Document Type : Research Paper
Authors
1 Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud, Islamic University (IMSIU), Riyadh, Saudi Arabia
2 Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
3 Department of water engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
Abstract
Flow through open channels can contain solids. The deposition of solids occasionally occurs due to insufficient flow velocity to transfer the solid particles, causing many problems with transfer systems. Therefore, a method to determine the limiting velocity (i.e. Fr) is required. In this paper, three alternative, hybrid evolutionary algorithm methods, including differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) based on the adaptive network-based fuzzy inference system are presented: ANFIS-GA, ANFIS-DE and ANFIS-PSO. In these methods, evolutionary algorithms optimize the membership functions, and ANFIS adjusts the premises and consequent parameters to optimize prediction performance. The performance of the proposed methods is compared with that of the general ANFIS using three different datasets comprising a wide range of data. The results show that the hybrid models (ANFIS-GA, ANFIS-DE and ANFIS-PSO) are more accurate than general ANFIS in training with a hybrid algorithm (hybrid of back propagation and least squares). Among the evolutionary algorithms, ANFIS-PSO performed the best (R2=0.976, RMSE=0.26, MARE=0.057, BIAS=-0.004 and SI=0.059).
Keywords
Abdi J., Moshiri B., Abdulhai B., Sedigh A.K., Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm, Engineering Applications of Artificial Intelligence 25 (2012) 1022-1042.
Ab Ghani, A., Sediment transport in sewers, Ph.D. Thesis, University of Newcastle Upon Tyne, UK, 1993.
Ab Ghani A, Azamathulla H.M., Gene-expression programming for sediment transport in sewer pipe systems, Journal of Pipeline Systems Engineering and Practice 2 (2010) 102-106.
Afshar A., Shojaei N., Sagharjooghifarahani M., Multiobjective calibration of reservoir water quality modeling using multiobjective particle swarm optimization (MOPSO), Water Resources Management 27 (2013) 1931-1947.
Akbari-Alashti H., Bozorg Haddad O., Fallah-Mehdipour E., Mariño, M.A., Multi-reservoir real-time operation rules: a new genetic programming approach, Proceedings of the Institution of Civil Engineers-Water Management 167 (2014) 561-576.
Akrami S.A., Nourani V., Hakim S.J.S., Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam, Water Resources Management 28 (2014) 2999-3018.
Amiri N., Ahmadi M., Pirsaheb M., Vasseghian Y., Amiri P., Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling, Journal of Applied Research in Water and Wastewater 2 (2015) 156-163.
Azamathulla H.M., Ab Ghani, A., Fei, S.Y., ANFIS - based approach for predicting sediment transport in clean sewer, Applied Soft Computing 12 (2012) 1227-1230.
Banasiak R., Hydraulic performance of sewer pipes with deposited sediments, Water Science & Technology 57 (2008) 1743–1748.
Behera B.K., Guruprasad, R., Predicting bending rigidity of woven fabrics using adaptive neuro-fuzzy inference system (ANFIS), Journal of The Textile Institute 103 (2012) 1205-1212.
Bilgehan M., Comparison of ANFIS and NN models—With a study in critical buckling load estimation, Applied Soft Computing 11 (2011) 3779-3791.
Bonakdari H., Ebtehaj, I., Study of sediment transport using soft computing technique, In 7th International Conference on Fluvial Hydraulics, RIVER FLOW 2014, Lausanne, Switzerland, 3-5 September, pp. 933-940, 2014a.
Bonakdari H., Ebtehaj I., Verification of equation for non-deposition sediment transport in flood water canals, In 7th International Conference on Fluvial Hydraulics, RIVER FLOW 2014, Lausanne, Switzerland, 3-5 September, 1527-1533, 2014b.
Bosque G., Del Campo I., Echanobe J., Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades, Engineering Applications of Artificial Intelligence 32 (2014) 283-331.
Ciuprina G., Ioan D., Munteanu I., Use of intelligent-particle swarm optimization in electromagnetics, Magnetics IEEE Transactions on 38 (2002) 1037–1040.
Del Valle Y., Venayagamoorthy G.K., Mohagheghi S., Hernandez J.C., Harley R.G., Particle swarm optimization: basic concepts, variants and applications in power systems, Evolutionary Computation IEEE Transactions on 12 (2008) 171-195.
Eberhart R.C., Kennedy J., A new optimizer using particle swarm theory, In Proceedings of the sixth international symposium on micro machine and human science 1 (1995) 39-43.
