{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T21:45:31Z","timestamp":1766180731590,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2014,11,5]],"date-time":"2014-11-05T00:00:00Z","timestamp":1415145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The accuracy of reservoir flow forecasting has the most significant influence on the assurance of stability and annual operations of hydro-constructions. For instance, accurate forecasting on the ebb and flow of Vietnam\u2019s Hoabinh Reservoir can aid in the preparation and prevention of lowland flooding and drought, as well as regulating electric energy. This raises the need to propose a model that accurately forecasts the incoming flow of the Hoabinh Reservoir. In this study, a solution to this problem based on neural network with the Cuckoo Search (CS) algorithm is presented. In particular, we used hydrographic data and predicted total incoming flows of the Hoabinh Reservoir over a period of 10 days. The Cuckoo Search algorithm was utilized to train the feedforward neural network (FNN) for prediction. The algorithm optimized the weights between layers and biases of the neuron network. Different forecasting models for the three scenarios were developed. The constructed models have shown high forecasting performance based on the performance indices calculated. These results were also compared with those obtained from the neural networks trained by the particle swarm optimization (PSO) and back-propagation (BP), indicating that the proposed approach performed more effectively. Based on the experimental results, the scenario using the rainfall and the flow as input yielded the highest forecasting accuracy when compared with other scenarios. The performance criteria RMSE, MAPE, and R obtained by the CS-FNN in this scenario were calculated as 48.7161, 0.067268 and 0.8965, respectively. These results were highly correlated to actual values. It is expected that this work may be useful for hydrographic forecasting.<\/jats:p>","DOI":"10.3390\/info5040570","type":"journal-article","created":{"date-parts":[[2014,11,6]],"date-time":"2014-11-06T02:48:56Z","timestamp":1415242136000},"page":"570-586","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Forecasting Hoabinh Reservoir\u2019s Incoming Flow: An Application of Neural Networks with the Cuckoo Search Algorithm"],"prefix":"10.3390","volume":"5","author":[{"given":"Jeng-Fung","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan"}]},{"given":"Ho-Nien","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8937-5102","authenticated-orcid":false,"given":"Quang","family":"Do","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, University of Transport Technology, Hanoi 100000, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2014,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4160","DOI":"10.1021\/ie801666u","article-title":"Neural Network Prediction of Interfacial Tension at Crystal\/Solution Interface","volume":"48","author":"Kumar","year":"2009","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.fluid.2013.08.018","article-title":"Utilization of support vector machine to calculate gas compressibility factor","volume":"358","author":"Chamkalani","year":"2013","journal-title":"Fluid Phase Equilib."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.fuel.2013.01.056","article-title":"A new screening tool for evaluation of steamflooding performance in Naturally Fractured Carbonate Reservoirs","volume":"108","author":"Shafiei","year":"2013","journal-title":"Fuel"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1021\/ie2017459","article-title":"Artificial Neural Network Modeling of Surface Tension for Pure Organic Compounds","volume":"51","author":"Roosta","year":"2011","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6009","DOI":"10.1021\/ie301949c","article-title":"Thermodynamic Investigation of Asphaltene Precipitation during Primary Oil Production: Laboratory and Smart Technique","volume":"52","author":"Zendehboudi","year":"2013","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1061\/(ASCE)1084-0699(2001)6:5(367)","article-title":"Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks","volume":"6","author":"Coulibaly","year":"2001","journal-title":"J. Hydrol. Eng."},{"key":"ref_7","unstructured":"Can, I., Yerdelen, C., and Kahya1, E. (2007, January 19\u201321). Stochastic Modeling of Karasu River (Turkey) Using the Methods of Artificial Neural Networks. Proceedings of the AGU Hydrology Days, Colorado, CO, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1080\/00221680209499899","article-title":"Artificial neural networks for stream flow prediction","volume":"40","author":"Dolling","year":"2002","journal-title":"J. Hydraul. Res."},{"key":"ref_9","unstructured":"Kilin\u015f, I., and Ci\u011fizou\u011flu, K. Reservoir Management Using Artificial Neural Networks. Available online:http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.104.174."},{"key":"ref_10","unstructured":"Lekkas, D.F., and Onof, C. (2005, January 1\u20133). Improved Flow Forecasting Using Artificial Neural Networks. Proceedings of the 9th International Conference on Environmental Science and Technology, Rhodes island, Greece."},{"key":"ref_11","unstructured":"Nguyen, V.H., Cuong, T.H., and Pham, T.H.N. (2007, January 4\u20136). Hoabinh Reservoir Incoming Flow Forecast for the Period of 10 Days with Neural Networks. Proceedings of Scientific Research in Open Universities\u2019 HS-IC2007, CatBa, Vietnam."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1007\/s00521-013-1419-6","article-title":"Monthly flow forecast for Mississippi River basin using artificial neural networks","volume":"24","author":"Sivapragasam","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/79.180705","article-title":"Progress in supervised neural networks","volume":"10","author":"Hush","year":"1993","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training feedforward networks with the Marquardt algorithm","volume":"5","author":"Hagar","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/0096-3003(94)90134-1","article-title":"An adaptive conjugate gradient learning algorithm for efficient training of neural networks","volume":"62","author":"Adeli","year":"1994","journal-title":"Appl. Math. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.amc.2009.02.038","article-title":"An online gradient method with momentum for two-layer feedforward neural networks","volume":"212","author":"Zhang","year":"2009","journal-title":"Appl. Math. