Abstract
Monthly prediction of streamflow is a fundamental and complex hydrological phenomenon. Accurate streamflow prediction helps in water resources planning, design, and management, particularly for hydropower production, irrigation, protection of dams and flood risk management. Hence, in this paper, a hybrid robust model integrating adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) algorithm was developed for monthly streamflow prediction of Barak River basin, India. Multiple factors like Precipitation (Pt), temperature (Tt), humidity (Ht), Infiltration loss (It), are considered as the inputs for determining the streamflow. For validating the model performance, 70% of data (1980–2007) were used for training them and 30% of data (2008–2019) were used for testing them. A comparison is made between results of developed hybrid model with simple artificial neural network (ANN) and ANFIS models for assessing accuracy and efficiency of model performances. Obtained results of proposed models were evaluated based on four assessment indices including by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), determination coefficient (R2) and Nash-Sutcliffe Coefficient (ENS). Based on comparison of results, it was concluded that robust ANFIS-PSO model with RMSE = 5.887, MAE = 4.978, R2 = 0.9668, and ENS = 0.961 demonstrated best performance with more reliability and accuracy in comparison to ANFIS and ANN models. Findings of this research proved that hybrid ANFIS with an evolutionary optimization algorithm is a reliable modelling approach for monthly streamflow prediction.
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References
Adedeji PA, Akinlabi S, Madushele N, Olatunji OO (2020) Wind turbine power output very short-term forecast: A comparative study of data clustering techniques in a PSO-ANFIS model. Journal of Cleaner Production 254:120135, DOI: https://doi.org/10.1016/j.jclepro.2020.120135
Azad A, Farzin S, Kashi H, Sanikhani H, Karami H, Kisi O (2018) Prediction of river flow using hybrid neuro-fuzzy models. Arabian Journal of Geosciences 11(22):718, DOI: https://doi.org/10.1007/s12517-018-4079-0
Azad A, Manoochehri M, Kashi H, Farzin S, Karami H, Nourani V, Shiri J (2019) Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling. Journal of Hydrology 571:214–224, DOI: https://doi.org/10.1016/j.jhydrol.2019.01.062
Chi S, Ni S, Liu Z (2015) Back analysis of the permeability coefficient of a high core rockfill dam based on a RBF neural network optimized using the PSO algorithm. Mathematical Problems in Engineering 2015, DOI: https://doi.org/10.1155/2015/124042
Dalkiliç HY, Hashimi SA (2020) Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models. Water Supply 20(4):1396–1408, DOI: https://doi.org/10.2166/ws.2020.062
Dariane AB, Azimi S (2016) Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models. Hydrological Sciences Journal 61(3):585–600, DOI: https://doi.org/10.1080/02626667.2014.988155
Dariane AB, Azimi S (2018) Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection. Journal of Hydroinformatics 20(2):520–532, DOI: https://doi.org/10.2166/hydro.2017.076
Ehteram M, Afan HA, Dianatikhah M, Ahmed AN, Ming Fai C, Hossain MS, Allawi MF, Elshafie A (2019) Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors. Water 11(6):1130, DOI: https://doi.org/10.3390/w11061130
Freire PKMM, Santos CAG, Silva GBLD (2019) Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting. Applied Soft Computing 80:494–505, DOI: https://doi.org/10.1016/j.asoc.2019.04.024
Hong M, Wang D, Wang Y, Zeng X, Ge S, Yan H, Singh VP (2016) Mid-and long-term runoff predictions by an improved phase-space reconstruction model. Environmental Research 148:560–573, DOI: https://doi.org/10.1016/j.envres.2015.11.024
Huang H, Liang Z, Li B, Wang D, Hu Y, Li Y (2019) Combination of multiple datadriven models for long-term monthly runoff predictions based on Bayesian model averaging. Water Resour Manag 33:3321–3338, DOI: https://doi.org/10.1007/s11269-019-02305-9
Hundecha Y, Bardossy A, Werner H-W (2001) Development of a fuzzy logic-based rainfall-runoff model. Hydrological Sciences Journal 46(3):363–376, DOI: https://doi.org/10.1080/02626660109492832
Jang J-S (1993) ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23(3):665–685, DOI: https://doi.org/10.1109/21.256541
