{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:22:09Z","timestamp":1774315329205,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515011520"],"award-info":[{"award-number":["2023A1515011520"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-024-00699-y","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T10:12:29Z","timestamp":1732615949000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge\u2013Kutta with Aquila Optimizer"],"prefix":"10.1007","volume":"17","author":[{"given":"Rana Muhammad","family":"Adnan","sequence":"first","affiliation":[]},{"given":"Wang","family":"Mo","sequence":"additional","affiliation":[]},{"given":"Ahmed A.","family":"Ewees","sequence":"additional","affiliation":[]},{"given":"Salim","family":"Heddam","sequence":"additional","affiliation":[]},{"given":"Ozgur","family":"Kisi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1421-8671","authenticated-orcid":false,"given":"Mohammad","family":"Zounemat-Kermani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"key":"699_CR1","doi-asserted-by":"publisher","first-page":"107250","DOI":"10.1016\/j.cie.2021.107250","volume":"157","author":"L Abualigah","year":"2021","unstructured":"Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A.A., Al-Qaness, M.A., Gandomi, A.H.: Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput. Ind. Eng. 157, 107250 (2021). https:\/\/doi.org\/10.1016\/j.cie.2021.107250","journal-title":"Comput. Ind. Eng."},{"issue":"6","key":"699_CR2","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s13201-023-01943-0","volume":"13","author":"F Ahmadi","year":"2023","unstructured":"Ahmadi, F., Tohidi, M., Sadrianzade, M.: Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches. Appl Water Sci 13(6), 135 (2023)","journal-title":"Appl Water Sci"},{"key":"699_CR3","doi-asserted-by":"publisher","first-page":"115079","DOI":"10.1016\/j.eswa.2021.115079","volume":"181","author":"I Ahmadianfar","year":"2021","unstructured":"Ahmadianfar, I., Heidari, A.A., Gandomi, A.H., Chu, X., Chen, H.: RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst. Appl. 181, 115079 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.115079","journal-title":"Expert Syst. Appl."},{"key":"699_CR4","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3390\/en12020329","volume":"12","author":"RM Adnan","year":"2019","unstructured":"Adnan, R.M., Liang, Z., Yuan, X., Kisi, O., Akhlaq, M., Li, B.: Comparison of LSSVR, M5RT, NF-GP, and NF-SC models for predictions of hourly wind speed and wind power based on cross-validation. Energies 12, 329 (2019). https:\/\/doi.org\/10.3390\/en12020329","journal-title":"Energies"},{"key":"699_CR5","doi-asserted-by":"publisher","first-page":"123981","DOI":"10.1016\/j.jhydrol.2019.123981","volume":"577","author":"RM Adnan","year":"2019","unstructured":"Adnan, R.M., Liang, Z., Trajkovic, S., Zounemat-Kermani, M., Li, B., Kisi, O.: Daily streamflow prediction using optimally pruned extreme learning machine. J. Hydrol. 577, 123981 (2019). https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123981","journal-title":"J. Hydrol."},{"key":"699_CR6","doi-asserted-by":"publisher","first-page":"12063","DOI":"10.1016\/j.egyr.2022.09.015","volume":"8","author":"RM Adnan","year":"2022","unstructured":"Adnan, R.M., Dai, H.-L., Ewees, A.A., Shiri, J., Kisi, O., Zounemat-Kermani, M.: Application of improved version of multi verse optimizer algorithm for modeling solar radiation. Energy Rep. 8, 12063\u201312080 (2022). https:\/\/doi.org\/10.1016\/j.egyr.2022.09.015","journal-title":"Energy Rep."},{"issue":"11","key":"699_CR7","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.3390\/w10111676","volume":"10","author":"Z Alizadeh","year":"2018","unstructured":"Alizadeh, Z., Yazdi, J., Kim, J.H., Al-Shamiri, A.K.: Assessment of machine learning techniques for monthly flow prediction. Water 10(11), 1676 (2018)","journal-title":"Water"},{"key":"699_CR8","doi-asserted-by":"crossref","unstructured":"Aljahdali, S., Sheta, A., Turabieh, H.: River flow forecasting: a comparison between feedforward and layered recurrent neural network. In: Innovation in Information Systems and Technologies to Support Learning Research: Proceedings of EMENA-ISTL 2019, vol. 3, pp. 523\u2013532. Springer (2020)","DOI":"10.1007\/978-3-030-36778-7_58"},{"issue":"1","key":"699_CR9","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1002\/hyp.1313","volume":"18","author":"LC Chang","year":"2004","unstructured":"Chang, L.C., Chang, F.J., Chiang, Y.M.: A two-step-ahead recurrent neural network for stream-flow forecasting. Hydrol. Process. 