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Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming\u2010generalized reduced gradient (MGGP\u2010GRG) technique for estimating river discharges for steady as well as unsteady flows. It also compares the MGGP\u2010GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP\u2010GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP\u2010GRG model overall performs the best in developing stage\u2010discharge relationships for both single\u2010value and loop rating curves. For instance, the hybrid MGGP\u2010GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence (AI) models (ANN and MGGP) performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model.<\/jats:p>","DOI":"10.1155\/2021\/6627011","type":"journal-article","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T04:50:50Z","timestamp":1614142250000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Assessment of Artificial Intelligence Models for Developing Single\u2010Value and Loop Rating Curves"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5022-1026","authenticated-orcid":false,"given":"Majid","family":"Niazkar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4295-004X","authenticated-orcid":false,"given":"Mohammad","family":"Zakwan","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.2166\/hydro.2018.023"},{"key":"e_1_2_8_2_2","first-page":"1","article-title":"Numerical analysis of hysteresis in rating curves for open channel flow","volume":"25","author":"Trava\u0161 V.","year":"2012","journal-title":"International Journal for Engineering Modelling"},{"key":"e_1_2_8_3_2","volume-title":"Stage-discharge Relationships in Open Channels: Practices and Problems","author":"Braca G.","year":"2008"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.20897\/awet.201702"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2018.07.025"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/2014wr016618"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.2166\/hydro.2012.127"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/02626667.2010.504186"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.2166\/nh.2010.098"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2017.09.022"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-019-02384-8"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-9429(2008)134:9(1245)"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)he.1943-5584.0000459"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.2166\/nh.2019.021"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1623\/hysj.51.3.365"},{"key":"e_1_2_8_16_2","first-page":"10","article-title":"Modelling of stage-discharge relationship","volume":"7","author":"Zakwan M.","year":"2018","journal-title":"NDCWWC Journal (A Half Yearly Journal of New Delhi Centre of WWC)"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13201-018-0745-3"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0887-3801(1997)11:3(206)"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)1084-0699(2003)8:3(161)"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1623\/hysj.48.2.197.44697"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-005-6811-2"},{"key":"e_1_2_8_22_2","doi-asserted-by":"publisher","DOI":"10.5829\/idosi.ije.2012.25.01a.01"},{"key":"e_1_2_8_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-011-9845-7"},{"key":"e_1_2_8_24_2","doi-asserted-by":"publisher","DOI":"10.20897\/awet\/xxxxx"},{"key":"e_1_2_8_25_2","article-title":"An optimization technique for estimation of rating curve parameters","volume":"2015","author":"Muzzammil M.","year":"2015","journal-title":"National Symposium on Hydrology"},{"key":"e_1_2_8_26_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781482265880"},{"key":"e_1_2_8_27_2","unstructured":"FreadD. 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