Eberhart R.C., Hu X., Human tremor analysis using particle swarm optimization. In Evolutionary Computation, 1999. CEC 99, Proceedings of the 1999 Congress on, IEEE 3 (1999) 1927–1930.
Ebtehaj I., Bonakdari H., Evaluation of Sediment Transport in Sewer using Artificial Neural Network, Engineering Applications of Computational Fluid Mechanics 7 (2013) 382–392.
Ebtehaj I., Bonakdari H., Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers, Water Resources Managemeent 28 (2014a) 4765–4779.
Ebtehaj I., Bonakdari H., Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe, Water Science & Technology 70 (2014b) 1695-1701.
Ebtehaj I., Bonakdari H., Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms, KSCE Journal of Civil Engineering 20 (2016a) 581–589
Ebtehaj I., Bonakdari H., Assessment of evolutionary algorithms in predicting non-deposition sediment transport. Urban Water Journal 13 (2016b) 499-510
Ebtehaj I., Bonakdari H., Khoshbin F., Azimi H., Pareto Genetic Design of GMDH-type Neural Network for Predict Discharge Coefficient in Rectangular Side Orifices, Flow Measurement and Instrumentation 41 (2015a) 67-74.
Ebtehaj I., Bonakdari H., Sharif A., Design criteria for sediment transport in sewers based on self-cleansing concept, Journal of Zhejiang University Science A. 15 (2014) 914-924.
Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.
Güneri A.F., Ertay T., YüCel A., An approach based on ANFIS input selection and modeling for supplier selection problem, Expert Systems with Applications 38 (2011) 14907-14917.
Heo J.S., Lee K.Y., Garduno-Ramirez R., Multiobjective control of power plants using particle swarm optimization techniques, Energy Conversion IEEE Transactions on 21 (2006) 552–561.
Holland J., Genetic algorithms, Scientific American, pp. 66–72, 1992.
Jang J.S.R., Neuro-fuzzy modeling: architecture, analyses and applications. Dissertation, University of California, Berkeley, 1992.
Karasakal O., Guzelkaya M., Eksin I., Yesil E., Kumbasar T., Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration, Engineering Applications of Artificial Intelligence 26 (2013) 184-197.
Ab Ghani, A., Sediment transport in sewers, Ph.D. Thesis, University of Newcastle Upon Tyne, UK, 1993.
Ab Ghani A, Azamathulla H.M., Gene-expression programming for sediment transport in sewer pipe systems, Journal of Pipeline Systems Engineering and Practice 2 (2010) 102-106.
Afshar A., Shojaei N., Sagharjooghifarahani M., Multiobjective calibration of reservoir water quality modeling using multiobjective particle swarm optimization (MOPSO), Water Resources Management 27 (2013) 1931-1947.
Akbari-Alashti H., Bozorg Haddad O., Fallah-Mehdipour E., Mariño, M.A., Multi-reservoir real-time operation rules: a new genetic programming approach, Proceedings of the Institution of Civil Engineers-Water Management 167 (2014) 561-576.
Akrami S.A., Nourani V., Hakim S.J.S., Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam, Water Resources Management 28 (2014) 2999-3018.
Amiri N., Ahmadi M., Pirsaheb M., Vasseghian Y., Amiri P., Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling, Journal of Applied Research in Water and Wastewater 2 (2015) 156-163.
Azamathulla H.M., Ab Ghani, A., Fei, S.Y., ANFIS - based approach for predicting sediment transport in clean sewer, Applied Soft Computing 12 (2012) 1227-1230.
Banasiak R., Hydraulic performance of sewer pipes with deposited sediments, Water Science & Technology 57 (2008) 1743–1748.
Behera B.K., Guruprasad, R., Predicting bending rigidity of woven fabrics using adaptive neuro-fuzzy inference system (ANFIS), Journal of The Textile Institute 103 (2012) 1205-1212.
Bilgehan M., Comparison of ANFIS and NN models—With a study in critical buckling load estimation, Applied Soft Computing 11 (2011) 3779-3791.
Bonakdari H., Ebtehaj, I., Study of sediment transport using soft computing technique, In 7th International Conference on Fluvial Hydraulics, RIVER FLOW 2014, Lausanne, Switzerland, 3-5 September, pp. 933-940, 2014a.
