메뉴 건너뛰기




Volumn 28, Issue 15, 2014, Pages 5433-5446

Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

Author keywords

Adaptive neuro fuzzy inference system; Artificial neural network; Bastam plain; Groundwater level

Indexed keywords

AQUIFERS; FORECASTING; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; GROUNDWATER; GROUNDWATER RESOURCES; HYDROGEOLOGY; MEAN SQUARE ERROR; NEURAL NETWORKS; PUMPS; RAIN; RECHARGING (UNDERGROUND WATERS); WATER MANAGEMENT; WATER WELLS;

EID: 84918515939     PISSN: 09204741     EISSN: 15731650     Source Type: Journal    
DOI: 10.1007/s11269-014-0810-0     Document Type: Article
Times cited : (155)

References (51)
  • 1
    • 77953973407 scopus 로고    scopus 로고
    • Daily groundwater level fluctuation using soft computing technique
    • Affandi A, Watanabe K (2007) Daily groundwater level fluctuation using soft computing technique. J Nat Sci 5(2):1–10
    • (2007) J Nat Sci , vol.5 , Issue.2 , pp. 1-10
    • Affandi, A.1    Watanabe, K.2
  • 2
    • 84903608100 scopus 로고    scopus 로고
    • Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang gates dam
    • Akrami SA, Nourani V, Hakim SJS (2014) Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang gates dam. Water Resour Manag 28:2999–3018
    • (2014) Water Resour Manag , vol.28 , pp. 2999-3018
    • Akrami, S.A.1    Nourani, V.2    Hakim, S.J.S.3
  • 3
    • 0034174280 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology – I: Preliminary concepts
    • ASCE Task Committee (2000a) Artificial neural networks in hydrology – I: Preliminary concepts. J Hydrol Eng ASCE 5(2):115–123
    • (2000) J Hydrol Eng ASCE , vol.5 , Issue.2 , pp. 115-123
  • 4
    • 0034174396 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology – II: Hydrologic applications
    • ASCE Task Committee (2000b) Artificial neural networks in hydrology – II: Hydrologic applications. J Hydrol Eng ASCE 5(2):124–137
    • (2000) J Hydrol Eng ASCE , vol.5 , Issue.2 , pp. 124-137
  • 5
    • 84887019013 scopus 로고    scopus 로고
    • Evaluation of lateral spreading using artificial neural networks
    • Baziar MH, Ghorbani A (2011) Evaluation of lateral spreading using artificial neural networks. Expert Syst Appl 38:5958–5966
    • (2011) Expert Syst Appl , vol.38 , pp. 5958-5966
    • Baziar, M.H.1    Ghorbani, A.2
  • 6
    • 22344432020 scopus 로고    scopus 로고
    • Optimal management of coastal aquifers using linked simulation optimization approach
    • Bhattacharjya RK, Datta B (2005) Optimal management of coastal aquifers using linked simulation optimization approach. Water Resour Manage 19:295–320
    • (2005) Water Resour Manage , vol.19 , pp. 295-320
    • Bhattacharjya, R.K.1    Datta, B.2
  • 7
    • 80051558938 scopus 로고    scopus 로고
    • Simulation of water table elevation fluctuation using fuzzy-logic and ANFIS
    • Bisht D, Mohan Raju M, Joshi M (2009) Simulation of water table elevation fluctuation using fuzzy-logic and ANFIS. Comput Model New Technol 13(2):16–23
    • (2009) Comput Model New Technol , vol.13 , Issue.2 , pp. 16-23
    • Bisht, D.1    Mohan Raju, M.2    Joshi, M.3
  • 8
    • 28444489651 scopus 로고    scopus 로고
    • Adaptive neuro-fuzzy inference system for prediction of water level in reservoir
    • Chang F, Chang Y (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. J Adv Water Res 1(10):1–10
    • (2006) J Adv Water Res , vol.1 , Issue.10 , pp. 1-10
    • Chang, F.1    Chang, Y.2
  • 9
    • 77349122981 scopus 로고
