Authors:
Bedri Kurtulus
1
;
Nicolas Flipo
1
;
Patrick Goblet
1
;
Guillaume Vilain
2
;
Julien Tournebize
3
and
Gaëlle Tallec
3
Affiliations:
1
Mines ParisTech, UMR Sisyphe, France
;
2
Université P. et M. Curie & CNRS, UMR Sisyphe, France
;
3
Cemagref, France
Keyword(s):
ANFIS, Ordinary kriging, Hydraulic head, Orgeval.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neuro-Fuzzy Systems
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this study, two methods are evaluated for assessing hydraulic head distribution in an aquifer unit. These methods consist in Ordinary Kriging (OK) and Adaptive Neuro Fuzzy based Inference System (ANFIS). Both methods are applied on the same case study: a part of the agricultural basin of the Orgeval located 70 km east of Paris, France. 68 samples were used to predict hydraulic head distribution on a 100 m square - grid. Cartesian coordinates of the samples were used as inputs of the ANFIS, which gives encouraging result. Both simulations have realistic pattern (R2 > 0.97) even if OK performs slightly better than ANFIS at sampling site. Simulated hydraulic head distributions present discrepancies because the two methods capture different patterns. Combined use of the two approaches allow for improving the sampling location of the observation network.