models were used to precipitation prediction of seven stations located in the Northern Cyprus. Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station's precipitation through its own data at previous time steps, while in scenario 2, the central station's data were also imposed into the models in addition to each station's data, as exogenous inputs. The obtained results showed the better performance of the EANN model in comparison with other models (FFNN and WANN) especially in three-step ahead prediction. The superiorities of the EANN model over other models are due to its ability in dealing with error magnification in multi-step ahead prediction. Also, the results indicated that the performance of the scenario 2 was better than scenario 1, showing improvement of modeling efficiency up to 17% and 26% in calibration and verification steps, respectively.