Bonakdari H., Ebtehaj I., Verification of equation for non-deposition sediment transport in flood water canals, In 7th International Conference on Fluvial Hydraulics, RIVER FLOW 2014, Lausanne, Switzerland, 3-5 September, 1527-1533, 2014b.
Bosque G., Del Campo I., Echanobe J., Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades, Engineering Applications of Artificial Intelligence 32 (2014) 283-331.
Ciuprina G., Ioan D., Munteanu I., Use of intelligent-particle swarm optimization in electromagnetics, Magnetics IEEE Transactions on 38 (2002) 1037–1040.
Del Valle Y., Venayagamoorthy G.K., Mohagheghi S., Hernandez J.C., Harley R.G., Particle swarm optimization: basic concepts, variants and applications in power systems, Evolutionary Computation IEEE Transactions on 12 (2008) 171-195.
Eberhart R.C., Kennedy J., A new optimizer using particle swarm theory, In Proceedings of the sixth international symposium on micro machine and human science 1 (1995) 39-43.
Eberhart R.C., Hu X., Human tremor analysis using particle swarm optimization. In Evolutionary Computation, 1999. CEC 99, Proceedings of the 1999 Congress on, IEEE 3 (1999) 1927–1930.
Ebtehaj I., Bonakdari H., Evaluation of Sediment Transport in Sewer using Artificial Neural Network, Engineering Applications of Computational Fluid Mechanics 7 (2013) 382–392.
Ebtehaj I., Bonakdari H., Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers, Water Resources Managemeent 28 (2014a) 4765–4779.
Ebtehaj I., Bonakdari H., Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe, Water Science & Technology 70 (2014b) 1695-1701.
Ebtehaj I., Bonakdari H., Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms, KSCE Journal of Civil Engineering 20 (2016a) 581–589
Ebtehaj I., Bonakdari H., Assessment of evolutionary algorithms in predicting non-deposition sediment transport. Urban Water Journal 13 (2016b) 499-510
Ebtehaj I., Bonakdari H., Khoshbin F., Azimi H., Pareto Genetic Design of GMDH-type Neural Network for Predict Discharge Coefficient in Rectangular Side Orifices, Flow Measurement and Instrumentation 41 (2015a) 67-74.
Ebtehaj I., Bonakdari H., Sharif A., Design criteria for sediment transport in sewers based on self-cleansing concept, Journal of Zhejiang University Science A. 15 (2014) 914-924.
Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.
Güneri A.F., Ertay T., YüCel A., An approach based on ANFIS input selection and modeling for supplier selection problem, Expert Systems with Applications 38 (2011) 14907-14917.
Heo J.S., Lee K.Y., Garduno-Ramirez R., Multiobjective control of power plants using particle swarm optimization techniques, Energy Conversion IEEE Transactions on 21 (2006) 552–561.
Holland J., Genetic algorithms, Scientific American, pp. 66–72, 1992.
Jang J.S.R., Neuro-fuzzy modeling: architecture, analyses and applications. Dissertation, University of California, Berkeley, 1992.
Karasakal O., Guzelkaya M., Eksin I., Yesil E., Kumbasar T., Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration, Engineering Applications of Artificial Intelligence 26 (2013) 184-197.
Khan M., Azamathulla H.M., Bridge pier scour prediction by gene expression programming, Proceedings of the Institution of Civil Engineers –Water management 165 (2012) 481-493.
Kisi Ö., River suspended sediment concentration modeling using a neural differential evolution approach, Journal of hydrology 389 (2010) 227-235.
Mamdani E.H., Assilan S., An experiment in linguistic synthesis with a fuzzy controller. International journal of man-machine studies 7 (1975) 1–13.
Melanie M., An Introduction to Genetic Algorithms. MIT Press, 1996.
Mendes R., Kennedy J., Neves., J., The fully informed particle swarm: simpler, maybe better, Evolutionary Computation, IEEE Transactions on 8 (2004) 204–210.
Mousa A.A., El-Shorbagy M.A., Abd-El-Wahed W.F., Local search based hybrid particle swarm optimization algorithm for multiobjective optimization, Swarm and Evolutionary Computation 3 (2012) 1-14.
Najafzadeh M., Barani G.A., Azamathulla H.M., Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling, Neural Computing and Applications 24 (2014) 629-635.
Naka S., Genji T., Yura T., Fukuyama Y., Hybrid particle swarm optimization based distribution state estimation using constriction factor approach. In Proceeding International Conference SCIS & ISIS 2, pp. 1083–1088, 2002.
