Transmissivity, Kriging, Artificial Neural Network, ANFIS
In interpolation of groundwater properties such as transmissivity, due to the unknown distributed values of the variables and heterogenity, the best and the unbiased aspects are frequently difficult to obtain. Therefore, applying a modern technique is necessary to obtain a real estimation of transmissivity. To gain the transmissivity values as an input data in groundwater modelling, the ordinary log kriging method has been used. In this study, the efficiency of the Adaptive Network based Fuzzy Inference System (ANFIS), artificial neural networks and ordinary kriging are investigated for interpolation of transmissivity in an unconfined aquifer. The results indicate that ANFIS model is more efficient to estimate the transmissivity in comparison with the ANN and kriging models. With these results, we can propose ANFIS model to interpolate the transmissivity values in groundwater modelling processes.
BYU ScholarsArchive Citation
"Estimation of Aquifer Transmissivity using Kriging, Artificial Neural Network, and Neuro-Fuzzy models,"
Journal of Spatial Hydrology: Vol. 6
, Article 7.
Available at: https://scholarsarchive.byu.edu/josh/vol6/iss2/7