Abstract
The suspended sediment load (SSL) prediction is one of the most important issues in water engineering. In this article, the adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) were used to estimate the SLL of two main tributaries of the Telar River placed in the north of Iran. The main Telar River had two main tributaries, namely, the Telar and the Kasilian. A new evolutionary algorithm, namely, the black widow optimization algorithm (BWOA), was used to enhance the precision of the ANFIS and SVM models for predicting daily SSL. The lagged rainfall, temperature, discharge, and SSL were used as the inputs to the models. The present study used a new hybrid Gamma test to determine the best input scenario. In the next step, the best input combination was determined based on the gamma value. In this research, the abilities of the ANFIS-BWOA and SVM-BWOA were benchmarked with the ANFIS-bat algorithm (BA), SVM-BA, SVM-particle swarm optimization (PSO), and ANFIS-PSO. The mean absolute error (MAE) of ANFIS-BWOA was 0.40%, 2.2%, and 2.5% lower than those of ANFIS-BA, ANFIS-PSO, and ANFIS models in the training level for Telar River. It was concluded that the ANFIS-BWOA had the highest value of R2 among other models in the Telar River. The MAE of the ANFIS-BWOA, SVM-BWOA, SVM-PSO, SVM-BA, and SVM models were 899.12 (Ton/day), 934.23 (Ton/day), 987.12 (Ton/day), 976.12, and 989.12 (Ton/day), respectively, in the testing level for the Kasilian River. An uncertainty analysis was used to investigate the effect of uncertainty of the inputs (first scenario) and the model parameters (the second scenario) on the accuracy of models. It was observed that the input uncertainty higher than the parameter uncertainty.
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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Aboutalebi M, Haddad OB, Loáiciga HA (2016) Application of the SVR-NSGAII to Hydrograph Routing in Open Channels. J Irrig Drain Eng 142:04015061. https://doi.org/10.1061/(asce)ir.1943-4774.0000969
Adnan RM, Liang Z, El-Shafie A, Zounemat-Kermani M, Kisi O (2019) Prediction of suspended sediment load using data-driven models. Water (Switzerland) 11. https://doi.org/10.3390/w11102060
Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Wan Mohtar WHM, Hussain A (2015) ANN Based Sediment Prediction Model Utilizing Different Input Scenarios. Water Resour Manag 29:1231–1245. https://doi.org/10.1007/s11269-014-0870-1
Azamathulla HM, Cuan YC, Ghani AA, Chang CK (2013) Suspended sediment load prediction of river systems: GEP approach. Arab J Geosci 6:3469–3480. https://doi.org/10.1007/s12517-012-0608-4
Banadkooki FB, Ehteram M, Ahmed AN, Teo FY, Ebrahimi M, Fai CM, Huang YF, El-Shafie A (2020) Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm. Environ Sci Pollut Res 27:38094–38116. https://doi.org/10.1007/s11356-020-09876-w
Cai X, Zhang J, Liang H, Wang L, Wu Q (2019) An ensemble bat algorithm for large-scale optimization. Int J Mach Learn Cybern 10:3099–3113. https://doi.org/10.1007/s13042-019-01002-8
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
Cui Z, Zhang J, Wu D, Cai X, Wang H, Zhang W, Chen J (2020) Hybrid many-objective particle swarm optimization algorithm for green coal production problem. Inf Sci 518:256–271. https://doi.org/10.1016/j.ins.2020.01.018
Deotti LMP, Pereira JLR, da Silva Júnior IC (2020) Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm. Energy Conversion and Management 221:113114
Ehteram M, Salih SQ, Yaseen ZM (2020a) Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization. Environ Sci Pollut Res 27:15278–15291. https://doi.org/10.1007/s11356-020-08023-9
Ehteram M, Yenn Teo F, Najah Ahmed A, Dashti Latif S, Feng Huang Y, Abozweita O, Al-Ansari N, El-Shafie A (2020b) Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2020.08.019
Ehteram M, Ahmed AN, Latif SD, Huang YF, Alizamir M, Kisi O, Mert C, El-Shafie A (2021) Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environ Sci Pollut Res 28:1596–1611. https://doi.org/10.1007/s11356-020-10421-y
Feng Z k, Niu W j, Zhang R, Wang S, Cheng C t (2019) Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization. J Hydrol 576:229–238. https://doi.org/10.1016/j.jhydrol.2019.06.045