Nalluri C., Ab Ghani A., Design Options for Self-Cleansing Storm Sewers, Water Science & Technology 33 (1996) 215-220.
Ocak H., A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being, Journal Medical Systems 37 (2013) 1-9.
Ota J.J., Nalluri C., Graded sediment transport at limit deposition in clean pipe channel, 28th International Association Hydro-Environmental Engineering Research, Graz, Austria, (1999).
Ota J.J., Nalluri C., Urban storm sewer design: Approach in consideration of sediments, Journal of Hydraulic Engineering 129 (2003) 291-297.
Parsaie A., Haghiabi A.H., Assessment of some famous empirical equation and artificial intelligent model (MLP, ANFIS) to predicting the side weir discharge coefficient, Journal of Applied Research in Water and Wastewater 2 (2014) 75-79.
Pedersen M.E.H., Chipperfield A.J., Simplifying particle swarm optimization, Applied Soft Computing 10 (2010) 618-628.
Premkumar K., Manikandan B.V., Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor, Neurocomputing 157 (2015) 76-90.
Ratnaweera A., Halgamuge S., Watson H., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, Evolutionary Computation IEEE Transactions on 8 (2004) 240–255.
Safarzadeh A., Mohajeri, S.H. On the fine sediment deposition patterns in a gravel bed open-channel flow, Journal of Applied Research in Water and Wastewater 3 (2016) 188-192.
Sahay R.R., Srivastava A., Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network, Water Resources Management 28 (2014) 301-317.
Shi Y., Eberhart R.C., Parameter selection in particle swarm optimization. In: V.W. Porto, N. Saravanan, D.Waagen and A.E. Eiben, eds. Evolutionary programming VII, Lecture Notes in Computer Science, 1447. Berlin, Heidelberg: Springer, pp. 611–616, 1998.
Shi Y., Eberhart R.C., Empirical study of particle swarm optimization. In: Proceedings of IEEE, International Congress Evolutionary Computation. Piscataway, NJ, USA: IEEE Xplore Digital Library, pp. 1945–1950, 1999.
Singh R., Kainthola A., Singh T.N., Estimation of elastic constant of rock using an ANFIS approach, Applied Soft Computing 12 (2012) 40–45.
Sobhani J., Najimi M., Pourkhorshidi A.R., Parhizkar T., Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models, Construction and Building Materials 24 (2010) 709-718.
Storn R., Price K., Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization 11 (1997) 341–359.
Takagi T., Sugeno M., Fuzzy identification of systems and its applications to modeling and control. Systems, Man and Cybernetics, IEEE Transactions on 15 (1985) 116–132.
Tayfur G., Karimi Y., Singh V.P., Principle component analysis in conjuction with data driven methods for sediment load prediction, Water Resources Management 27 (2013) 2541-2554.
Valizadeh N., El-Shafie A., Forecasting the level of reservoirs using multiple input fuzzification in ANFIS, Water Resources Management 27 (2013) 3319-3331.
Vongvisessomjai N., Tingsanchali T., Babel M.S., Non-deposition design criteria for sewers with part-full flow, Urban Water Journal 7 (2010) 61-77.
Yoshida H., Kawata K., Fukuyama Y., Takayama S., Naknishi Y., A particle swarm optimization for reactive power and voltage control considering voltage security assessment. Power Systems, IEEE Transactions on 15 (2000) 1232–1239.
Yu W., Li X., Fuzzy identification using fuzzy neural networks with stable learning algorithms, Fuzzy Systems, IEEE Transactions on 12 (2004) 411–420.
Zaji A.H., Bonakdari H., Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions, Flow Measurement and Instrumentation 41 (2015) 81-89.
Zanaganeh M., Mousavi S.J., Shahidi A.F.E., A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters, Engineering Applications of Artificial Intelligence 22 (2009) 1194-1202.
Kisi Ö., River suspended sediment concentration modeling using a neural differential evolution approach, Journal of hydrology 389 (2010) 227-235.
Mamdani E.H., Assilan S., An experiment in linguistic synthesis with a fuzzy controller. International journal of man-machine studies 7 (1975) 1–13.
Melanie M., An Introduction to Genetic Algorithms. MIT Press, 1996.
Mendes R., Kennedy J., Neves., J., The fully informed particle swarm: simpler, maybe better, Evolutionary Computation, IEEE Transactions on 8 (2004) 204–210.