18(1), 81\u201392 (2004)","journal-title":"Hydrol. Process."},{"issue":"3","key":"699_CR10","doi-asserted-by":"publisher","first-page":"186","DOI":"10.3390\/w9030186","volume":"9","author":"KW Chau","year":"2017","unstructured":"Chau, K.W.: Use of meta-heuristic techniques in rainfall-runoff modelling. Water 9(3), 186 (2017)","journal-title":"Water"},{"key":"699_CR11","doi-asserted-by":"publisher","first-page":"110408","DOI":"10.1016\/j.asoc.2023.110408","volume":"143","author":"S Davoodi","year":"2023","unstructured":"Davoodi, S., Thanh, H.V., Wood, D.A., Mehrad, M., Rukavishnikov, V.S.: Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations. Appl. Soft Comput. 143, 110408 (2023). https:\/\/doi.org\/10.1016\/j.asoc.2023.110408","journal-title":"Appl. Soft Comput."},{"key":"699_CR12","doi-asserted-by":"publisher","first-page":"106580","DOI":"10.1016\/j.knosys.2020.106580","volume":"211","author":"ZK Feng","year":"2021","unstructured":"Feng, Z.K., Niu, W.J.: Hybrid artificial neural network and cooperation search algorithm for nonlinear river flow time series forecasting in humid and semi-humid regions. Knowl.-Based Syst. 211, 106580 (2021)","journal-title":"Knowl.-Based Syst."},{"issue":"3","key":"699_CR13","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1061\/(ASCE)1084-0699(2009)14:3(286)","volume":"14","author":"DA Fernando","year":"2009","unstructured":"Fernando, D.A., Shamseldin, A.Y.: Investigation of internal functioning of the radial-basis-function neural network river flow forecasting models. J. Hydrol. Eng. 14(3), 286\u2013292 (2009)","journal-title":"J. Hydrol. Eng."},{"key":"699_CR14","doi-asserted-by":"crossref","unstructured":"Ganesan, V., Talluru, T., Challapalli, M., Seelam, C.: Identifying river drainage characteristics by deep neural network. In: Smart Technologies in Data Science and Communication: Proceedings of SMART-DSC 2022, pp. 71\u201379. Singapore: Springer Nature Singapore (2023)","DOI":"10.1007\/978-981-19-6880-8_7"},{"issue":"1","key":"699_CR15","doi-asserted-by":"publisher","first-page":"11166","DOI":"10.1038\/s41598-022-15127-4","volume":"12","author":"W Guo","year":"2022","unstructured":"Guo, W., Jiao, X., Zhou, H., Zhu, Y., Wang, H.: Hydrologic regime alteration and influence factors in the Jialing River of the Yangtze River, China. Sci. Rep. 12(1), 11166 (2022)","journal-title":"Sci. Rep."},{"key":"699_CR16","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1016\/j.jhydrol.2018.04.054","volume":"562","author":"MA Ghorbani","year":"2018","unstructured":"Ghorbani, M.A., Khatibi, R., Mehr, A.D., Asadi, H.: Chaos-based multigene genetic programming: a new hybrid strategy for river flow forecasting. J. Hydrol. 562, 455\u2013467 (2018)","journal-title":"J. Hydrol."},{"issue":"1","key":"699_CR17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.2166\/h2oj.2022.134","volume":"5","author":"G Hayder","year":"2022","unstructured":"Hayder, G., Iwan Solihin, M., Najwa, M.R.N.: Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM. H2Open J. 5(1), 43\u201360 (2022)","journal-title":"H2Open J."},{"key":"699_CR18","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence","author":"JH Holland","year":"1992","unstructured":"Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press (1992)"},{"key":"699_CR19","volume-title":"Adaptation in Natural and Artificial Systems","author":"JH Holland","year":"1975","unstructured":"Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)"},{"key":"699_CR20","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1007\/s12145-020-00450-z","volume":"13","author":"D Hussain","year":"2020","unstructured":"Hussain, D., Khan, A.A.: Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan. Earth Sci. Inf. 13, 939\u2013949 (2020)","journal-title":"Earth Sci. Inf."},{"issue":"3","key":"699_CR21","doi-asserted-by":"publisher","first-page":"658","DOI":"10.2166\/nh.2017.111","volume":"49","author":"S Karimi","year":"2018","unstructured":"Karimi, S., Shiri, J., Kisi, O., Xu, T.: Forecasting daily streamflow values: assessing heuristic models. Hydrol. Res. 49(3), 658\u2013669 (2018)","journal-title":"Hydrol. Res."