Feng ZK, Niu WJ, Tang ZY, Jiang ZQ, Xu Y, Liu Y, Zhang HR (2020) Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. J Hydrol 583:124627
Harkat MF, Mansouri M, Nounou MN, Nounou HN (2019) Fault detection of uncertain chemical processes using interval partial least squares-based generalized likelihood ratio test. Inf Sci 490:265–284
Hayyolalam V, Pourhaji Kazem AA (2020) Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.103249
Himanshu SK, Pandey A, Yadav B (2017) Ensemble Wavelet-Support Vector Machine Approach for Prediction of Suspended Sediment Load Using Hydrometeorological Data. J Hydrol Eng 22:05017006. https://doi.org/10.1061/(asce)he.1943-5584.0001516
Hong WC, Li MW, Geng J, Zhang Y (2019) Novel chaotic bat algorithm for forecasting complex motion of floating platforms. Applied Mathematical Modelling. https://doi.org/10.1016/j.apm.2019.03.031
Houssein EH, Helmy BE d, Oliva D, Elngar AA, Shaban H (2020) A novel Black Widow Optimization algorithm for multilevel thresholding image segmentation. Expert Syst Appl 167:114159. https://doi.org/10.1016/j.eswa.2020.114159
Hu Y, Zhang Y, Gong D (2021) Multiobjective Particle Swarm Optimization for Feature Selection with Fuzzy Cost. IEEE Trans Cybernet 51:874–888. https://doi.org/10.1109/TCYB.2020.3015756
Kaced K, Larbes C, Ramzan N, Bounabi M, Dahmane Z e (2017) Bat algorithm based maximum power point tracking for photovoltaic system under partial shading conditions. Sol Energy 158:490–503. https://doi.org/10.1016/j.solener.2017.09.063
Kakaei Lafdani E, Moghaddam Nia A, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62. https://doi.org/10.1016/j.jhydrol.2012.11.048
Kisi O, Yaseen ZM (2019) The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction. Catena. 174:11–23. https://doi.org/10.1016/j.catena.2018.10.047
Liang H, Liu Y, Shen Y, Li F, Man Y (2018) A Hybrid Bat Algorithm for Economic Dispatch with Random Wind Power. IEEE Trans Power Syst 33:5052–5061. https://doi.org/10.1109/TPWRS.2018.2812711
Liu J, Shao WW, Xiang C, Mei C, Li Z (2020) Uncertainties of urban flood modeling: Influence of parameters for different underlying surfaces. Environ Res 182:108929. https://doi.org/10.1016/j.envres.2019.108929
Moayedi H, Raftari M, Sharifi A, Jusoh WAW, Rashid ASA (2020) Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Eng Comput 36:227–238. https://doi.org/10.1007/s00366-018-00694-w
Mohamadi S, Sammen SS, Panahi F, Ehteram M, Kisi O, Mosavi A, Ahmed AN, El-Shafie A, Al-Ansari N (2020) Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm. Nat Hazards 104:537–579. https://doi.org/10.1007/s11069-020-04180-9
Nguyen TT, Pan JS, Dao TK (2019) A compact bat algorithm for unequal clustering in wireless sensor networks. Appl Sci 9. https://doi.org/10.3390/app9101973
Niu W j, Feng Z k, Chen Y b, Min Y w, Liu S, Li B j (2020) Multireservoir system operation optimization by hybrid quantum-behaved particle swarm optimization and heuristic constraint handling technique. J Hydrol 590:125477. https://doi.org/10.1016/j.jhydrol.2020.125477
Nourani V, Andalib G (2015) Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches. J Mt Sci 12:85–100. https://doi.org/10.1007/s11629-014-3121-2
Nourani V, Alizadeh F, Roushangar K (2016) Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load. Water Resour Manag 30:393–407. https://doi.org/10.1007/s11269-015-1168-7
Qasim OS, Algamal ZY (2020) Feature selection using different transfer functions for binary bat. Int J Math Eng Manag Sci. https://doi.org/10.33889/IJMEMS.2020.5.4.056
Rahgoshay M, Feiznia S, Arian M, Hashemi SAA (2018) Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm. Environ Sci Pollut Res 25:35693–35706. https://doi.org/10.1007/s11356-018-3533-6
Rahgoshay M, Feiznia S, Arian M, Hashemi SAA (2019) Simulation of daily suspended sediment load using an improved model of support vector machine and genetic algorithms and particle swarm. Arab J Geosci 12(9):1–14. https://doi.org/10.1007/s12517-019-4444-7
