Abstract
River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.
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References
Abbaspour KC, Johnson CA, Van Genuchten MT (2004) Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J 3(4):1340–1352. https://doi.org/10.2136/vzj2004.1340
Afan HA, Keshtegar B, Mohtar MW, El-Shafie A (2017) Harmonize input selection for sediment transport prediction. J Hydrol 552:366–375. https://doi.org/10.1016/j.jhydrol.2017.07.008
Afan HA, Ahmed E, Wan Hanna MWM, Zaher MY (2016) Past, present and prospect of an artificial intelligence (AI) based model for sediment transport prediction. J Hydrol 541:902–913. https://doi.org/10.1016/j.jhydrol.2016.07.048
Almedeij J, Diplas P (2005) Bed load sediment transport in ephemeral and perennial gravel bed streams. EOS Transactions AGU 86(44):429–434. https://doi.org/10.1029/2005EO440002
Almedeij JH, Diplas P (2003) Bedload transport in gravel-bed streams with unimodal sediment. J Hydraul Eng ASCE 128(11):896–904. https://doi.org/10.1061/(ASCE)0733-9429(2003)129:11(896)
Almedeij JH, Diplas P, Al-Ruwaih F (2006) Approach to separate sand from gravel for bedload transport calculations in streams with bimodal bed materials. J Hydraul Eng ASCE 132(11):1176–1185. https://doi.org/10.1061/(ASCE)0733-9429(2006)132:11(1176)
Ashraf M, Shakir AS (2018) Prediction of river bank erosion and protection works in a reach of Chenab River Pakistan. Arab J Geosci 11(7):145. https://doi.org/10.1007/s12517-018-3493-7
Ayyoubzadeh SA (1997) Hydraulic aspects of straight-compound channel flow and bed load sediment transport. PhD Thesis the University of Birmingham UK:1997
Azamathulla HMD, Cuan YC, Ghani AAB, Chang CK (2013) Suspended sediment load prediction of river systems: GEP approach. Arab J Geosci 6(9):3469–3480
Barry JJ, Buffington JM, King JG (2004) A general power equation for predicting bed load transport rates in gravel bed rivers. Water Resour Res. https://doi.org/10.1029/2004WR003190
Baylar H, Hanbay D, Ozpolat E (2008) an expert system for predicting aeration performance of weirs by using ANFIS. Expert Syst Appl 35:1214–1222. https://doi.org/10.1016/j.eswa.2007.08.019
Biswas M, Banerjee P (2018) Bridge construction and river channel morphology—a comprehensive study of flow behavior and sediment size alteration of the River Chel India. Arab J Geosci 11(16):467. https://doi.org/10.1007/s12517-018-3789-7
Bunte K, SR Abt, KW Swingle, Potyondy JP (2010) Functions to adjust transport rates from a Helley-Smith sampler to bedload traps in coarse gravel-bed streams (rating curve approach). In paper presented at 2nd joint Federal Interagency Conf. Las Vegas
Cherif HM, Khanchoul K, Bouanani A, Terfous A (2017) Prediction of sediment yield at storm period in Northwest Algeria. Arab J Geosci 10(9):198. https://doi.org/10.1007/s12517-017-2983-3
Choubin B, Darabi H, Rahmati O, Sajedi-Hosseini F, Kløve B (2018) River suspended sediment modelling using the CART model: a comparative study of machine learning techniques. Sci Total Environ 615:272–281. https://doi.org/10.1016/j.scitotenv.2017.09.293
Dehghani M, Saghafian B, Nasiri Saleh F, Farokhnia A, Noori R (2014) Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte-Carlo simulation. Int J Climatol 34(4):1169–1180
Dawson CW, Abrahart RJ, See LM (2007) HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ Model Softw 22(7):1034–1052
Dellino P, Dioguardi F, Doronzo DM, Mele D (2018) The rate of sedimentation from turbulent suspension: an experimental model with application to pyroclastic density currents and discussion on the grain-size dependence of flow runout. Sedimentology. https://doi.org/10.1111/sed.12485
Dhali MK, Sahana M (2017) Spatial variation in fluvial hydraulics with major bed erosion zone: a study of Kharsoti river of India in the post monsoon period. Arab J Geosci 10(20):451. https://doi.org/10.1007/s12517-017-3205-8