},{"issue":"4","key":"699_CR22","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1623\/hysj.51.4.588","volume":"51","author":"ME Keskin","year":"2006","unstructured":"Keskin, M.E., Taylan, D., Terzi, O.: Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrol. Sci. J. 51(4), 588\u2013598 (2006)","journal-title":"Hydrol. Sci. J."},{"issue":"15","key":"699_CR23","doi-asserted-by":"publisher","first-page":"21935","DOI":"10.1007\/s11356-021-17443-0","volume":"29","author":"H Khodakhah","year":"2022","unstructured":"Khodakhah, H., Aghelpour, P., Hamedi, Z.: Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH. Environ. Sci. Pollut. Res. 29(15), 21935\u201321954 (2022)","journal-title":"Environ. Sci. Pollut. Res."},{"issue":"1","key":"699_CR24","doi-asserted-by":"publisher","first-page":"27","DOI":"10.2166\/nh.2008.026","volume":"39","author":"\u00d6 Ki\u015fi","year":"2008","unstructured":"Ki\u015fi, \u00d6.: River flow forecasting and estimation using different artificial neural network techniques. Hydrol. Res. 39(1), 27\u201340 (2008)","journal-title":"Hydrol. Res."},{"key":"699_CR25","doi-asserted-by":"publisher","first-page":"8596","DOI":"10.3390\/su13158596","volume":"13","author":"O Kisi","year":"2021","unstructured":"Kisi, O.: Machine learning with metaheuristic algorithms for sustainable water resources management. Sustainability 13, 8596 (2021). https:\/\/doi.org\/10.3390\/su13158596","journal-title":"Sustainability"},{"key":"699_CR26","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.compag.2016.01.026","volume":"122","author":"O Kisi","year":"2016","unstructured":"Kisi, O., Genc, O., Dinc, S., Zounemat-Kermani, M.: Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree. Comput. Electron. Agric. 122, 112\u2013117 (2016)","journal-title":"Comput. Electron. Agric."},{"key":"699_CR27","doi-asserted-by":"publisher","first-page":"50","DOI":"10.4236\/jwarp.2011.31006","volume":"3","author":"B Krishna","year":"2011","unstructured":"Krishna, B., Satyaji, Y.R., Nayak, P.C.: Time series modeling of river flow using wavelet neural networks. J. Water Resour. Prot. 3, 50\u201359 (2011)","journal-title":"J. Water Resour. Prot."},{"key":"699_CR28","doi-asserted-by":"publisher","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN'95-International Conference on Neural Networks, vol. 4, pp. 1942\u20131948. IEEE (1995). https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"key":"699_CR29","doi-asserted-by":"publisher","first-page":"110329","DOI":"10.1016\/j.asoc.2023.110329","volume":"142","author":"L Kumar","year":"2023","unstructured":"Kumar, L., Pandey, M., Ahirwal, M.K.: Parallel global best-worst particle swarm optimization algorithm for solving optimization problems. Appl. Soft Comput. 142, 110329 (2023). https:\/\/doi.org\/10.1016\/j.asoc.2023.110329","journal-title":"Appl. Soft Comput."},{"issue":"14","key":"699_CR30","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.3390\/w15142572","volume":"15","author":"V Kumar","year":"2023","unstructured":"Kumar, V., Kedam, N., Sharma, K.V., Mehta, D.J., Caloiero, T.: Advanced machine learning techniques to improve hydrological prediction: a comparative analysis of streamflow prediction models. Water 15(14), 2572 (2023)","journal-title":"Water"},{"key":"699_CR31","unstructured":"Kutta, W.: Beitrag zur n\u00e4herungsweisen Integration totaler Differentialgleichungen. Teubner (1901)"},{"key":"699_CR32","unstructured":"Le Coz, J.: A literature review of methods for estimating the uncertainty associated with stage-discharge relations. WMO Rep. PO6a, 21 (2012)"},{"key":"699_CR33","doi-asserted-by":"publisher","first-page":"3493","DOI":"10.1007\/s00382-017-3525-0","volume":"49","author":"T Lee","year":"2017","unstructured":"Lee, T., Ouarda, T.B., Yoon, S.: KNN-based local linear regression for the analysis and simulation of low flow extremes under climatic influence. Clim. Dyn. 49, 3493\u20133511 (2017)","journal-title":"Clim. Dyn."},{"issue":"5","key":"699_CR34","doi-asserted-by":"publisher","first-page":"859","DOI":"10.5194\/hess-6-859-2002","volume":"6","author":"Z Liu","year":"2002","unstructured":"Liu, Z., Todini, E.: Towards a comprehensive physically-based rainfall-runoff model. Hydrol. Earth Syst. Sci. 6(5), 859\u2013881 (2002)","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"699_CR35","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46\u201361 (2014). https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv. Eng. Softw."