Rajagopal A, Joshi GP, Ramachandran A, Subhalakshmi RT, Khari M, Jha S, Shankar K, You J (2020) A Deep Learning Model Based on Multi-Objective Particle Swarm Optimization for Scene Classification in Unmanned Aerial Vehicles. IEEE Access 8:135383–135393. https://doi.org/10.1109/ACCESS.2020.3011502
Ramesh B, Chandra Jagan Mohan V, Veera Reddy VC (2013) Application of bat algorithm for combined economic load and emission dispatch. Int J of Electricl Engineering and Telecommunications 2(1):1–9
Rashidi S, Vafakhah M, Lafdani EK, Javadi MR (2016) Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arab J Geosci 9. https://doi.org/10.1007/s12517-016-2601-9
Sadeghi D, Hesami Naghshbandy A, Bahramara S (2020) Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization. Energy. 209:118471. https://doi.org/10.1016/j.energy.2020.118471
Salih SQ, Sharafati A, Khosravi K, Faris H, Kisi O, Tao H, Ali M, Yaseen ZM (2020) River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2019.1703186
Satapathy SC, Sri Madhava Raja N, Rajinikanth V, Ashour AS, Dey N (2018) Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput & Applic 29:1285–1307. https://doi.org/10.1007/s00521-016-2645-5
Seifi A, Ehteram M, Soroush F (2020) Uncertainties of instantaneous influent flow predictions by intelligence models hybridized with multi-objective shark smell optimization algorithm. J Hydrol 587:124977. https://doi.org/10.1016/j.jhydrol.2020.124977
Sharafati A, Haji Seyed Asadollah SB, Motta D, Yaseen ZM (2020a) Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrol Sci J 65:2022–2042. https://doi.org/10.1080/02626667.2020.1786571
Sharafati A, Tafarojnoruz A, Motta D, Yaseen ZM (2020b) Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines. J Hydroinf 22:1425–1451. https://doi.org/10.2166/HYDRO.2020.184
Shiau JT, Chen TJ (2015) Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads. Water Resources Management 29(8):2805–2818. https://doi.org/10.1007/s11269-015-0971-5
Spiliotis M, Mediero L, Garrote L (2016) Optimization of Hedging Rules for Reservoir Operation During Droughts Based on Particle Swarm Optimization. Water Resour Manag 30:5759–5778. https://doi.org/10.1007/s11269-016-1285-y
Tan J, Cao J, Cui Y, Duan Q, Gong W (2019) Comparison of the generalized likelihood uncertainty estimation and markov chain monte carlo methods for uncertainty analysis of the oryza_v3 model. Agron J 111:555–564. https://doi.org/10.2134/agronj2018.05.0336
Wang Y, Wang P, Zhang J, Cui Z, Cai X, Zhang W, Chen J (2019) A novel bat algorithm with multiple strategies coupling for numerical optimization. Mathematics. 7. https://doi.org/10.3390/math7020135
Xin-gang Z, Ze-qi Z, Yi-min X, Jin M (2020) Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization. Energy. 195:117014. https://doi.org/10.1016/j.energy.2020.117014
Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio-Inspired Comput 3:267. https://doi.org/10.1504/IJBIC.2011.042259
Yildizdan G, Baykan ÖK (2020) A novel modified bat algorithm hybridizing by differential evolution algorithm. Expert Syst Appl 141:112949. https://doi.org/10.1016/j.eswa.2019.112949
Zhang J, Wang G (2012) Image matching using a bat algorithm with mutation. Appl Mech Mater 203:88–93. https://doi.org/10.4028/www.scientific.net/AMM.203.88
Zhang XW, Liu H, Tu LP (2020) A modified particle swarm optimization for multimodal multi-objective optimization. Eng Appl Artif Intell 95:103905. https://doi.org/10.1016/j.engappai.2020.103905
Zounemat-Kermani M, Kişi Ö, Adamowski J, Ramezani-Charmahineh A (2016) Evaluation of data driven models for river suspended sediment concentration modeling. Journal of Hydrology 535:457–472
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Conceptualization: Mohammad ehteram, Fatemeh Panahi; Methodology: Mohammad Emami, Mohammad Ehteram; Formal analysis and investigation: Mohammad Ehteram, Mohammad Emami Writing original draft preparation: Mohammad Ehteram, Fatemeh Panahi
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Panahi, F., Ehteram, M. & Emami, M. Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an enhanced gamma test. Environ Sci Pollut Res 28, 48253–48273 (2021). https://doi.org/10.1007/s11356-021-14065-4
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DOI: https://doi.org/10.1007/s11356-021-14065-4