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.
TY - JOUR
AU - Djurović, Nevenka
AU - Domazet, Milka
AU - Stričević, Ružica
AU - Počuča, Vesna
AU - Spalević, V.
AU - Pivić, Radmila
AU - Gregorić, Eniko
AU - Domazet, Uroš
PY - 2015
UR - http://aspace.agrif.bg.ac.rs/handle/123456789/3838
AB - 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.
PB - Hindawi Publishing Corporation
T2 - Scientific World Journal
T1 - Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS
VL - 2015
DO - 10.1155/2015/742138
ER -
@article{
author = "Djurović, Nevenka and Domazet, Milka and Stričević, Ružica and Počuča, Vesna and Spalević, V. and Pivić, Radmila and Gregorić, Eniko and Domazet, Uroš",
year = "2015",
abstract = "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.",
publisher = "Hindawi Publishing Corporation",
journal = "Scientific World Journal",
title = "Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS",
volume = "2015",
doi = "10.1155/2015/742138"
}
Djurović, N., Domazet, M., Stričević, R., Počuča, V., Spalević, V., Pivić, R., Gregorić, E.,& Domazet, U.. (2015). Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS. in Scientific World Journal
Hindawi Publishing Corporation., 2015.
https://doi.org/10.1155/2015/742138
Djurović N, Domazet M, Stričević R, Počuča V, Spalević V, Pivić R, Gregorić E, Domazet U. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS. in Scientific World Journal. 2015;2015.
doi:10.1155/2015/742138 .
Djurović, Nevenka, Domazet, Milka, Stričević, Ružica, Počuča, Vesna, Spalević, V., Pivić, Radmila, Gregorić, Eniko, Domazet, Uroš, "Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS" in Scientific World Journal, 2015 (2015),
https://doi.org/10.1155/2015/742138 . .