Doronzo DM, Dellino P (2013) Hydraulics of subaqueous ash flows as deduced from their deposits: 2. Water entrainment, sedimentation, and deposition, with implications on pyroclastic density current deposit emplacement. J Volcanol Geotherm Res 258:176–186. https://doi.org/10.1016/j.jvolgeores.2013.04.013
Emamgholizadeh S, Demneh RK (2018) A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran. Water Sci Technol Water Supply:ws2018062. https://doi.org/10.2166/ws.2018.062
Gaeuman D, Stewart RL, Pittman S (2018) Toward the prediction of bed load rating curve parameter values: the influence of scale, particle size, and entrainment threshold. Water Resour Res 54(5):3313–3334. https://doi.org/10.1002/2017WR021627
Gomez B, Church M (1989) An assessment of bed load sediment transport formulae for gravel bed rivers. Water Resour Res 25(6):1161–1186. https://doi.org/10.1029/WR025i006p01161
Hamaamin YA, Nejadhashemi AP, Zhang Z, Giri S, Adhikari U, Herman MR (2018) Evaluation of neuro-fuzzy and Bayesian techniques in estimating suspended sediment loads. Sustainable Water Resources Management 1–16. https://doi.org/10.1007/s40899-018-0225-9
Hinton D, Hotchkiss RH, Cope M (2018) Comparison of calibrated empirical and semi-empirical methods for bedload transport rate prediction in gravel bed streams. J Hydraul Eng 144(7):04018038. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001474
Hinton D, Hotchkiss RH, Ames DP (2017) Comprehensive and quality-controlled bedload transport database. J Hydraul Eng 143(2):06016024. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001474
Hsu KL, Gupta H, Sorooshian S (1995) Artificial neural network modeling of the rainfall runoff process. Water Resour Res 31(10):2517–2530. https://doi.org/10.1029/95WR01955
Huisman BJA, Ruessink BG, Schipper MA, Luijendijk AP, Stive MJF (2018) Modelling of bed sediment composition changes at the lower shoreface of the sand motor. Coast Eng 132:33–49. https://doi.org/10.1016/j.coastaleng.2017.11.007
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems man and cybernetics 23(3)L 665-685
Javernick L, Redolfi M, Bertoldi W (2018) Evaluation of a numerical model’s ability to predict bed load transport observed in braided river experiments. Adv Water Resour 115:207–218. https://doi.org/10.1016/j.advwatres.2018.03.012
Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11(1):137–148
Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S (2008) Adaptive neuro-fuzzycomputing technique for suspended sediment estimation. AdvEng Softw 40:438–444. https://doi.org/10.1016/j.advengsoft.2008.06.004
Kisi O, Karahan E, Sen Z (2006) River suspended sediment modelling using a fuzzy logic. Approach. Hydrol Process 20(20):4351–4362. https://doi.org/10.1002/hyp.6166
Lajiness M, Watson I (2008) Dissimilarity-based approaches to compound acquisition. Curr Opin Chem Biol 12(3):366–371
Marce’ R, Comerma M, Garcia JC, Armengol J (2004) A neuro-fuzzy modeling tool to estimate fluvial nutrient loads in watersheds under time-varying human impact. LimnolOceanogr 2(1):342–355. https://doi.org/10.4319/lom.2004.2.342
Nagy HM, Watanabe K, Hirano M (2002) Estimation of sediment load concentration in rivers using artificial neural network model. J Hydraulic Eng ASCE 128(6):588–595. https://doi.org/10.1061/ (ASCE)0733-9429(2002)128:6(588)
Noori R, Hoshyaripour GA, Ashrafi K, Araabi BN (2009) Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment, in press 44:476–482. https://doi.org/10.1016/j.atmosenv.2009.11.005
Oguz H, Sarıtas I, Baydan HE (2010) Prediction of diesel engine performance using biofuels with artificial neural network. Expert Syst Appl 37(9):6579–6586. https://doi.org/10.1016/j.eswa.2010.02.128
Piuleac CG, Rodrigo MA, izares P, Curteanu C (2010) Ten steps modeling of electrolysis processes by using neural networks. Environ Model Softw 25:74–81. https://doi.org/10.1016/j.envsoft.2009.07.012
Qasem SN, Ebtehaj I, RiahiMadavar H (2017) Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms. J Appl Res Water Wastewater 4(1):290–298. https://doi.org/10.22126/arww.2017.773