    • A note on the investigation of ground water resources in western districts of Uttar Pradesh. annual report, U. P
    • Chaturvedi RS (1973) A note on the investigation of ground water resources in western districts of Uttar Pradesh. annual report, U. P. Irrig Res Inst 1973:86–122
    • (1973) Irrig Res Inst , vol.1973 , pp. 86-122
    • Chaturvedi, R.S.1
  • 10
    • 33646075449 scopus 로고    scopus 로고
    • The strategy of building a flood forecast model by neuro fuzzy network
    • Chen SH, Lin YH, Chang LC, Chang FJ (2006) The strategy of building a flood forecast model by neuro fuzzy network. Hydr Proc 20:1525–1540
    • (2006) Hydr Proc , vol.20 , pp. 1525-1540
    • Chen, S.H.1    Lin, Y.H.2    Chang, L.C.3    Chang, F.J.4
  • 11
    • 84918582247 scopus 로고    scopus 로고
    • Recharge on non-irrigated lands: Idaho Falls, University of Idaho, Idaho Water Resource Research Institute Technical Report 04–006, 19 p
    • Contor, BA (2004) Recharge on non-irrigated lands: Idaho Falls, University of Idaho, Idaho Water Resource Research Institute Technical Report 04–006, 19 p. Available online at URL: ttp://www.if.uidaho.edu/%7ejohnson/DDW003_ NIR_09_1_04.pdf
    • (2004) Available online at URL: ttp://www.if.uidaho.edu/%7ejohnson/DDW003_ NIR_09_1_04.pdf
    • Contor, B.A.1
  • 12
    • 0034621379 scopus 로고    scopus 로고
    • Daily reservoir inflow forecasting using artificial neural networks with stopped training approach
    • Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257
    • (2000) J Hydrol , vol.230 , pp. 244-257
    • Coulibaly, P.1    Anctil, F.2    Bobee, B.3
  • 13
    • 0034993945 scopus 로고    scopus 로고
    • Artificial neural network modeling of water table depth fluctuations
    • Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Wat Res 37:885–896
    • (2001) Wat Res , vol.37 , pp. 885-896
    • Coulibaly, P.1    Anctil, F.2    Aravena, R.3    Bobee, B.4
  • 14
    • 20344369583 scopus 로고    scopus 로고
    • Groundwater level forecasting using artificial neural networks
    • Daliakopoulose NI, Colibaly P, Tsanis KI (2005) Groundwater level forecasting using artificial neural networks. Hydrol 309:229–240
    • (2005) Hydrol , vol.309 , pp. 229-240
    • Daliakopoulose, N.I.1    Colibaly, P.2    Tsanis, K.I.3
  • 16
    • 84877258233 scopus 로고    scopus 로고
    • Neural network modeling of scour cone geometry around outlet in the pressure flushing
    • Emamgholizadeh S (2012) Neural network modeling of scour cone geometry around outlet in the pressure flushing. Glob Nest J 14:540–549
    • (2012) Glob Nest J , vol.14 , pp. 540-549
    • Emamgholizadeh, S.1
  • 17
    • 84887019878 scopus 로고    scopus 로고
    • Artificial intelligence-based estimation of flushing half-cone geometry
    • Emamgholizadeh S, Bateni SM, Jeng DS (2013a) Artificial intelligence-based estimation of flushing half-cone geometry. Eng Appli Arti Intel 26:2551–2558
    • (2013) Eng Appli Arti Intel , vol.26 , pp. 2551-2558
    • Emamgholizadeh, S.1    Bateni, S.M.2    Jeng, D.S.3
  • 18
    • 84900376489 scopus 로고    scopus 로고
    • Prediction of water quality parameters of Karoon river (Iran) by artificial intelligence-based models
    • Emamgholizadeh S, Kashi H, Marofpoor I, Zalaghi E (2013b) Prediction of water quality parameters of Karoon river (Iran) by artificial intelligence-based models. Int J Environ Sci Technol 11:645–656
    • (2013) Int J Environ Sci Technol , vol.11 , pp. 645-656
    • Emamgholizadeh, S.1    Kashi, H.2    Marofpoor, I.3    Zalaghi, E.4
  • 19
    • 77957854962 scopus 로고    scopus 로고
    • ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system
    • Esen H, Inalli M (2010) ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system. Exp Sys Appl 37:8134–8147
    • (2010) Exp Sys Appl , vol.37 , pp. 8134-8147
    • Esen, H.1    Inalli, M.2
  • 20
    • 0036741941 scopus 로고    scopus 로고
    • A neural-network-based classification scheme for sorting sources and ages of fecal contamination in Water
    • Gail M, Brion TR, Neelakantan SL (2002) A neural-network-based classification scheme for sorting sources and ages of fecal contamination in Water. Wat Res 36:3765–3774
    • (2002) Wat Res , vol.36 , pp. 3765-3774
    • Gail, M.1    Brion, T.R.2    Neelakantan, S.L.3
  • 21
    • 78149414476 scopus 로고    scopus 로고
    • Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks
    • Ghose D, Panada S, Swain P (2010) Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks. J Hydr 296–304
    • (2010) J Hydr , pp. 296-304
    • Ghose, D.1    Panada, S.2    Swain, P.3
  • 22
    • 78751576467 scopus 로고    scopus 로고
    • Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir lake level forcasting
    • Guldal V, Tongal H (2010) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir lake level forcasting. Wat Res Mana 24:105–128
    • (2010) Wat Res Mana , vol.24 , pp. 105-128
    • Guldal, V.1    Tongal, H.2
  • 23
    • 0004063090 scopus 로고    scopus 로고
    • A Comprehensive Foundation. Second ed. Prentice-Hall, Englewood Cliffs:
    • Haykin S (1999) Neural Networks. A Comprehensive Foundation. Second ed. Prentice-Hall, Englewood Cliffs
    • (1999) Neural Networks
    • Haykin, S.1
  • 24
    • 0027601884 scopus 로고
    • ANFIS adaptive-network-based fuzzy inference systems
    • Jang JSR (1993) ANFIS adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23(03):665–685
    • (1993) IEEE Trans Syst Man Cybern , vol.23 , Issue.3 , pp. 665-685
    • Jang, J.S.R.1
  • 26
    • 84918552566 scopus 로고    scopus 로고
    • Karami g. H. (2010). Groundwater draft in Bastam Plain, Shahrood, Iran. In proceeding of the 2010 international conference of Environmental Science and Technology. Editor (s): Saji Baby, Wataniya Environmental Services, Kuwait Parvinder Singh Sandhu, Rayat & Bahra Institute of Engineering & Bio-Technology, India
    • Karami g. H. (2010). Groundwater draft in Bastam Plain, Shahrood, Iran. In proceeding of the 2010 international conference of Environmental Science and Technology. Editor (s): Saji Baby, Wataniya Environmental Services, Kuwait Parvinder Singh Sandhu, Rayat & Bahra Institute of Engineering & Bio-Technology, India
  • 28
    • 69349098138 scopus 로고    scopus 로고
    • Estimation of river flow by artificial neural networks and identification of input vectors susceptibble to producing unreliable flow estimates
    • Kentel E (2009) Estimation of river flow by artificial neural networks and identification of input vectors susceptibble to producing unreliable flow estimates. J Hydr: 481–488
    • (2009) J Hydr , pp. 481-488
    • Kentel, E.1
  • 29
    • 33748933705 scopus 로고    scopus 로고
    • Daily pan evaporation modelling using a neuro-fuzzy computing technique
    • Kisi O (2006) Daily pan evaporation modelling using a neuro-fuzzy computing technique. J Hydrol 329:636–646
    • (2006) J Hydrol , vol.329 , pp. 636-646
    • Kisi, O.1
  • 30
    • 33748305155 scopus 로고    scopus 로고
    • Assessment of natural ground water recharge in upper ganga canal command area
