Estimation hydraulic conductivity via intelligent models using geophysical data

Authors

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Iran

Abstract

In recent years, population growth, urban community development, and consequently increased demand for various water uses such as household and agricultural uses have severely threatened groundwater resources. This issue is highly sensitive in our country due to the arid climate. Therefore, understanding the hydro geological conditions of the aquifers, understanding the groundwater flow and estimating the parameters affecting the ground water flow, such as hydraulic conductivity, is of particular importance in the management, protection, recovery, and exploitation of groundwater. In this study, the hydraulic conductivity was estimated by using artificial intelligence models (artificial neural networks (ANNs), adaptive neuro-fuzzy inference system(ANFIS), and support vector machine(SVM)). For this purpose, the results of geophysical (geoelectric) studies in Maragheh-Bonab plain were analyzed as inputs of the models. Based on the results, the support vector machine showed better performance than the other models with RMSE =1.08 and R ^ 2 = 0.97 in the test phase.

Keywords


Alyamani, M., Sen, Z., 1993. Determination of hydraulic conductivity from complete grain size distribution curves. GroundWater, 31, 551-555.
Anifowose, F., Abdulraheem, A., 2011. Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization. Journal of Natural Gas Science and Engineering 3(3), 505–517.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000. Artificial neural network in hydrology, part I and II.
Bárdossy, A., Disse, M., 1993. Fuzzy rule-based models for infiltration. Water Resource Research 29(2), 373–382.
Batyrshin, I., Sheremetov, L., Markov, M., Panova, A., 2005. Hybrid method for porosity classification in carbonate formations. Journal of Petroleum Science and Engineering 47(1–2), 35–50.
Boulton, G.S., 1954. The drawdown of the water table under non-steady condition near a pumped well in an unconfined formation. Proceedings of the Institution of Civil Engineers 3, 564-579.
Boulton, G.S., 1963. Analysis of data from non-equilibrium pumping tests allowing from delayed yield from storage. Proceedings of the Institution of Civil Engineers 26, 469-482.
Boulton, G.S., Stretsova, T.D., 1975. New equation for determining the formation constant of an aquifer from pumping test data. Water Resources Research 11, 148-153.
Carman, P.C., 1956. Flow of Gases Through Porous Media. Butterworths, London, Great Britain.
Chitsazan, N., Nadiri, A.A., Tsai, F.T.C., 2015b. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging. Journal of Hydrology 528, 52-62.
Chiu, S., 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2, 267–278.
Chow, V.T., 1952. On the determination of transmissibility and storage coefficnts from pumping test data. Trans American Geophysical Union 33, 397-404.
Cooper, H.H., Jacob, C.E., 1946. A generalized graphical method for evaluating formation constants and summarizing well field history. Trans American Geophysical Union 27, 526-534.
Corinna; C., Vapnik, V., 1995. Support-vector networks. Machine learning 20, 273-297.
Cristianini, N., Shawe -Taylor, J., 2000. An Introduction to Support Vector Machines. Cambridge University Press, New York, USA.
Fair, G.M., Hatch, L.P., 1933. Fundamental factors governing the stream line flow of water through sand. Journal of American Water Work Association 25, 1551-1565.
Fijani, A., 2013. Hydrogeology and Hydrogeochemistry of Maragheh-Bonab Aquifer Aia Ground water modeling. M.Sc Thesis.
Gorzalczany, M.B., 2001. Computational Intelligence Systems and Applications. Physica-Verlag, Heidelberg 362pp.
Hazen, A., 1892. Some physical properties of sands and gravels. Massachusetts state board of health 24th  Annual Report, p. 539-556.
Helmy, T., Fatai, A., Faisal, K., 2010. Hybrid computational models for the characterization of oil and gas reservoirs. Expert Systems with Applications 37(7), 5353–5363.
Hong, W.C., 2011. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74, 2096-2107.
Hsieh, B., Kewis, C., Lin, Z.S., 2005. Lithology identification of aquifer from geophysical well logs and fuzzy logic analysis: shui-lin area, Taiwan. Computer and Geoscrences 31, 263-275.
Hu, C., Hao, Y., Yeh, T.C.J., Pang, B., Wu, Z., 2008. Simulation of spring flows from a karst aquifer with an artificial neural network. Hydrological Processes 22, 596–604.
Huang, Y., Gedeon, T.D., Wong, P.M., 2001. An integrated neuralfuzzy- genetic-algorithm using hyper-surface membership functions to predict permeability in petroleum reservoirs. Engineering Applications of Artificial Intelligence 14(1), 15–21.
Jang, J.S.R., 1993. ANFIS: adaptive network-based fuzzyinference system. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685.
Jiajian, Y., 2011. Log prediction for blocked tripeptides with amino acids descriptors (HMLP) by multiple linear regression and support vector regression. Procedia Environmental Sciences 8, 173–178.
Karam-Beygi, M., Hekmat, Z., Mohebi, A., Nezam Abadi Pour, H., 2008. Determination of groundwater aquifers lithology using neural fuzzy model using well surveying data. 12th Conference of the Geological Society of Iran, Ahvaz, Iran.
Khashei, A., Ghahreman, B., Kouchak Zadeh, M., 2013. Comparison of artificial neural style models, ANFIS and regression in estimating the water table of Neishabour plain aquifer. Iranian Journal of Irrigation and Drainage 1(7), 10-22.