},{"issue":"19","key":"699_CR36","doi-asserted-by":"publisher","first-page":"10720","DOI":"10.3390\/su131910720","volume":"13","author":"MA Musarat","year":"2021","unstructured":"Musarat, M.A., Alaloul, W.S., Rabbani, M.B.A., Ali, M., Altaf, M., Fediuk, R., Vatin, N., Klyuev, S., Bukhari, H., Sadiq, A., Farooq, W.: Kabul river flow prediction using automated ARIMA forecasting: a machine learning approach. Sustainability 13(19), 10720 (2021)","journal-title":"Sustainability"},{"key":"699_CR37","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1007\/s11269-020-02756-5","volume":"35","author":"H Riahi-Madvar","year":"2021","unstructured":"Riahi-Madvar, H., Dehghani, M., Memarzadeh, R., Gharabaghi, B.: Short to long-term forecasting of river flows by heuristic optimization algorithms hybridized with ANFIS. Water Resour. Manag. 35, 1149\u20131166 (2021)","journal-title":"Water Resour. Manag."},{"issue":"2","key":"699_CR38","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/BF01446807","volume":"46","author":"C Runge","year":"1895","unstructured":"Runge, C.: \u00dcber die numerische Aufl\u00f6sung von Differentialgleichungen. Mathematische Annalen 46(2), 167\u2013178 (1895)","journal-title":"Mathematische Annalen"},{"issue":"5","key":"699_CR39","doi-asserted-by":"publisher","first-page":"101732","DOI":"10.1016\/j.asej.2022.101732","volume":"13","author":"S Samantaray","year":"2022","unstructured":"Samantaray, S., Das, S.S., Sahoo, A., Satapathy, D.P.: Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm. Ain Shams Eng. J. 13(5), 101732 (2022)","journal-title":"Ain Shams Eng. J."},{"key":"699_CR40","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1007\/s00704-021-03682-1","volume":"145","author":"Y Shao","year":"2021","unstructured":"Shao, Y., He, Y., Mu, X., Zhao, G., Gao, P., Sun, W.: Contributions of climate change and human activities to runoff and sediment discharge reductions in the Jialing River, a main tributary of the upper Yangtze River, China. Theor. Appl. Climatol. 145, 1437\u20131450 (2021)","journal-title":"Theor. Appl. Climatol."},{"key":"699_CR41","doi-asserted-by":"publisher","first-page":"3471","DOI":"10.1016\/j.rser.2012.02.044","volume":"16","author":"J Shi","year":"2012","unstructured":"Shi, J., Guo, J., Zheng, S.: Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renew. Sustain. Energy Rev. 16, 3471\u20133480 (2012)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"699_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10661-018-6768-2","volume":"190","author":"Y Seo","year":"2018","unstructured":"Seo, Y., Kim, S., Singh, V.P.: Comparison of different heuristic and decomposition techniques for river stage modeling. Environ. Monit. Assess. 190, 1\u201322 (2018)","journal-title":"Environ. Monit. Assess."},{"key":"699_CR43","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s11269-017-1796-1","volume":"32","author":"M Shoaib","year":"2018","unstructured":"Shoaib, M., Shamseldin, A.Y., Khan, S., Khan, M.M., Khan, Z.M., Sultan, T., Melville, B.W.: A comparative study of various hybrid wavelet feedforward neural network models for runoff forecasting. Water Resour. Manag. 32, 83\u2013103 (2018)","journal-title":"Water Resour. Manag."},{"key":"699_CR44","doi-asserted-by":"publisher","first-page":"107559","DOI":"10.1016\/j.engappai.2023.107559","volume":"129","author":"H Tao","year":"2024","unstructured":"Tao, H., Abba, S.I., Al-Areeq, A.M., Tangang, F., Samantaray, S., Sahoo, A., Yaseen, Z.M.: Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions. Eng. Appl. Artif. Intell. 129, 107559 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"699_CR45","unstructured":"Tritthart, M.: Three-dimensional numerical modelling of turbulent river flow using polyhedral finite volumes. Doctoral dissertation (2005)"},{"issue":"1","key":"699_CR46","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s13201-022-01831-z","volume":"13","author":"WJ Wee","year":"2023","unstructured":"Wee, W.J., Chong, K.L., Ahmed, A.N., Malek, M.B.A., Huang, Y.F., Sherif, M., Elshafie, A.: Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia. Appl Water Sci 13(1), 30 (2023)","journal-title":"Appl Water Sci"},{"issue":"3","key":"699_CR47","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1061\/(ASCE)1084-0699(2005)10:3(216)","volume":"10","author":"JS Wu","year":"2005","unstructured":"Wu, J.S., Han, J., Annambhotla, S., Bryant, S.: Artificial neural networks for forecasting watershed runoff and stream flows. J. Hydrol. Eng. 10(3), 216\u2013222 (2005)","journal-title":"J. Hydrol. Eng."