    • Kumar CP, Seethapathi PV (2002) Assessment of natural ground water recharge in upper ganga canal command area. J Appl Hydrol Assoc Hydrol India 4:13–20
    • (2002) J Appl Hydrol Assoc Hydrol India , vol.4 , pp. 13-20
    • Kumar, C.P.1    Seethapathi, P.V.2
  • 31
    • 73649113119 scopus 로고    scopus 로고
    • Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neuro-fuzzy
    • Kurtulus B, Razak M (2010) Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neuro-fuzzy. J Hydrol 381:101–111
    • (2010) J Hydrol , vol.381 , pp. 101-111
    • Kurtulus, B.1    Razak, M.2
  • 32
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications
    • 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 15:101–124
    • (2000) Environ Model Softw , vol.15 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 33
    • 84918571278 scopus 로고    scopus 로고
    • Software for technical computing and Model-Based Design
    • Matlab 7.1 (2005) Software for technical computing and Model-Based Design. The Math Works Inc
    • (2005) The Math Works Inc
  • 34
    • 77953325250 scopus 로고    scopus 로고
    • Artificial neural network modeling for groundwater level forecasting in a river island of eastern India
    • Mohanty S, Jha K, Kumar A, Sudheer K (2010) Artificial neural network modeling for groundwater level forecasting in a river island of eastern India. J Water Resour Manag 24:1845–1865
    • (2010) J Water Resour Manag , vol.24 , pp. 1845-1865
    • Mohanty, S.1    Jha, K.2    Kumar, A.3    Sudheer, K.4
  • 35
    • 84874285796 scopus 로고    scopus 로고
    • A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods
    • Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manag 27:1301–1321
    • (2013) Water Resour Manag , vol.27 , pp. 1301-1321
    • Moosavi, V.1    Vafakhah, M.2    Shirmohammadi, B.3    Behnia, N.4
  • 36
    • 1942490118 scopus 로고    scopus 로고
    • A neuro fuzzy computing technique for modeling hydrological time series
    • Nayak P, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro fuzzy computing technique for modeling hydrological time series. J Hydrol 291:52–66
    • (2004) J Hydrol , vol.291 , pp. 52-66
    • Nayak, P.1    Sudheer, K.P.2    Rangan, D.M.3    Ramasastri, K.S.4
  • 37
    • 32044458602 scopus 로고    scopus 로고
    • Groundwater level forcasting in a shallow aquifer using artificial neural network
    • Nayak P, SatyajiRao Y, Sudheer K (2006) Groundwater level forcasting in a shallow aquifer using artificial neural network. J Water Resour Manag 20:77–90
    • (2006) J Water Resour Manag , vol.20 , pp. 77-90
    • Nayak, P.1    SatyajiRao, Y.2    Sudheer, K.3
  • 38
    • 67649122251 scopus 로고    scopus 로고
    • An ANN-based model for spatiotemporal groundwater level forcasting
    • Nourani V, AsghariMoghaddam A, Nadiri A (2008) An ANN-based model for spatiotemporal groundwater level forcasting. J Hydrol Proc 22:5054–5066
    • (2008) J Hydrol Proc , vol.22 , pp. 5054-5066
    • Nourani, V.1    AsghariMoghaddam, A.2    Nadiri, A.3
  • 39
    • 0141569592 scopus 로고    scopus 로고
    • Minimizing variance of reservoir systems operations benefits using soft computing tools
    • Ponnambalam K, Karray F, Mousavi SJ (2003) Minimizing variance of reservoir systems operations benefits using soft computing tools. Fuzzy Sets Syst 139:451–61
    • (2003) Fuzzy Sets Syst , vol.139 , pp. 451-461
    • Ponnambalam, K.1    Karray, F.2    Mousavi, S.J.3
  • 40
    • 84938542521 scopus 로고    scopus 로고
    • Design of a real-time groundwater level monitoring network and portrayal of hydrologic data in southern Floria. US Geological Survey Report 01-4275
    • Tallahassee: FL
    • Prinos ST, Lietz AC, Irvin RB (2002) Design of a real-time groundwater level monitoring network and portrayal of hydrologic data in southern Floria. US Geological Survey Report 01-4275, US Geological Survey, Tallahassee, FL
    • (2002) US Geological Survey
    • Prinos, S.T.1    Lietz, A.C.2    Irvin, R.B.3
  • 41
    • 84918528695 scopus 로고    scopus 로고
    • Qnet 2000 neural network modelling for windows 95/98/NT, Qnet Toll User’s Guideand Datapro User’s Guide, Vesta Services, Inc
    • Qnet 2000 (1999) Qnet 2000 neural network modelling for windows 95/98/NT, Qnet Toll User’s Guideand Datapro User’s Guide, Vesta Services, Inc., USA
    • (1999) USA
  • 42
    • 84859108726 scopus 로고    scopus 로고
    • River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches
    • Sanikhani H, Kisi O (2012) River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches. Water Resour Manag 26:1715–1729
    • (2012) Water Resour Manag , vol.26 , pp. 1715-1729
    • Sanikhani, H.1    Kisi, O.2
  • 45
    • 79952075425 scopus 로고    scopus 로고
    • Artificial neural network (ANN) based modeling for Karstic groundwater level simulation
    • Trichakis IC, Nikolos IK, Karatzas GP (2011) Artificial neural network (ANN) based modeling for Karstic groundwater level simulation. Water Resour Manage 25(4):1143–1152
    • (2011) Water Resour Manage , vol.25 , Issue.4 , pp. 1143-1152
    • Trichakis, I.C.1    Nikolos, I.K.2    Karatzas, G.P.3
  • 46
    • 33644891019 scopus 로고    scopus 로고
    • Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system
    • Tutmez B, Hatipoglu Z, Kaymak U (2006) Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Compt Rendus Geosci 32:421–433
    • (2006) Compt Rendus Geosci , vol.32 , pp. 421-433
    • Tutmez, B.1    Hatipoglu, Z.2    Kaymak, U.3
  • 47
    • 0030107781 scopus 로고    scopus 로고
    • Application of artificial neural network to simulate water-table depths under subirrigation
    • Yang CC, Prasher S, Lacroxi R (1996) Application of artificial neural network to simulate water-table depths under subirrigation. Cana Water Res J 1–12
    • (1996) Cana Water Res J , pp. 1-12
    • Yang, C.C.1    Prasher, S.2    Lacroxi, R.3
  • 49
    • 60949086949 scopus 로고    scopus 로고
    • Application and comparison of two prediction models for groundwater levels; a case study in western Jilin province, China
    • Yang ZP, Lu WX, Long YQ, Li P (2009) Application and comparison of two prediction models for groundwater levels; a case study in western Jilin province, China. J Arid Environ 73:487–492
    • (2009) J Arid Environ , vol.73 , pp. 487-492
    • Yang, Z.P.1    Lu, W.X.2    Long, Y.Q.3    Li, P.4
  • 50
    • 58849094959 scopus 로고    scopus 로고
    • Modelling level change in lakes using neuro-fuzzy and artificial neural networks
    • Yarar A, Onucyildiz M, Copty N (2009) Modelling level change in lakes using neuro-fuzzy and artificial neural networks. J Hydrol 365:329–334
    • (2009) J Hydrol , vol.365 , pp. 329-334
    • Yarar, A.1    Onucyildiz, M.2    Copty, N.3
  • 51
    • 78650179085 scopus 로고    scopus 로고
    • A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
    • Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2010) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396:128–138
    • (2010) J Hydrol , vol.396 , pp. 128-138
    • Yoon, H.1    Jun, S.C.2    Hyun, Y.3    Bae, G.O.4    Lee, K.K.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.