Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS.

Autor: Djurovic N; University of Belgrade, Faculty of Agriculture, Nemanjina 6, Zemun, 11000 Belgrade, Serbia., Domazet M; Electric Power Industry of Serbia, Vojvode Stepe 412, 11000 Belgrade, Serbia., Stricevic R; University of Belgrade, Faculty of Agriculture, Nemanjina 6, Zemun, 11000 Belgrade, Serbia., Pocuca V; University of Belgrade, Faculty of Agriculture, Nemanjina 6, Zemun, 11000 Belgrade, Serbia., Spalevic V; The Institute of Forestry of Montenegro, Novaka Miloseva 10/II, 81000 Podgorica, Montenegro., Pivic R; Institute of Soil Science Belgrade, Teodora Drajzera 7, 11000 Belgrade, Serbia., Gregoric E; University of Belgrade, Faculty of Agriculture, Nemanjina 6, Zemun, 11000 Belgrade, Serbia., Domazet U; Military Academy, Generala Pavla Jurisica Sturma 33, 11000 Belgrade, Serbia.
Jazyk: angličtina
Zdroj: TheScientificWorldJournal [ScientificWorldJournal] 2015; Vol. 2015, pp. 742138. Date of Electronic Publication: 2015 Nov 23.
DOI: 10.1155/2015/742138
Abstrakt: Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.
Databáze: MEDLINE
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