Kurtulus, B., Razack, M., 2010. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy. Journal of Hydrology 381, 101-111.
Merdun, H., Ç─▒nar, Ö., Meral, R., Apan, M., 2006. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil Tillage Research 90(1–2), 108–116.
Moench, A.F, 1997. Flow to a well of finite diameter in a homogeneous, anisotropic water table aquifer. Water resource Research 33(6), 1397-1407.
Mokhtari, Z., Nazemi, A., Nadiri, A., 2012. Groundwater level prediction using artificial neural network model (Case study: Shabestar plain). Journal of Applied Geology 4, 345-353.
Motaghian, H.R., Mohammadi, J., 2011. Spatial estimation of saturated hydraulic conductivity from terrain attributes using regression, kriging, and artificial neural networks. Pedosphere 21(2), 170–177.
Nadiri, A., Asghari Moghadam, A., Abgari, H., Kalantari, A., Hosain Pour, A., Habib zadeh, A., 2014. Fuzzy logic model in estimating aquifer portability. Case study: Tasuj plain. Journal of Soil and Water Science 1( 24), 209-223.
Nadiri, A., Asgharimoghddam, A., Nourani, V., 2006. Basic of artificial neuran networks model (ANNs) and its application in hydrogeology. Proceeding of the 24th Symposium of Geosciences, Geological Survey of Iran, Tehran, Iran.
Nadiri, A., Chitsazan, N., Tsai, F., Moghaddam, A., 2014. Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation. Journal of Hydrologic Engineerin 19(3), 520-532.
Nadiri, A.A., Fijani, E., Tsai, F.T.C., Asghari Moghaddam, A.A. 2013b. Supervised Committee Machine with Artificial Intelligence for Prediction of Fluoride Concentration. Hydroinformatics Journal 15.4, 1474-1490.
Nauck, D., Kruse, R., 1999. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine 16 (2), 149–169.
Neuman, S.P., 1972. Theory of flow in unconfined aquifers considering delayed response of the water table. Water Resources Research 8, 1031-1045.
Neuman, S.P., 1973. Supplementary common on "Theory of flow in unconfined aquifers considering delayed response of the water table". Water Resources Research 9, 1102-1103.
Neuman, S.P., 1975. Analysis of pumping test data from anisotropic unconfined aquifers considering delayed gravity response. Water Resources Research 11, 329-342.
Nourani, V., Asgharimoghaddam, A., Nadiri, A., Singh, V. P., 2008b. Forecasting spatiotemporal water level of Tabriz aquifer. Trends in Applied Sciensses Reserch 3(4), 319-329.
Nourani, V., Asgharimoghddam, A., Nadiri, A., 2008a. An ANN-based model for spatiotemporal groundwater level forecasting. Hydrogeological Procrsses 22(26), 5054-5066.
Ortega, R.M.V., Miranda, W.R., 2004. Resolution power of well log geophysics in karst aquifers. Journal of Environmental Hydrology 12, 1-7.
Raghavendra, S., Paresh, C.D., 2014. Support vector machine applications in the field of hydrology: A review. Applied Soft Computing 19, 372-386.
Sadeghfam, S., Hassanzadeh, Y., Nadiri, A.A., Khatibi, R., 2016. Mapping groundwater potential field using catastrophe fuzzy membership functions and Jenks optimization method: a case study of Maragheh-Bonab plain, Iran. Environmental Earth Sciences 75, p. 545.
Samani, N., Gohari-Moghadam, M., Safavi, A.A., 2007. A simple neural network model for the determination of aquifer parameters. Journal of Hydrology 340(1–2), 1–11.
Samani, N., Zare, M., Shahsavand, D., Nouri, M.H., 2005.  Estimation hydraulic conductivity via adoptive neuro-fuzzy system and Gis. Ninth Conference of the Iranian Geological Society, University of Tarbiat Moalem, Tehran, Iran, p. 81-91.
Schaap, M.G., Leij, F.J., 1998. Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil Tillage Research 47(1–2), 37–42.
Seifi, A., 2010. Developing of expert system to prediction of daily evapotranspiration by support vector machine and compare results to ANN, ANFIS and experimental method. M.Sc. Thesis, Tarbiat Modarres University, Tehran, Iran.
Shabri, A., Suhartono., 2012. Streamflow forecasting using least-squares support vector machines. Hydrological Sciences journal 57(7), 1275-1293.
Shepherd, R.G., 1989.Correlations of permeability and grain size. Ground Water 27, 633-638.
Srivastav, R.K., Sudheer, K.P., Chaubey, I., 2007. A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resource Research 43(10), 10407.
Suykens, J.A.K., Tony, V.G., De, B.J., Bart, D.M., Joos, V., 2002. Least Squares Support Vector Machines, World Scientific Publishing, Singapore.
Taheri Tizro, A., Voudouris, K., Basami, Y., 2012. Estimation of porosity and specific yield by application of geoelectrical. Journal of Hydrology 454–455, 160–172
Tayfur, G., Nadiri, A.A., Moghaddam, A., 2014. Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation. Water Resour Manage 28, 1173–1184.
Theis C.V., 1935. The relation between lowering of the piezometric surface and the rate and duration of discharge of the well using groundwater storage. Trans American Geophysics Union 2, 519-524.
Tutmez, B., 2010. Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system. Neural Computing and Applications 19(3), 499–505.
Tutmez, B., Hatipoglu, Z., 2007. Spatial estimation model of porosity. Computers & Geosciences 33(4), 465–475.
Walton, W.C., 1962. Selected analytical methods for well and aquifer evaluation. Bulletin 49, Illinois State Water Survey, Urban, pp. 18.
Yoon, H., Jun, S.C., Hyun, Y., Bae, G.O., Lee, K.K., 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology 396, 128–138.