Abstract
The deterministic approach, which utilizes the gradient information in the search process, is prone to trapping at local minima, primarily due to the presence of saddle points and local minima in the non-convex objective function of an artificial neural network (ANN). This study investigated the efficacy of a hybrid model that adopted a meta-heuristic algorithm (MHA) as an optimizer to extend the training ANN method, from a gradient-based to a stochastic population-based approach for streamflow forecasting. In the latter, parameter tuning utilizing the design of experiment (DOE) technique, has become an integral element in the optimization process due to reliance on their parameters. For model convenience, a wavelet transform was employed to decompose the series into sub-series. The empirical studies of MHA performance showed that the hybrid MHA-ANN was superior for streamflow forecasting, especially with the firefly algorithm that had an average RMSE = 96.06, an improvement of approximately 17% over the gradient-based ANN (RMSE = 113.92). However, among the adopted MHAs, not all are compatible with optimizing the ANN for streamflow forecasting, thus requiring a thorough study as performance varies from case to case. Two additional statistical tests, such as the Kruskal-Wallis H test and the Mann-Whitney U test, further validated such disparity in the MHA’s performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adnan RM, Liang Z, Kuriqi A, Kisi O, Malik A, Li B (2020) Streamflow forecasting using heuristic machine learning methods. 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia
Al-Gharaibeh RS, Ali MZ, Daoud MI, Alazrai R, Abdel-Nabi H, Hriez S, Suganthan PN (2021) Real-parameter constrained optimization using enhanced quality-based cultural algorithm with novel influence and selection schemes. Information Sciences 576:242–273, DOI: https://doi.org/10.1016/j.ins.2021.06.057
Apaydin H, Sibtain M (2021) A multivariate streamflow forecasting model by integrating improved complete ensemble empirical mode decomposition with additive noise, sample entropy, Gini index and sequence-to-sequence approaches. Journal of Hydrology 603:126831, DOI: https://doi.org/10.1016/j.jhydrol.2021.126831
Areffian A, Eslamian S, Sadr MK, Khoshfetrat A (2021) Monitoring the effects of drought on vegetation cover and ground water using MODIS satellite images and ANN. KSCE Journal of Civil Engineering 25(3):1095–1105, DOI: https://doi.org/10.1007/s12205-021-2062-x
Birikundavyi S, Labib R, Trung HT, Rousselle J (2002) Performance of neural networks in daily streamflow forecasting. Journal of Hydrologic Engineering 7(5):392–398, DOI: https://doi.org/10.1061/(ASCE)1084-0699(2002)7:5(392)
Borky JM, Bradley TH (2019) Effective model-based systems engineering: Springer
Cachim P, Bezuijen A (2019) Modelling the torque with artificial neural networks on a tunnel boring machine. KSCE Journal of Civil Engineering 23(10):4529–4537, DOI: https://doi.org/10.1007/s12205-019-0302-0
Chaipimonplin T (2016) Investigation internal parameters of neural network model for flood forecasting at upper river Ping, Chiang Mai. KSCE Journal of Civil Engineering 20(1):478–484, DOI: https://doi.org/10.1007/s12205-015-1282-3
Chong KL, Lai SH, Ahmed AN, Zaafar WZW, Rao RV, Sherif M, Sefelnasr A, El-Shafie A (2021) Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms. IEEE Access 9:19488–19505, DOI: https://doi.org/10.1109/ACCESS.2021.3054424
Chong KL, Lai SH, Yao Y, Ahmed AN, Jaafar WZW, El-Shafie A (2020) Performance enhancement model for rainfall forecasting utilizing integrated wavelet-convolutional neural network. Water Resources Management 34(8):2371–2387, DOI: https://doi.org/10.1007/s11269-020-02554-z
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26(1):29–41, DOI: https://doi.org/10.1109/3477.484436
Du Y-C, Stephanus A (2018) Levenberg-marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor. Sensors 18(7): 2322, DOI: https://doi.org/10.3390/s18072322
Gandomi AH, Yang X-S, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Computers & Structures 89(23–24):2325–2336, DOI: https://doi.org/10.1016/j.compstruc.2011.08.002
Golberg D (1989) Machine learning and its applications. In: Springer Berlin
Gouravaraju S, Narayan J, Sauer RA, Gautam SS (2021) A bayesian regularization-backpropagation neural network model for peeling computations. The Journal of Adhesion 1–24, DOI: https://doi.org/10.1080/00218464.2021.2001335
Guillot J, Restrepo-Leal D, Robles-Algarín C, Oliveros I (2019) Search for global maxima in multimodal functions by applying numerical optimization algorithms: A comparison between golden section and simulated annealing. Computation 7(3):43, DOI: https://doi.org/10.3390/computation7030043
Hussain K, Salleh MNM, Cheng S, Shi Y (2019) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Computing and Applications 31(11):7665–7683, DOI: https://doi.org/10.1007/s00521-018-3592-0
