Tracer model identification using artificial neural networks

The derivation of transport parameters from tracer tests conducted in geothermal systems will depend strongly on the conceptual and mathematical model that is fitted to the data. Depending on the model employed the estimation of transport parameters (porosity and dispersivity of the fracture network, porosity of the matrix) may result in a significant variation in dispersivity. If the results from such tracer tests are to be used in parameter selection for larger-scale models, it is crucial that the tracer test is itself interpreted with an appropriate model. In order to tackle this problem, artificial neural network (ANN) technology is proposed. A dual-layer neural network model was trained using synthetic tracer test data generated using analytical one-dimensional homogeneous, double-porosity pseudosteady state, multifracture, and fracture matrix models. The developed model was then used to identify several actual tracer tests conducted in various geothermal reservoirs reported in the literature. In most cases it was observed that the model successfully identified a wide variety of reservoir models. In some cases the model decreased the number possible models to two. It was also observed that ANN results were in accord with least squares analysis.


Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River basin
Yılmaz, Mustafa Tuğrul; Zaitchik, Ben; Hain, Chris R.; Crow, Wade T.; Ozdogan, Mutlu; Chun, Jong Ahn; Evans, Jason (American Geophysical Union (AGU), 2014-01-01)
Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance closure, or with spatially distributed prognostic models that simultaneously balance both energy and water budgets over landscapes using predictive equations for land surface temperature and moisture states. Each modeling approach has complementary advantages and disadvantages, and in combination they can be used to obtain more accurate ET estimates over a variety of land...
Periodic stationarity conditions for periodic autoregressive moving average processes as eigenvalue problems
Ula, TA; Smadi, AA (American Geophysical Union (AGU), 1997-08-01)
The determination of periodic stationarity conditions for periodic autoregressive moving average (PARMA) processes is a prerequisite to their analysis. Means of obtaining these conditions in analytically simple forms are sought. It is shown that periodic stationarity conditions for univariate and multivariate PARMA processes can always be reduced to eigenvalue problems, which are computationally and analytically easier to deal with. Two different lumpings of the periodic process are considered along this li...
Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography
Amjad, Muhammad; Yılmaz, Mustafa Tuğrul; Yücel, İsmail; Yılmaz, Koray Kamil (Elsevier BV, 2020-05-01)
Accuracy assessment of precipitation retrievals is a pre-requisite for many hydrological studies as it helps to understand the source and the magnitude of the uncertainty in hydrological response variables, particularly over regions with complex topography. This study evaluates GPM IMERGv05, TMPA 3B42V7, ERA-Interim, and ERA5 precipitation products using 256 ground-based gauge stations between 2014 and 2018 over Turkey known to have complex topography and varying climate. Error statistics, categorical perfo...
Rise velocity of an air bubble in porous media: Theoretical studies
Corapcioglu, MY; Cihan, A; Drazenovic, M (American Geophysical Union (AGU), 2004-04-29)
[1] The rise velocity of injected air phase from the injection point toward the vadose zone is a critical factor in in-situ air sparging operations. It has been reported in the literature that air injected into saturated gravel rises as discrete air bubbles in bubbly flow of air phase. The objective of this study is to develop a quantitative technique to estimate the rise velocity of an air bubble in coarse porous media. The model is based on the macroscopic balance equation for forces acting on a bubble ri...
ULA, TA (Elsevier BV, 1992-12-01)
Certain aspects of data generation are studied through multivariate autoregressive (AR) models. The main emphasis is on the preservation of certain desired moments and the effect of initial values on these moments. The problem of preservation of moments is approached in a nontraditional way by starting with the initial values. For this purpose, general AR processes with a random start and with time-varying parameters are introduced to lay a foundation for the analysis of all types of AR processes, including...
Citation Formats
S. Akın, “Tracer model identification using artificial neural networks,” WATER RESOURCES RESEARCH, pp. 0–0, 2005, Accessed: 00, 2020. [Online]. Available: