Abstract
Accurate prediction of lake-level changes is a very important problem for a wise and sustainable use. In recent years significant lake level fluctuations have occurred and can be related to the climatic change. Such a problem is crucial to the works and decisions related to the water resources and management. This study is aimed to predict future lake levels during hydrometeorological changes and anthropogenic activities taking place in the Lake Eğirdir which is the most important water storage of Lake Region, one of the biggest fresh water lakes of Turkey. For this aim, recurrent neural network (RNN), adaptive network-based fuzzy inference system (ANFIS) as prediction models which have various input structures were constructed and the best fit model was investigated. Also, the classical stochastic models, auto-regressive (AR) and auto-regressive moving average (ARMA) models are generated and compared with RNN and ANFIS models. The performances of the models are examined with the form of numerical and graphical comparisons in addition to some statistic efficiency criteria. The results indicated that the RNN and ANFIS can be applied successfully and provide high accuracy and reliability for lake-level changes than the AR and the ARMA models. Also it was shown that these stochastic models can be used in the lake management policies with the acceptable risk.
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References
Ahmed JA, Sarma AK (2007) Artificial neural network model for synthetic streamflow generation. Water Resour Manag 21:1015–1029. doi:10.1007/s11269-006-9070-y
Aqil M, Kita I, Yano A, Nishiyama S (2007) Neural networks for real time catchment flow modeling and prediction. Water Resour Manag 21:1781–1796. doi:10.1007/s11269-006-9127-y
Batenia SM, Borgheib SM, Jeng DS (2007) Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Eng Appl Artif Intell 20:401–414. doi:10.1016/j.engappai.2006.06.012
Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden–Day, San Francisco
Chang LC, Chang FC (2001) Intelligent control for modeling of real-time reservoir operation. Hydrol Process 15(9):1621–1634. doi:10.1002/hyp.226
Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10. doi:10.1016/j.advwatres.2005.04.015
Chang FJ, Chang LC, Huang HL (2002) Real-time recurrent neural network for streamflow forecasting. Hydrol Process 16:2577–2588. doi:10.1002/hyp.1015
Chang LC, Chang FJ, Chiang YM (2004) A two-step-ahead recurrent neural network for stream-flow forecasting. Hydrol Process 18(1):81–92. doi:10.1002/hyp.1313
Chang YT, Chang LC, Chang FJ (2005) Intelligent control for modeling of real-time reservoir operation. Part II: ANN with operating rule curves. Hydrol Process 19:1431–1444. doi:10.1002/hyp.5582
Chen SH, Lin YH, Chang LC, Chang FJ (2005) The strategy of building a flood forecast model by neuro fuzzy network. Hydrol Process 20:1525–1540
Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278
Dawson CW, Wilby RL (1998) An artificial neural network approach to rainfall-runoff modeling. Hydrol Sci 43(1):47–67
Elmzabi A, Bellafkih M, Ramdani M (2006) An adaptive fuzzy clustering approach for the network management. Int J Inf Technol 3(1):12–17
Güldal V, Barut HB, Türk G (2002) A Rainfall database for the wide area of Lake Eğirdir, Turkey Engineering News, TMMOB. Water-II 420(421):422 (in Turkish)
Hasabe M, Nagayama Y (2002) Reservoir operation using the neural network and fuzzy systems for dam control and operation support. Adv Eng Softw 33(5):245–260. doi:10.1016/S0965-9978(02)00015-7
Jain AK, Murty MN, Flynn PJ (1999a) Data clustering: a review. ACM Comput Surv 31(3):264–323. doi:10.1145/331499.331504
Jain SK, Das A, Srivastava DK (1999b) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manage 125(5):263–271. doi:10.1061/(ASCE)0733-9496(1999)125:5(263)
Jain A, Sudheer KP, Srinivasulu S (2004) Identification of physical processes inherent in artificial neural network rainfall-runoff models. Hydrol Process 118(3):571–581. doi:10.1002/hyp.5502
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. doi:10.1109/21.256541
Jang JSR, Sun CT, Mizutani E (1997) Neuro-Fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River
Kadıoğlu M, Şen Z, Batur F (1999) Cumulative departures model for lake-water fluctuations. J Hydrol Eng 4(3):245–250. doi:10.1061/(ASCE)1084-0699(1999)4:3(245)
Kumar DN, Raju KS, Sathish T (2004) River flow forecasting using recurrent neural networks. Water Resour Manag 18:143–161. doi:10.1023/B:WARM.0000024727.94701.12