Jimmy SR, Sahoo A, Samantaray S Ghose DK (2021) Prophecy of runoff in a river basin using various neural networks. In: Satapathy SC, Bhateja V, Ramakrishna Murty M, Gia Nhu N, Jayasri K (eds) Communication software and networks. Springer, Singapore, 709–718, DOI: https://doi.org/10.1007/978-981-15-5397-4_72
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of ICNN’95 - International conference on neural networks, November 27-December 1, Perth, WA, Australia, 1942–1978, DOI: https://doi.org/10.1109/ICNN.1995.488968
Kisi O (2008) River flow forecasting and estimation using different artificial neural network techniques. Hydrology Research 39(1):27–40, DOI: https://doi.org/10.2166/nh.2008.026
Kisi O, Azad, A, Kashi H, Saeedian A, Hashemi SAA, Ghorbani S (2019) Modeling groundwater quality parameters using hybrid neurofuzzy methods. Water Resources Management 33(2):847–861, DOI: https://doi.org/10.1007/s11269-018-2147-6
Kisi O, Shiri, J, Demir V (2017) Hydrological time series forecasting using three different heuristic regression techniques. Handbook of Neural Computation 45–65, DOI: https://doi.org/10.1016/B978-0-12-811318-9.00003-X
Modaresi F, Araghinejad S, Ebrahimi K (2018) A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and K-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions. Water Resources Management 32(1):243–258, DOI: https://doi.org/10.1007/s11269-017-1807-2
Moghaddamnia A, Gousheh MG, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources 32(1):88–97, DOI: https://doi.org/10.1016/j.advwatres.2008.10.005
Mohammadi B, Linh NTT, Pham QB, Ahmed AN, Vojteková J, Guan Y, Abba SI, El-Shafie A (2020) Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series. Hydrological Sciences Journal 65(10): 1738–1751, DOI: https://doi.org/10.1080/02626667.2020.1758703
Mohanta NR, Patel N, Beck K, Samantaray S, Sahoo A (2021) Efficiency of river flow prediction in river using wavelet-CANFIS: A case study. In: Satapathy S, Zhang YD, Bhateja V, Majhi R (eds) Intelligent data engineering and analytics. Springer, Singapore, 435–443, DOI: https://doi.org/10.1007/978-981-15-5679-1_41
Nourani V, Davanlou Tajbakhsh A, Molajou A, Gokcekus H (2019) Hybrid wavelet-M5 model tree for rainfall-runoff modeling. Journal of Hydrologic Engineering 24(5), DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0001777
Okkan U, Serbes ZA (2013) The combined use of wavelet transform and black box models in reservoir inflow modeling. Journal of Hydrology and Hydromechanics 61(2):112–119, DOI: https://doi.org/10.2478/johh-2013-0015
Peng T, Zhou J, Zhang C, Fu W (2017) Streamflow forecasting using empirical wavelet transform and artificial neural networks. Water 9(6):406, DOI: https://doi.org/10.3390/w9060406
Poul AK, Shourian M, Ebrahimi H (2019) A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction. Water Resources Management 33(8): 2907–2923, DOI: https://doi.org/10.1007/s11269-019-02273-0
Pumo D, Viola F, Noto LV (2016) Generation of natural runoff monthly series at ungauged sites using a regional regressive model. Water 8(5):209, DOI: https://doi.org/10.3390/w8050209
Rezaeianzadeh M, Tabari H, Yazdi AA, Isik S, Kalin L (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications 25(1):25–37, DOI: https://doi.org/10.1007/s00521-013-1443-6
Roy B, Singh MP (2020) An empirical-based rainfall-runoff modelling using optimization technique. International Journal of River Basin Management 18(1):49–67, DOI: https://doi.org/10.1080/15715124.2019.1680557
Sahoo A, Samantaray S, Bankuru S, Ghose DK (2020) Prediction of flood using adaptive neuro-fuzzy inference systems: A case study. In: Satapathy S, Bhateja V, Mohanty J, Udgata S (eds) Smart intelligent computing and applications. Springer, Singapore, 733–739, DOI: https://doi.org/10.1007/978-981-13-9282-5_70
Sahoo A, Samantaray S, Ghose DK (2019) Stream flow forecasting in mahanadi river basin using artificial neural networks. Procedia Computer Science 157:168–174, DOI: https://doi.org/10.1016/j.procs.2019.08.154
Salvadore E, Bronders J, Batelaan O (2015) Hydrological modelling of urbanized catchments: A review and future directions. Journal of Hydrology 529:62–81, DOI: https://doi.org/10.1016/j.jhydrol.2015.06.028