Mousa A.A., El-Shorbagy M.A., Abd-El-Wahed W.F., Local search based hybrid particle swarm optimization algorithm for multiobjective optimization, Swarm and Evolutionary Computation 3 (2012) 1-14.
Najafzadeh M., Barani G.A., Azamathulla H.M., Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling, Neural Computing and Applications 24 (2014) 629-635.
Naka S., Genji T., Yura T., Fukuyama Y., Hybrid particle swarm optimization based distribution state estimation using constriction factor approach. In Proceeding International Conference SCIS & ISIS 2, pp. 1083–1088, 2002.
Nalluri C., Ab Ghani A., Design Options for Self-Cleansing Storm Sewers, Water Science & Technology 33 (1996) 215-220.
Ocak H., A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being, Journal Medical Systems 37 (2013) 1-9.
Ota J.J., Nalluri C., Graded sediment transport at limit deposition in clean pipe channel, 28th International Association Hydro-Environmental Engineering Research, Graz, Austria, (1999).
Ota J.J., Nalluri C., Urban storm sewer design: Approach in consideration of sediments, Journal of Hydraulic Engineering 129 (2003) 291-297.
Parsaie A., Haghiabi A.H., Assessment of some famous empirical equation and artificial intelligent model (MLP, ANFIS) to predicting the side weir discharge coefficient, Journal of Applied Research in Water and Wastewater 2 (2014) 75-79.
Pedersen M.E.H., Chipperfield A.J., Simplifying particle swarm optimization, Applied Soft Computing 10 (2010) 618-628.
Premkumar K., Manikandan B.V., Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor, Neurocomputing 157 (2015) 76-90.
Ratnaweera A., Halgamuge S., Watson H., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, Evolutionary Computation IEEE Transactions on 8 (2004) 240–255.
Safarzadeh A., Mohajeri, S.H. On the fine sediment deposition patterns in a gravel bed open-channel flow, Journal of Applied Research in Water and Wastewater 3 (2016) 188-192.
Sahay R.R., Srivastava A., Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network, Water Resources Management 28 (2014) 301-317.
Shi Y., Eberhart R.C., Parameter selection in particle swarm optimization. In: V.W. Porto, N. Saravanan, D.Waagen and A.E. Eiben, eds. Evolutionary programming VII, Lecture Notes in Computer Science, 1447. Berlin, Heidelberg: Springer, pp. 611–616, 1998.
Shi Y., Eberhart R.C., Empirical study of particle swarm optimization. In: Proceedings of IEEE, International Congress Evolutionary Computation. Piscataway, NJ, USA: IEEE Xplore Digital Library, pp. 1945–1950, 1999.
Singh R., Kainthola A., Singh T.N., Estimation of elastic constant of rock using an ANFIS approach, Applied Soft Computing 12 (2012) 40–45.
Sobhani J., Najimi M., Pourkhorshidi A.R., Parhizkar T., Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models, Construction and Building Materials 24 (2010) 709-718.
Storn R., Price K., Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization 11 (1997) 341–359.
Takagi T., Sugeno M., Fuzzy identification of systems and its applications to modeling and control. Systems, Man and Cybernetics, IEEE Transactions on 15 (1985) 116–132.
Tayfur G., Karimi Y., Singh V.P., Principle component analysis in conjuction with data driven methods for sediment load prediction, Water Resources Management 27 (2013) 2541-2554.
Valizadeh N., El-Shafie A., Forecasting the level of reservoirs using multiple input fuzzification in ANFIS, Water Resources Management 27 (2013) 3319-3331.
Vongvisessomjai N., Tingsanchali T., Babel M.S., Non-deposition design criteria for sewers with part-full flow, Urban Water Journal 7 (2010) 61-77.
Yoshida H., Kawata K., Fukuyama Y., Takayama S., Naknishi Y., A particle swarm optimization for reactive power and voltage control considering voltage security assessment. Power Systems, IEEE Transactions on 15 (2000) 1232–1239.
Yu W., Li X., Fuzzy identification using fuzzy neural networks with stable learning algorithms, Fuzzy Systems, IEEE Transactions on 12 (2004) 411–420.
Zaji A.H., Bonakdari H., Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions, Flow Measurement and Instrumentation 41 (2015) 81-89.
Zanaganeh M., Mousavi S.J., Shahidi A.F.E., A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters, Engineering Applications of Artificial Intelligence 22 (2009) 1194-1202.
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