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mingguang, L., and Gaoyang, L. (2009, January 12\u201314). Artificial Neural Network Co-optimization Algorithm Based on Differential Evolution. Proceedings of the Second International Symposium on Computational Intelligence and Design, Changsha, China.","DOI":"10.1109\/ISCID.2009.71"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/S0305-0483(99)00027-4","article-title":"Comparing backpropagation with a genetic algorithm for neural network training","volume":"27","author":"Gupta","year":"1999","journal-title":"Omega"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11125","DOI":"10.1016\/j.amc.2012.04.069","article-title":"Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm","volume":"218","author":"Mirjalili","year":"2012","journal-title":"Appl. Math. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, X.S., and Deb, S. (2009, January 9\u201311). Cuckoo Search via L\u00e9vy Flights. Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC), Coimbatore, India.","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"ref_21","first-page":"330","article-title":"Engineering optimisation by cuckoo search","volume":"1","author":"Yang","year":"2010","journal-title":"Int. J. Math. Model. Numer. Optim."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, X.-S., Deb, S., Karamanoglu, M., and He, X. (2012, January 21\u201322). Cuckoo Search for Business Optimization Applications. Proceedings of National Conference on Computing and Communication Systems (NCCCS), Durgapur, India.","DOI":"10.1109\/NCCCS.2012.6412973"},{"key":"ref_23","first-page":"156","article-title":"Metaheuristic Optimization Algorithms for Training Artificial Neural Networks","volume":"1","author":"Kawam","year":"2012","journal-title":"Int. J. Comput. Inf. Technol."},{"key":"ref_24","first-page":"36","article-title":"Improved Cuckoo Search algorithm for feed forward neural network training","volume":"2","author":"Valian","year":"2011","journal-title":"Int. J. Artif. Intell. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1002\/clen.200900238","article-title":"Prediction of Density Flow Plunging Depth in Dam Reservoirs: An Artificial Neural Network Approach","volume":"38","year":"2010","journal-title":"CLEAN Soil Air Water"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.5194\/hess-13-1607-2009","article-title":"River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: Case study of the Ganges river basin","volume":"13","author":"Akhtar","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.1016\/j.apm.2011.09.048","article-title":"Performance evaluation of artificial neural network approaches in forecasting reservoir inflow","volume":"36","author":"Yureklib","year":"2012","journal-title":"Appl. Math. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1002\/hyp.1492","article-title":"Forecasting flows in Apalachicola River using neural networks","volume":"18","author":"Huang","year":"2004","journal-title":"Hydrol. Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/34.107014","article-title":"On the problem of local minima in back-propagation","volume":"14","author":"Gori","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1016\/j.amc.2006.07.025","article-title":"A hybrid particle swarm optimization\u2014back-propagation algorithm for feedforward neural network training","volume":"185","author":"Zhang","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_31","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley."},{"key":"ref_32","unstructured":"Kennedy, J., and Eberhart, R.C. (December, January 27). Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/3477.484436","article-title":"Ant system: Optimization by a colony of cooperating agents","volume":"26","author":"Dorigo","year":"1996","journal-title":"IEEE Trans. Systems Man Cybern. Part B Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1080\/08839514.2014.904599","article-title":"Cuckoo Search Algorithm for Optimization Problems\u2014A Literature Review and its Applications","volume":"28","author":"Mohamad","year":"2014","journal-title":"Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s00521-013-1367-1","article-title":"Cuckoo search: Recent advances and applications","volume":"24","author":"Yang","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, X.-S. (2014). Cuckoo Search and Firefly Algorithm, Springer.","DOI":"10.1007\/978-3-319-02141-6"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.chaos.2011.06.004","article-title":"Modified cuckoo search: A new gradient free optimization algorithm","volume":"44","author":"Walton","year":"2011","journal-title":"Chaos Solitons Fractals"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.fluid.2013.05.011","article-title":"Comment on Cuckoo search: A new nature-inspired optimization method for phase equilibrium calculations by V. Bhargava, S. Fateen, A. Bonilla-Petriciolet","volume":"352","author":"Walton","year":"2013","journal-title":"Fluid Phase Equilib."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0893-6080(89)90003-8","article-title":"On the approximate realization of continuous mappings by neural networks","volume":"2","author":"Funahashi","year":"1989","journal-title":"Neural Netw."},{"key":"ref_40","unstructured":"Norgaard, M.R., Ravn, O., Poulsen, N.K., and Hansen, L.K. (2000). Neural Networks for Modeling and Control of Dynamic Systems: A Practitioner\u2019s Handbook, Springer."},{"key":"ref_41","unstructured":"Caruana, R., Lawrence, S., and Giles, C.L. (2001, January 3\u20138). Overfitting in Neural Networks: Backpropagation, Conjugate Gradient, and Early Stopping. Proceedings of 13th Conference on Advances Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/0893-6080(90)90005-6","article-title":"Universal Approximation of an unknown Mapping and its Derivatives Using Multilayer Feed forward Networks","volume":"3","author":"Hornik","year":"1990","journal-title":"Neural Netw."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superposition of a sigmoid function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control Signals Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TPAMI.2008.88","article-title":"Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals","volume":"31","author":"Bhattacharya","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1007\/s00521-011-0778-0","article-title":"Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks","volume":"22","author":"Dogan","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_46","unstructured":"(2014). MATLAB, The MathWorks, Inc."},{"key":"ref_47","first-page":"47","article-title":"Interpreting neural-network connection weights","volume":"6","author":"Garson","year":"1991","journal-title":"AI Expert"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/5\/4\/570\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:08:58Z","timestamp":1760216938000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/5\/4\/570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11,5]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2014,12]]}},"alternative-id":["info5040570"],"URL":"https:\/\/doi.org\/10.3390\/info5040570","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2014,11,5]]}}}