Labadie JW (2004) Optimal operation of multi reservoir systems: state of the art review. J Water Resour Plan Manage 130(2):93–111
Lallahem S, Mania J (2003) Evaluation and forecasting of daily groundwater outflow in a small chalky watershed. Hydrol Process 17(8):1561–1577. doi:10.1002/hyp.1199
Liong SY, Lim WH, Kojiri T, Hori T (2000) Advance flood forecasting for flood stricken Bangladesh with a fuzzy reasoning method. Hydrol Process 14:431–448. doi:10.1002/(SICI)1099-1085(20000228)14:3<431::AID-HYP947>3.0.CO;2-0
Luk KC, Ball JE, Sharma A (2000) A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J Hydrol (Amst) 227:56–55. doi:10.1016/S0022-1694(99)00165-1
Mahabir C, Hicks FE, Fayek AR (2000) Application of fuzzy logic to forecast seasonal runoff. Hydrol Process 17:3749–3762. doi:10.1002/hyp.1359
Matheussen BV, Thorolfsson ST (2003) Estimation of snow covered area for an urban catchment using image processing and neural networks. Water Sci Technol 48(9):155–164
Nayak PC, Sudheer KP, Ramasastri KS (2005) Fuzzy computing based rainfall–runoff model for real-time flood forecasting. Hydrol Process 19:955–968. doi:10.1002/hyp.5553
Poff LN, Tokar AS, Johnson PA (1996) Stream hydrological and ecological responses to climatic changes assessed with an artificial neural network. Limnol Oceanogr 41(5):857–863
Raman H, Sunilkumar N (1995) Multivariate modeling of water resources time series using artificial neural networks. J Hydrol Sci 40:145–163
Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural network. J Water Resour Plan Manage 122(5):342–347. doi:10.1061/(ASCE)0733-9496(1996)122:5(342)
Rami’rez MCP, Velho HFC, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the Sao Paulo Region. J Hydrol (Amst) 301:146–162. doi:10.1016/j.jhydrol.2004.06.028
Riad S, Mania J, Bouchau L, Najjar Y (2004) Rainfall–runoff model using an artificial neural network approach. Math Comput Model 40:839–846. doi:10.1016/j.mcm.2004.10.012
Roger LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30(2):457–481. doi:10.1029/93WR01494
Russell SO and Campbell PF (1996) Reservoir operating rules with fuzzy programming. J Water Resour Plan Manage, ASCE 122(3):165–179
Şen Z, Altunkaynak A (2003) Fuzzy awakening in rainfall-runoff modeling. Nord Hydrol 35(1):31–43
Sfetsos A (2000) A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renew Energy 21:23–35. doi:10.1016/S0960-1481(99)00125-1
Shafie AE, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manag 21:533–556. doi:10.1007/s11269-006-9027-1
Shamseldin AY (1997) Application of a neural network technique to rainfall-runoff modeling. J Hydrol (Amst) 199:272–294. doi:10.1016/S0022-1694(96)03330-6
Singh RD, Agarwal A, Bhunya PK (2005) ANN-based sediment yield models for Vamsadhara river basin (India). Water SA 31(1):95–100
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132
Tayfur G, Güldal V (2006) Artificial neural networks for estimating daily total suspended sediment in natural systems. Nord Hydrol 37(1):69–79
Tokar AS, Johnson A (1999) rainfall–runoff modeling using artificial neural networks. J Hydrol Eng 4(3):232–239. doi:10.1061/(ASCE)1084-0699(1999)4:3(232)
Tokar AS, Markus M (2000) Precipitation-runoff modeling using artificial neural networks and conceptual models. J Hydrol Eng 5(2):156–161. doi:10.1061/(ASCE)1084-0699(2000)5:2(156)
Trigo RM, Palutikof JP (1999) Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach. Clim Res 13:45–59. doi:10.3354/cr013045
Türker C (2003) Develo** a conceptual rainfall-runoff model and the application to the Eğirdir Lake catchment. S.D.Ü., Institute of Natural and Applied Sciences, Isparta
UNESCO (2003) http://www.unesco.org/water/wwap/wwdr/wwdr1/table_contents/index. shtml
Xu ZX, Li JY (2002) Short-term inflow forecasting using an artificial neural network model. Hydrol Process 16:2433–2439
Yao J, Dash M, Tan ST (2000) Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets Syst 113:381–388. doi:10.1016/S0165-0114(98)00038-4
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. doi:10.1016/S0019-9958(65)90241-X
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Güldal, V., Tongal, H. Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting. Water Resour Manage 24, 105–128 (2010). https://doi.org/10.1007/s11269-009-9439-9
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DOI: https://doi.org/10.1007/s11269-009-9439-9