Modelling groundwater level variations by learning from multiple models using fuzzy logic

References

• Alvisi, S., et al., 2006. Water level forecasting through fuzzy logic and artificial neural network approaches. Journal of Hydrology and Earth System Sciences, 10 (1), 1–17. doi:10.5194/hess-10-1-2006
   Web of Science ®Google Scholar
• Asadi, S., et al., 2014. Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. Environmental Science and Pollution Research, 21 (14), 8847–8857. doi:10.1007/s11356-014-2821-z
   PubMed Web of Science ®Google Scholar
• Baghapour, M.A., et al., 2016. Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran. Journal of Environmental Health Science and Engineering, 14 (1), 13. doi:10.1186/s40201-016-0254-y
   Web of Science ®Google Scholar
• Chen, C.H. and Lin, Z.S., 2006. A committee machine with empirical formulas for permeability prediction. Computers and Geosciences, 32 (4), 485–496. doi:10.1016/j.cageo.2005.08.003
   Web of Science ®Google Scholar
• Chen, M.S. and Wang, S.W., 1999. Fuzzy clustering analysis for optimizing fuzzy membership functions. Fuzzy Sets and Systems, 103 (2), 239–254. doi:10.1016/S0165-0114(98)00224-3
   Web of Science ®Google Scholar
• Chitsazan, N., Nadiri, A.A., and Tsai, F.T.-C., 2015. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging. Journal of Hydrology, 528, 52–62. doi:10.1016/j.jhydrol.2015.06.007
   Web of Science ®Google Scholar
• Chiu, S., 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2, 267–278.
   Web of Science ®Google Scholar
• Daliakopoulos, I.N., Coulibaly, P., and Tsanis, I.K., 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309, 229–240. doi:10.1016/j.jhydrol.2004.12.001
   Web of Science ®Google Scholar
• Emberger, L., 1930. Sur une formule applicable en g´eogrphiebotanique. Cahiers Herbs Seances de l’Academie des Sciences, 191, 389–390.
   Google Scholar
• Fijani, E., et al., 2013. Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer Iran. Journal of Hydrology, 503, 89–100. doi:10.1016/j.jhydrol.2013.08.038
   Web of Science ®Google Scholar
• Giustolisi, O. and Simeone, V., 2010. Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions. Hydrological Sciences Journal, 53, 502–523.
   Google Scholar
• Jha, M.K. and Sahoo, S., 2015. Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater. Hydrological Processes, 29, 671–691. doi:10.1002/hyp.v29.5
   Web of Science ®Google Scholar
• Kadkhodaie-Ilkhchi, A., et al., 2009. Petrophysical data prediction from seismic attributes using committee fuzzy inference system. Computers and Geosciences, 35, 2314–2330. doi:10.1016/j.cageo.2009.04.010
   Web of Science ®Google Scholar
• Khatibi, R., Ghorbani, M.A., and Pourhosseini, F.A., 2017. Stream flow predictions using nature-inspired Firefly Algorithms and a Multiple Model strategy – directions of innovation towards next generation practices. Journal of Advanced Engineering Informatics, 34, 80–89. doi:10.1016/j.aei.2017.10.002
   Web of Science ®Google Scholar
• Khatibi, R., et al., 2011. Comparison of three artificial intelligence techniques for discharge routing. Journal of Hydrology, 403, 201–212. doi:10.1016/j.jhydrol.2011.03.007
   Web of Science ®Google Scholar
• Labani, M.M., Kadkhodaie-Ilkhchi, A., and 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. Journal of Petroleum Science and Engineering, 72, 175–185. doi:10.1016/j.petrol.2010.03.015
   Web of Science ®Google Scholar
• Lallahem, S., et al., 2005. On the use of neural networks to evaluate groundwater levels in fractured media. Journal of Hydrology, 307, 92–111. doi:10.1016/j.jhydrol.2004.10.005
   Web of Science ®Google Scholar
• Larsen, P.M., 1980. Industrial applications of fuzzy logic control. International Journal of Man-Machine Studies, 12, 3–10. doi:10.1016/S0020-7373(80)80050-2
   Google Scholar
• Lee, K.H., 2004. First course on fuzzy theory and applications. Berlin: Springer.
