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

Autor:	Amir Molajou, Fahreddin Sadikoglu, Selin Uzelaltinbulat, Vahid Nourani
Rok vydání:	2019
Předmět:	Atmospheric Science 010504 meteorology & atmospheric sciences Artificial neural network Computer science Calibration (statistics) 0207 environmental engineering Markov process 02 engineering and technology computer.software_genre 01 natural sciences Set (abstract data type) symbols.namesake symbols Feedforward neural network Precipitation Data mining 020701 environmental engineering computer Hybrid model 0105 earth and related environmental sciences
Zdroj:	Theoretical and Applied Climatology. 138:1419-1434
ISSN:	1434-4483 0177-798X
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::45921ecd057276296bbeb20386e88a46 https://doi.org/10.1007/s00704-019-02904-x Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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