},{"key":"699_CR48","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.measurement.2016.06.042","volume":"92","author":"B Yadav","year":"2016","unstructured":"Yadav, B., Ch, S., Mathur, S., Adamowski, J.: Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: a case study in Neckar River, Germany. Measurement 92, 433\u2013445 (2016)","journal-title":"Measurement"},{"key":"699_CR49","doi-asserted-by":"publisher","first-page":"106215","DOI":"10.1016\/j.engappai.2023.106215","volume":"123","author":"X Yang","year":"2023","unstructured":"Yang, X., Li, H., Huang, Y.: An adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion for solving continuous optimization problems. Eng. Appl. Artif. Intell. 123, 106215 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106215","journal-title":"Eng. Appl. Artif. Intell."},{"key":"699_CR50","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1007\/s00521-015-1952-6","volume":"27","author":"ZM Yaseen","year":"2016","unstructured":"Yaseen, Z.M., El-Shafie, A., Afan, H.A., Hameed, M., Mohtar, W.H.M.W., Hussain, A.: RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Computing and Applications 27, 1533\u20131542 (2016)","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"699_CR51","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s13201-022-01830-0","volume":"13","author":"WNCW Zanial","year":"2023","unstructured":"Zanial, W.N.C.W., Malek, M.B.A., Reba, M.N.M., Zaini, N., Ahmed, A.N., Sherif, M., Elshafie, A.: River flow prediction based on improved machine learning method: cuckoo search-artificial neural network. Appl Water Sci 13(1), 28 (2023)","journal-title":"Appl Water Sci"},{"key":"699_CR52","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.enconman.2018.10.089","volume":"180","author":"D Zhang","year":"2019","unstructured":"Zhang, D., Peng, X., Pan, K., Liu, Y.: A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine. Energy Convers. Manag. 180, 338\u2013357 (2019)","journal-title":"Energy Convers. Manag."},{"issue":"10","key":"699_CR53","doi-asserted-by":"publisher","first-page":"04019033","DOI":"10.1061\/(ASCE)HE.1943-5584.0001835","volume":"24","author":"M Zounemat-Kermani","year":"2019","unstructured":"Zounemat-Kermani, M., Kisi, O., Piri, J., Mahdavi-Meymand, A.: Assessment of artificial intelligence-based models and metaheuristic algorithms in modeling evaporation. J. Hydrol. Eng. 24(10), 04019033 (2019)","journal-title":"J. Hydrol. Eng."},{"key":"699_CR54","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/s12145-021-00599-1","volume":"14","author":"M Zounemat-Kermani","year":"2021","unstructured":"Zounemat-Kermani, M., Mahdavi-Meymand, A., Hinkelmann, R.: A comprehensive survey on conventional and modern neural networks: application to river flow forecasting. Earth Sci. Inf. 14, 893\u2013911 (2021)","journal-title":"Earth Sci. Inf."},{"key":"699_CR55","doi-asserted-by":"publisher","first-page":"125085","DOI":"10.1016\/j.jhydrol.2020.125085","volume":"588","author":"M Zounemat-Kermani","year":"2020","unstructured":"Zounemat-Kermani, M., Matta, E., Cominola, A., Xia, X., Zhang, Q., Liang, Q., Hinkelmann, R.: Neurocomputing in surface water hydrology and hydraulics: a review of two decades retrospective, current status and future prospects. J. Hydrol. 588, 125085 (2020)","journal-title":"J. Hydrol."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00699-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00699-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00699-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T11:15:23Z","timestamp":1732619723000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00699-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,26]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["699"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00699-y","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,26]]},"assertion":[{"value":"23 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"293"}}