   Google Scholar
• Legates, D.R. and McCabe, G.J., 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35 (1), 233–241. doi:10.1029/1998WR900018
   Web of Science ®Google Scholar
• Mamdani, E.H., 1977. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers, 26, 1182–1191. doi:10.1109/TC.1977.1674779
   Web of Science ®Google Scholar
• Mohanty, S., et al., 2013. Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha, India. Journal of Hydrology, 495, 38–51. doi:10.1016/j.jhydrol.2013.04.041
   Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2013a. Supervised committee machine with artificial intelligence for prediction of fluoride concentration. Journal of Hydroinformatics, 15 (4), 1474–1490. doi:10.2166/hydro.2013.008
   Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2013b. Hydro geochemical analysis for Tasuj plain aquifer, Iran. Journal of Earth System Science, 122 (4), 1091–1105. doi:10.1007/s12040-013-0329-4
   Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2014. Bayesian artificial intelligence model averaging for hydraulic conductivity estimation. Journal of Hydrologic Engineering, 19, 520–532. doi:10.1061/(ASCE)HE.1943–5584.0000824
   Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2017a. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Science of the Total Environment, 574, 691–706. doi:10.1016/j.scitotenv.2016.09.093
   PubMed Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2017b. Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Environmental Science and Pollution Research, 24 (9), 8562–8577. doi:10.1007/s11356-017-8489-4
   PubMed Web of Science ®Google Scholar
• Nadiri, A. A., et al., 2017c. Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures. Journal of Science of the Total Environment, 593, 75–90. doi: 10.1016/j.scitotenv.2017.03.109
   PubMed Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2018a. Hybrid fuzzy model to predict strength and optimum compositions of natural Alumina-Silica-based geopolymers. Computers and Concrete, 21 (1). doi:10.12989/cac.2018.21.1.000
   Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2018b. Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model. Journal of Cleaner Production, 180, 539–549. doi:10.1016/j.jclepro.2018.01.139
   Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2018c. Introducing the risk aggregation problem to aquifers exposed to impacts of anthropogenic and geogenic origins on a modular basis using “risk cells”. Journal of Environmental Management, 217, 654–667. doi:10.1016/j.jenvman.2018.04.011
   PubMed Web of Science ®Google Scholar
• Nadiri, A.A., et al., 2018d. Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks. Journal of Environmental Management, 227, 415–428. doi:10.1016/j.jenvman.2018.08.019
   PubMed Web of Science ®Google Scholar
• Nadiri, A.A., Asgharimoghaddam, A., and Shokri, S., 2015a. Efficiency assessment of wastewater treatment plant of Tabriz using artificial intelligence models. Journal of Environmental Studies, 40 (4), 827–844.
   Google Scholar
• Naftaly, U., Intrator, N., and Horn, D., 1997. Optimal ensemble averaging of neural networks. Journal of Computational Neuroscience, 8, 283–296.
   Google Scholar
• Nayak, P.C., et al., 2004. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291 (1–2), 52–66. doi:10.1016/j.jhydrol.2003.12.010
   Web of Science ®Google Scholar
• Newton, S.C., Pemmaraju, S., and Mitra, S., 1992. Adaptive fuzzy leader clustering of complex data sets in pattern recognition. IEEE Transactions on Neural Networks, 3, 794–800. doi:10.1109/72.159068
   PubMed Web of Science ®Google Scholar
• Nourani, V., Mogaddam, A., and Nadiri, A.O., 2008a. An ANN-based model for spatiotemporal groundwater level forecasting. Hydrological Processes, 22, 5054–5066. doi:10.1002/hyp.v22:26
   Web of Science ®Google Scholar
• Nourani, V., et al., 2008b. Forecasting spatiotemporal water levels of Tabriz aquifer. Trends in Applied Sciences Research, 3 (4), 319–329. doi:10.3923/tasr.2008.319.329
   Google Scholar
• Sahoo, M., et al., 2016. Space–time forecasting of groundwater level using a hybrid soft computing model. Hydrological Sciences Journal, 62, 561–574.
   Web of Science ®Google Scholar
• Shiri, J. and Kişi, Ö., 2011. Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers and Geosciences, 37 (10), 1692–1701. doi:10.1016/j.cageo.2010.11.010
   Web of Science ®Google Scholar
• Sugeno, M., 1985. Industrial application of fuzzy control. New York: North-Holland.
   Google Scholar
• Tayfur, G., Nadiri, A.A., and Moghaddam, A.A., 2014. Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resources Management, 28, 1173–1184. doi:10.1007/s11269-014-0553–y
   Web of Science ®Google Scholar
• Ward Jr., J.H., 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244. doi:10.1080/01621459.1963.10500845
   Web of Science ®Google Scholar
• Yurdusev, M.A., et al., 2009. Neural networks and fuzzy inference systems for predicting water consumption time series. Stochastic Environmental Research and Risk Assessment, 23, 1225. doi:10.1007/s00477-009-0320-4
   Google Scholar
• Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8 (3), 338–353. doi:10.1016/S0019-9958(65)90241-X
   Google Scholar