Samantaray S, Ghose D K (2020) Modelling runoff in a river basin, India: An integration for developing un-gauged catchment. International Journal of Hydrology Science and Technology 10(3):248–266, DOI: https://doi.org/10.1504/IJHST.2020.107214
Samantaray S, Sahoo A (2020) Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study. International Journal of Knowledge-based and Intelligent Engineering Systems 24(3):243–251, DOI: https://doi.org/10.3233/KES-200046
Samantaray S, Sahoo A, Ghose DK (2019) Assessment of groundwater potential using neural network: A case study. In: Bhateja V, Satapathy S, Zhang YD, Aradhya V (eds) Intelligent computing and communication. Springer, Singapore, 655–664, DOI: https://doi.org/10.1007/978-981-15-1084-7_63
Samantaray S, Sahoo A, Ghose DK (2020a) Prediction of sedimentation in an arid watershed using BPNN and ANFIS. In: Fong S, Dey N, Joshi A (eds) ICT analysis and applications. Springer, Singapore, 295–302, DOI: https://doi.org/10.1007/978-981-15-0630-7_29
Samantaray S, Tripathy O, Sahoo A, Ghose DK (2020b) Rainfall forecasting through ANN and SVM in Bolangir Watershed, India. In: Satapathy S, Bhateja V, Mohanty J, Udgata S (eds) Smart intelligent computing and applications. Springer, Singapore, DOI: https://doi.org/10.1007/978-981-13-9282-5_74
Sanikhani H, Kisi O (2012) River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches. Water resources Management 26(6):1715–1729, DOI: https://doi.org/10.1007/s11269-012-9982-7
Seo Y, Kim S, Kisi O, Singh VP (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. Journal of Hydrology 520:224–243, DOI: https://doi.org/10.1016/j.jhydrol.2014.11.050
Shabri A, Suhartono (2012) Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal 57(7):1275–1293, DOI: https://doi.org/10.1080/02626667.2012.714468
Sharafati A, Tafarojnoruz A, Shourian M, Yaseen ZM (2020) Simulation of the depth scouring downstream sluice gate: The validation of newly developed data-intelligent models. Journal of Hydro-Environment Research 29:20–30, DOI: https://doi.org/10.1016/j.jher.2019.11.002
Shiri J, Kisi O (2010) Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. Journal of Hydrology 394(3–4):486–493, DOI: https://doi.org/10.1016/j.jhydrol.2010.10.008
Sihag P, Esmaeilbeiki F, Singh B, Ebtehaj I, Bonakdari H (2019) Modeling unsaturated hydraulic conductivity by hybrid soft computing techniques. Soft Computing 23(23):12897–12910, DOI: https://doi.org/10.1007/s00500-019-03847-1
Sridharam S, Sahoo A, Samantaray S, Ghose DK (2021) Assessment of flow discharge in a river basin through CFBPNN, LRNN and CANFIS. In: Satapathy SC, Bhateja V, Ramakrishna Murty M, Gia Nhu N, Jayasri K (eds) Communication software and networks. Springer, Singapore, 765–773, DOI: https://doi.org/10.1007/978-981-15-5397-4_78
Talaee PH (2014) Multilayer perceptron with different training algorithms for streamflow forecasting. Neural Computing and Applications 24(3–4):695–703, DOI: https://doi.org/10.1007/s00521-012-1287-5
Tikhamarine Y, Souag-Gamane D, Kisi O (2019) A new intelligent method for monthly streamflow prediction: Hybrid wavelet support vector regression based on grey wolf optimizer (WSVR-GWO). Arabian Journal of Geosciences 12(17):1–20, DOI: https://doi.org/10.1007/s12517-019-4697-1
Zakhrouf M, Bouchelkia H, Stamboul M, Kim S (2020) Novel hybrid approaches based on evolutionary strategy for streamflow forecasting in the Chellif River, Algeria. Acta Geophysica 68(1):167–180, DOI: https://doi.org/10.1007/s11600-019-00380-5
Zhang Z, Zhang Q, Singh VP (2018) Univariate streamflow forecasting using commonly used data-driven models: Literature review and case study. Hydrological Sciences Journal 63(7):1091–1111, DOI: https://doi.org/10.1080/02626667.2018.1469756
Zhou J, Peng T, Zhang C, Sun N (2018) Data pre-analysis and ensemble of various artificial neural networks for monthly streamflow forecasting. Water 10(5):628, DOI: https://doi.org/10.3390/w10050628
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Samanataray, S., Sahoo, A. A Comparative Study on Prediction of Monthly Streamflow Using Hybrid ANFIS-PSO Approaches. KSCE J Civ Eng 25, 4032–4043 (2021). https://doi.org/10.1007/s12205-021-2223-y
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DOI: https://doi.org/10.1007/s12205-021-2223-y