Rajaee T, Mirbagheri SA, Zounemat-Kermani M, Nourani V (2009) Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci Total Environ 407(17):4916–4927
Riahi H, Ayyoubzadeh SA (2007a) Prediction of regime channel treatments using ASNFIS modeling, first national hydraulic and dam engineering 1–3 November, Islamic Azad University of Karaj, Karaj
Riahi H, Ayyoubzadeh SA (2007b) Using neuro-fuzzy in scour hole dimensions. In Sixth hydraulic conference of Iran; 2–4 September, Shahrekord University, Shahrekord
Riahi H, Ayyoubzadeh SA, Atani MG (2011a) Developing an expert system for predicting alluvial channel geometry using ANN. Expert Syst Appl 38(1):215–222. https://doi.org/10.1016/j.eswa.2010.06.047
Riahi H, Ayyoubzadeh SA, Khadangi E, Ebadzadeh MM (2009) An expert system for predicting longitudinal dispersion coefficient in natural streams using ANFIS. Expert Syst Appl 36:8589–8596. https://doi.org/10.1016/j.eswa.2008.10.043
Riahi H, Ayyoubzadeh S, Namin M, Seifi A (2011b) Uncertainty analysis of quasi-two-dimensional flow simulation in compound channels with overbank flows. J Hydrol Hydromechanics 59(3):171–183. https://doi.org/10.2478/v10098-011-0014-8
Rossel RV, Webster R (2012) Predicting soil properties from the Australian soil visible–near infrared spectroscopic database. Eur J Soil Sci 63(6):848–860
Roushangar K, Koosheh A (2015) Evaluation of GA-SVR method for modeling bed load transport in gravel-bed rivers. J Hydrol 527:1142–1152. https://doi.org/10.1016/j.jhydrol.2015.06.006
Sasal EMD, Isik S (2005) Suspended sediment load estimation in lower Sakarya River by using soft computational methods. Proceeding of the international conference on computational and mathematical methods in science and engineering, CMMSE2005. Spain: Alicante; 395–406
Seifi A, Riahi H (2018) Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran. J Water Clim Change. https://doi.org/10.2166/wcc.2018.003
Shih WR, Diplas P (2018) A unified approach to bed load transport description over a wide range of flow conditions via the use of conditional data treatment. Water Resour Res 54(2):3490–3509. https://doi.org/10.1029/2017WR022373
Spiliotis M, Kitsikoudis V, Kirca VO, Hrissanthou V (2018) Fuzzy threshold for the initiation of sediment motion. Appl Soft Comput 72:312–320. https://doi.org/10.1016/j.asoc.2018.08.006
Talebizadeh M, Moridnejad A (2011) Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Syst Appl 38(4):4126–4135. https://doi.org/10.1016/j.eswa.2010.09.075
Talebizadeh M, Morid S, Ayyoubzadeh SA (2009) Uncertainty analysis in sediment load modeling using ANN and SWAT model. Water Resour Manag 24(9):1747–1761. https://doi.org/10.1007/s11269-009-9522-2
Van Rijn LC (2007) Unified view of sediment transport by currents and waves. I: initiation of motion, bed roughness, and bed-load transport. J Hydraul Eng 133(6):649–667. https://doi.org/10.1061/(ASCE)0733-9429(2007)133:6(649)
Yang CT, Reza M, Aalami MT (2009) Evaluation of total load sediment transport using AAN. IntJ Sediment Res 24(3):274–286. https://doi.org/10.1016/S1001-6279(10)60003-0
Yapo PO, Gupta HV, Sorooshian S (1998) Multi-objective global optimization for hydrologic models. J Hydrol 204(1–4):83–97
Yilmaz B, Egemen A, Sinan N, Murat K (2018) Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Sci Total Environ 639:826–840. https://doi.org/10.1016/j.scitotenv.2018.05.153
Zadeh LA (1983) The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst 11(1–3):199–227
Zeynoddin M, Bonakdari H, Azari A, Ebtehaj I, Gharabaghi B, Madavar HR (2018) Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate. J Environ Manag 222:190–206. https://doi.org/10.1016/j.jenvman.2018.05.072
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Riahi-Madvar, H., Seifi, A. Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS. Arab J Geosci 11, 688 (2018). https://doi.org/10.1007/s12517-018-3968-6
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DOI: https://doi.org/10.1007/s12517-018-3968-6