Abstract
Aquifer parameters are the important factors for assessing groundwater potential in any area. Yet estimation of aquifer parameters is expensive and time-consuming. This study proposes an optimal and improved model to make a quantitative and qualitative correlation between pumping test data set and aquifer parameters by integration of artificial neural network training algorithms and the supervised committee machine concept. This supervised committee machine with training algorithms (SCMTA) combines Levenberg–Marquardt (LM), Bayesian regularization (BR), gradient descent (GD), one-step secant (OSS) and resilient back-propagation (RP) algorithms using a supervised combiner to estimate non-leaky confined aquifer parameters using pumping test data set. Each of these algorithms has a weight factor showing its contribution in overall prediction. The results reveal that RP, BR and LM have more contribution than OSS and GD. The developed SCMTA model trained with 2000 training sets of the Theis well function and tested with 800 sets of synthetic time-drawdown generated from the aquifers parameters. In situ observation data from the time-drawdown at station Katasbes in Shiraz plain, Southwest of Iran, are further adopted to test the applicability and reliability of the proposed method. The results of this study suggest that the SCMTA model performs better than the individual artificial neural networks differing in training algorithms, the simple averaging and weighted averaging committee machine methods and the type-curve matching technique. Additionally, results indicate that the SCMTA method corrects the concept of the superimposed plot by applying a supervised combiner to determine the optimal match point and estimate aquifer parameters more precisely. The proposed SCMTA method is recommended as an alternative to the type-curve graphical method and the existing ANN approaches.
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References
Abo-Hammour ZS, Alsmadi O, Momani S, Abu Arqub O (2013) A genetic algorithm approach for prediction of linear dynamical systems. Math Probl Eng 2013:1–12. https://doi.org/10.1155/2013/831657
Abu Arqub O, Abo-Hammour ZS (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415. https://doi.org/10.1016/j.ins.2014.03.128
Abu Arqub O, Abo-Hammour ZS, Momani S, Shawagfeh N (2012) Solving singular two-point boundary value problems using continuous genetic algorithm. Abst Appl Anal 2012:1–25. https://doi.org/10.1155/2012/205391
Abu Arqub O, Abo-Hammour ZS, Momani S (2014) Application of continuous genetic algorithm for nonlinear system of second-order boundary value problems. Appl Math Inf Sci 8(1):235–248. https://doi.org/10.12785/amis/080129
Aggarwal K, Singh Y, Chandra P, Manimala P (2005) Bayesian regularization in a neural network model to estimate lines of code using function points. J Comput Sci 1(4):505–509. https://doi.org/10.3844/jcssp.2005.505.509
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000a) Artificial neural networks in hydrology, I: preliminary concepts. J Hydrol Eng 5(2):115–123. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115)
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000b) Artificial neural networks in hydrology, II: hydrologic applications. J Hydrol Eng 5(2):124–137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
Azari T, Samani N (2018) Modeling the Neuman’s well function by an artificial neural network for the determination of unconfined aquifer parameters. Comput Geosci 22:1135–1148. https://doi.org/10.1007/s10596-018-9742-8
Azari T, Samani N, Mansoori E (2015) An artificial neural network model for the determination of leaky confined aquifer parameters: an accurate alternative to type curve matching methods. Iran J Sci Technol 39(4):463–472. https://doi.org/10.22099/IJSTS.2015.3389
Baird L, Moore A (1999) Gradient descent for general reinforcement learning. Adv Neural Inf Process Syst 11:968–974
Battiti R (1992) First and second order methods for learning: between steepest descent and Newton’s method. Neural Comput 4(2):141–166
Bhatt A, Helle HB (2002) Committee neural networks for porosity and permeability prediction from well logs. Geophys Prospect 50:645–660
Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, Oxford, p 670
Boadu FK (1997) Rock properties and seismic attenuation: neural network analysis. Pure Appl Geophys 149:507–524
Boadu FK (1998) Inversion of fracture density from field seismic velocities using artificial neural networks. Geophysica 63:534–545
Burney SMA, Jilani TA, Ardil C (2008) Levenberg–Marquardt algorithm for Karachi stock exchange share rates forecasting. Int J Comput Inf Eng 2(4):1330–1335
Chen CH, Lin ZS (2006) A committee machine with empirical formulas for permeability prediction. Comput Geosci 32(4):485–496. https://doi.org/10.1016/j.cageo.2005.08.003
Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896. https://doi.org/10.1029/2000WR900368
Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309(1–4):229–240. https://doi.org/10.1016/j.jhydrol.2004.12.001
Davis JC (2002) Statistics and data analysis in geology, 3rd edn. Wiley, New York
Demuth H, Beale M (2002) Neural network toolbox, user’s guide (Version 4). The Mathworks Inc
Fausett L (1994) Fundamentals of neural networks. Prentice-Hall, Englewood Cliffs
Garcia LA, Shigidi A (2006) Using neural networks for parameter estimation in groundwater. J Hydrol 318(1–4):215–231. https://doi.org/10.1016/j.jhydrol.2005.05.028
Gaur S, Ch S, Graillot D, Chahar BR, Kumar DN (2013) Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resour Manage 27(3):927–941. https://doi.org/10.1007/s11269-012-0226-7
Hantush MS, Jacob CE (1955) Non-steady radial flow in an infinite leaky aquifer. Trans Am Geophys Union 36(1):95–100
Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Englewood Cliffs
Huang Z, Williamson MA (1996) Artificial neural network modeling as an aid to source rock characterization. Mar Pet Geol 13(2):227–290
Huang Y, Gedeon TD, Wong PM (2001) An integrated neural-fuzzy-genetic algorithm using hyper-surface membership functions to predict permeability in petroleum reservoirs. Eng Appl Artif Intell 14(1):15–21. https://doi.org/10.1016/S0952-1976(00)00048-8
Kononen V (2005) Gradient descent for symmetric and asymmetric multiagent reinforcement learning. Web Intell Agent Syst 3:17–30
Labani MM, Kadkhodaie-Ilkhchi A, Salahshoor K (2010) Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: a case study from the Iranian part of the south pars gas field, Persian Gulf Basin. J Petrol Sci Eng 72(1):175–185. https://doi.org/10.1016/j.petrol.2010.03.015
Lim JS (2005) Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. J Petrol Sci Eng 49(3–4):182–192. https://doi.org/10.1016/j.petrol.2005.05.005
Lin GF, Chen GR (2005) Determination of aquifer parameters using radial basis function network approach. J Chin Inst Eng 28(2):241–249. https://doi.org/10.1080/02533839.2005.9670991
Lin GF, Chen GR (2006) An improved neural network approach to the determination of aquifer parameters. J Hydrol 316(1–4):281–289. https://doi.org/10.1016/j.jhydrol.2005.04.023
Lin HT, Ke KY, Chen CH, Wu SC, Tan YC (2010) Estimating anisotropic aquifer parameters by artificial neural networks. Hydrol Process 24(22):3237–3250. https://doi.org/10.1002/hyp.7750
MacKay DJC (1992) A practical Bayesian framework for backpropagation networks. Neural Comput 4(3):448–472. https://doi.org/10.1162/neco.1992.4.3.448
Mahmoudabadi H, Izadi M, Menhaj MB (2009) A hybrid method for grade estimation using genetic algorithm and neural networks. Comput Geosci 13(1):91–101. https://doi.org/10.1007/s10596-008-9107-9
Maier HR, Dandy GC (1999) Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting. Water Resour Res 32(8):2591–2596. https://doi.org/10.1029/1999WR900150
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15(1):101–124. https://doi.org/10.1016/S1364-8152(99)00007-9
Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25(8):891–909. https://doi.org/10.1016/j.envsoft.2010.02.003
Malekpour MM, Tabari MMR (2020) Implementation of supervised intelligence committee machine method for monthly water level prediction. Arab J Geosci 13:1049. https://doi.org/10.1007/s12517-020-06034-x
McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:113–115
Nadiri AA, Fijani E, Tsai FTC, Asghari-Moghaddam A (2013) Supervised committee machine with artificial intelligence for prediction of fluoride concentration. J Hydroinformatics 15(4):1474–1490. https://doi.org/10.2166/hydro.2013.008
Nadiri AA, Naderi K, Khatibi R, Gharekhani M (2019) Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrol Sci J 64(2):210–226. https://doi.org/10.1080/02626667.2018.1554940
Naftaly U, Intrator N, Horn D (1997) Optimal ensemble averaging of neural networks. Comput J Netw Comput Neural Syst 8(3):283–296. https://doi.org/10.1088/0954-898X_8_3_004
Razavi S, Tolson BA (2011) A new formulation for feed forward neural networks. IEEE Trans Neural Netw 22(10):1588–1598. https://doi.org/10.1109/TNN.2011.2163169
Riedmiller M, Braun H (1993) A direct adaptive method for faster back-propagation learning: the RPROP algorithm. In: Proc IEEE conf on neural networks
Samani N, Gohari-Moghadam M, Safavi AA (2007) A simple neural network model for the determination of aquifer parameters. J Hydrol 340(1–2):1–11. https://doi.org/10.1016/j.jhydrol.2007.03.017
Soltani J, Tabari MMR (2012) Determination of effective parameters in pipe failure rate in water distribution system using the combination of artificial neural networks and genetic algorithm. J Water Wastewater 23(83):2–15 (In Persian)
Sun J, Zhao Z, Zhang Y (2011) Determination of three-dimensional hydraulic conductivities using a combined analytical/neural network model. Tunnel Undergr Space Technol 26(2):310–319. https://doi.org/10.1016/j.tust.2010.11.002
Tabari MMR, Sanayei HRZ (2019) Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models. Soft Comput 23(19):9629–9645. https://doi.org/10.1007/s00500-018-3528-8
Tabari MMR, Azadani MN, Kamgar R (2020) Development of operation multi-objective model of dam reservoir under conditions of temperature variation and loading using NSGA-II and DANN models: a case study of Karaj/Amir Kabir dam. Soft Comput. https://doi.org/10.1007/s00500-020-04686-1
Tahmasebi P, Hezarkhani A (2012) A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput Geosci 42:18–20. https://doi.org/10.1016/j.cageo.2012.02.004
Tayfur G, Nadiri AA, Asghari-Moghaddam A (2014) Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resour Manage 28(4):1173–1184. https://doi.org/10.1007/s11269-014-0553-y
Theis CV (1935) The relationship between the lowering of the piezometric surface and the rate and duration of discharge of a well using ground-water storage. Trans Amer Geophys Union 16:519–524
Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239(1–4):132–147. https://doi.org/10.1016/S0022-1694(00)00344-9
Walton WC (1962) Leaky artesian aquifer conditions in Illinois. Illinois State Water Survey, Illinois
Wu W, Dandy GC, Maier HR (2014) Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modeling. Environ Model Softw 54:108–127. https://doi.org/10.1016/j.envsoft.2013.12.016
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Tabari, M.M.R., Azari, T. & Dehghan, V. A supervised committee neural network for the determination of aquifer parameters: a case study of Katasbes aquifer in Shiraz plain, Iran. Soft Comput 25, 4785–4798 (2021). https://doi.org/10.1007/s00500-020-05487-2
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DOI: https://doi.org/10.1007/s00500-020-05487-2