Jahandideh-Tehrani M, Jenkins G, Helfer F (2021) A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: A case study for southeast queensland, Australia. Optimization and Engineering 22(1):29–50, DOI: https://doi.org/10.1007/s11081-020-09538-3
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of ICNN’95-international conference on neural networks, Perth, WA, Australia
Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. Journal of Hydrologic Engineering 12(5):532–539, DOI: https://doi.org/10.1061/(ASCE)1084-0699(2007)12:5(532)
Meresa H (2019) Modelling of river flow in ungauged catchment using remote sensing data: Application of the empirical (SCS-CN), artificial neural network (ANN) and hydrological model (HEC-HMS). Modeling Earth Systems and Environment 5(1):257–273, DOI: https://doi.org/10.1007/s40808-018-0532-z
Miller OL, Putman AL, Alder J, Miller M, Jones DK, Wise DR (2021) Changing climate drives future streamflow declines and challenges in meeting water demand across the southwestern United States. Journal of Hydrology X 11:100074, DOI: https://doi.org/10.1016/j.hydroa.2021.100074
Nearing GS, Kratzert F, Sampson AK, Pelissier CS, Klotz D, Frame JM, Prieto C, Gupta HV (2021) What role does hydrological science play in the age of machine learning? Water Resources Research 57(3):e2020WR028091, DOI: https://doi.org/10.1029/2020WR028091
Pandiri V, Singh A, Rossi A (2020) Two hybrid metaheuristic approaches for the covering salesman problem. Neural Computing and Applications 32(19):15643–15663, DOI: https://doi.org/10.1007/s00521-020-04898-4
Rezaeianzadeh M, Tabari H, Arabi Yazdi A, 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
Riese FM, Keller S, Hinz S (2019) Supervised and semi-supervised self-organizing maps for regression and classification focusing on hyperspectral data. Remote Sensing 12(1):7, DOI: https://doi.org/10.3390/rs12010007
Sahoo BB, Jha R, Singh A, Kumar D (2019) Application of support vector regression for modeling low flow time series. KSCE Journal of Civil Engineering 23(2):923–934, DOI: https://doi.org/10.1007/s12205-018-0128-1
Samanataray S, Sahoo A (2021) A comparative study on prediction of monthly streamflow using hybrid ANFIS-PSO approaches. KSCE Journal of Civil Engineering 25(10):4032–4043, DOI: https://doi.org/10.1007/s12205-021-2223-y
Schumann G, Bates PD, Apel H, Aronica GT (2018) Global flood hazard mapping, modeling, and forecasting: Challenges and perspectives. Global Flood Hazard: Applications in Modeling, Mapping, and Forecasting 239–244, DOI: https://doi.org/10.1002/9781119217886.ch14
Swischuk R, Mainini L, Peherstorfer B, Willcox K (2019) Projection-based model reduction: Formulations for physics-based machine learning. Computers & Fluids 179:704–717, DOI: https://doi.org/10.1016/j.compfluid.2018.07.021
Tabbussum R, Dar AQ (2020) Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river. Journal of Flood Risk Management 13(4):e12656, DOI: https://doi.org/10.1111/jfr3.12656
Tayarani-N M-H, Yao X, Xu H (2014) Meta-heuristic algorithms in car engine design: A literature survey. IEEE Transactions on Evolutionary Computation 19(5):609–629, DOI: https://doi.org/10.1109/TEVC.2014.2355174
Tikhamarine Y, Souag-Gamane D, Ahmed AN, Kisi O, El-Shafie A (2020) Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm. Journal of Hydrology 582:124435, DOI: https://doi.org/10.1016/j.jhydrol.2019.124435
Valdez F, Melin P, Castillo O (2014) A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Systems with Applications 41(14):6459–6466, DOI: https://doi.org/10.1016/j.eswa.2014.04.015
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1):67–82, DOI: https://doi.org/10.1109/4235.585893
Wu J, Wang Z (2022) A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water 14(4):610, DOI: https://doi.org/10.3390/w14040610
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65–74). Berlin, Heidelberg: Springer
Zhang X, Peng Y, Zhang C, Wang B (2015) Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences. Journal of Hydrology 530:137–152, DOI: https://doi.org/10.1016/j.jhydrol.2015.09.047
Zhao X, Lv H, Lv S, Sang Y, Wei Y, Zhu X (2021) Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer. Journal of Hydrology 601:126607, DOI: https://doi.org/10.1016/j.jhydrol.2021.126607
Zhou F, Hu P, Yang S, Wen C (2018) A multimodal feature fusion-based deep learning method for online fault diagnosis of rotating machinery. Sensors 18(10):3521, DOI: https://doi.org/10.3390/s18103521
Acknowledgments
We gratefully acknowledge the contributions of all the co-authors in this research study.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wei, Y., Hashim, H., Chong, K.L. et al. Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting. KSCE J Civ Eng 27, 2297–2312 (2023). https://doi.org/10.1007/s12205-023-0821-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-023-0821-6