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The Muskingham method uses two formulas to describe the translation of flow surges in a river bed. The continuity formula is the first formula, while the relationship between the reach’s storage, inflow, and outflow is the second formula (the discharge storage formula); these formulas are applied to a portion of the river between two river cross sections. Several methods can be utilized to estimate the model’s parameters. This section contrasts the conventional graphic approach with three numerical methods: Genetic algorithm, Exponential regression, and Classical fourth-order Runge-Kutta. This application’s most noticeable plus point was the need to employ a few hydrological variables, such as intake, output, and duration. The location of the Euphrates entrance to the Iraqi territory in Husaybah city was chosen with its hydrological data during the period (1993-2017) to conduct this study. The goal function is established by accuracy criterion approaches (Sum of squares error and sum of squared deviations). Depending on the simulation findings, the suggested predictive flood routing idea was highly acceptable with the prospect of adopting the Genetic Expression Programming model as a suitable and more accurate replacement to existing methods such as the Muskingum model and other numerical models, where this method gave results (R2 = 0.9984, SSQ = 1.06, SSSD = 80.75), These results achieved a hydrograph that is largely identical to what was given by the hydrological method called Muskingham.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
507--519
Opis fizyczny
Bibliogr. 34 poz., il., tab.
Twórcy
autor
- Department of Civil Engineering, College of Engineering/University of Babylon, Babylon, Iraq
autor
- Department of Building and Construction Technical, Al-Mussaib Technical College, Al-Furat Al-Awsat Technical University, Babylon, Iraq
autor
- Department of Civil Engineering, College of Engineering/Dijlah University College, Baghdad, Iraq
autor
- Building and Construction Engineering Technology Department, Al-Mustaqbal University College, Hillah, Iraq
- Al-Turath University College, Baghdad, Iraq
Bibliografia
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- [4] A.I. Requena, F. Chebana, and L. Mediero, “A complete procedure for multivariate index-flood model application”, Journal of Hydrology, vol. 535, pp. 559-580, 2016, doi: 10.1016/j.jhydrol.2016.02.004.
- [5] A. Sharafati, M. Haghbin, D. Motta, and Z.M. Yaseen, “The application of soft computing models and empirical formulations for hydraulic structure scouring depth simulation: a comprehensive review, assessment and possible future research direction”, Archives of Computational Methods in Engineering, vol. 28, no. 2, pp. 423-447, 2021, doi: 10.1007/s11831-019-09382-4.
- [6] I. Iskender and N. Sajikumar, “Evaluation of surface runoff estimation in ungauged watersheds using SWAT and GIUH”, Procedia Technology, vol. 24, pp. 109-115, 2016, doi: 10.1016/j.protcy.2016.05.016.
- [7] A. Kumar, “Geomorphologic instantaneous unit hydrograph based hydrologic response models for ungauged hilly watersheds in India”, Water Resources Management, vol. 29, no. 3, pp. 863-883, 2015, doi: 10.1007/s11269-014-0848-z.
- [8] A. Parsaie, A.H. Haghiabi, M. Saneie, and H. Torabi, “Prediction of energy dissipation of flow over stepped spillways using data-driven models”, Iranian Journal of Science and Technology, vol. 42, no. 1, pp. 39-53, 2018, doi: 10.1007/s40996-017-0060-5.
- [9] M. Najafzadeh, A. Tafarojnoruz, and S.Y. Lim, “Prediction of local scour depth downstream of sluice gates using data-driven models”, ISH Journal of Hydraulic Engineering, vol. 23, no. 2, pp. 195-202, 2017, doi: 10.1080/09715010.2017.1286614.
- [10] A. Pourzangbar, A. Saber, A. Yeganeh-Bakhtiary, and L.R. Ahari, “Predicting scour depth at seawalls using GP and ANNs”, Journal of Hydroinformatics, vol. 19, no. 3, pp. 349-363, 2017, doi: 10.2166/hydro.2017.125.
- [11] K.-S. Cheng, Y.-T. Lien, Y.-C. Wu, and Y.-F. Su, “On the criteria of model performance evaluation for real-time flood forecasting”, Stochastic Environmental Research and Risk Assessment, vol. 31, no. 5, pp. 1123-1146, 2017, doi: 10.1007/s00477-016-1322-7.
- [12] S. Karkheiran, A. Kabiri-Samani, M. Zekri, and H.M. Azamathulla, “Scour at bridge piers in uniform and armored beds under steady and unsteady flow conditions using ANN-APSO and ANN-GA algorithms”, ISH Journal of Hydraulic Engineering, vol. 27, no. sup1, pp. 220-228, 2021, doi: 10.1080/09715010.2019.1617796.
- [13] M. Esmaeili, “Prediction of scour depth around inclined bridge Piers group using optimized ANFIS system parameters with GA”, Journal of Water and Soil Conservation, vol. 22, no. 6, pp. 283-294, 2016, https://dorl.net/dor/20.1001.1.23222069.1394.22.6.18.0.
- [14] M.I. Brunner, D. Viviroli, A.E. Sikorska, O. Vannier, A. Favre, and J. Seibert, “Flood type specific construction of synthetic design hydrographs”, Water Resources Research, vol. 53, no. 2, pp. 1390-1406, 2017, doi: 10.1002/2016WR019535.
- [15] T.J. Wilkinson, L. Rayne, and J. Jotheri, “Hydraulic landscapes in Mesopotamia: The role of human niche construction”, Water History, vol. 7, no. 4, pp. 397-418, 2015, doi: 10.1007/s12685-015-0127-9.
- [16] F. Zaina, “A risk assessment for cultural heritage in southern Iraq: framing drivers, threats and actions affecting archaeological sites”, Conservation and Management of Archaeological Sites, vol. 21, no. 3, pp. 184-206, 2019, doi: 10.1080/13505033.2019.1662653.
- [17] O. Fevzi and O.O. Safak, “Flood routing model using genetic expression programing”, in 7th International Scientific Forum, ISF 2017. ESI, 2017, pp. 481-490.
- [18] E. Plebankiewicz, A. Leśniak, E.Vitkova, and V. Hromadka, “Models for estimating costs of public buildings maintaining-review and assessment”, Archives of Civil Engineering, vol. 68, no. 1, pp. 335-351, 2022, doi: 10.24425/ace.2022.140171.
- [19] A. Dziadosz and A. Kończak, “Review of selected methods of supporting decision-making process in the construction industry”, Archives of Civil Engineering, vol. 62, no. 1, pp. 111-126, 2016, doi: 10.1515/ace-2015-0055.
- [20] M.T. Ayvaz and G. Gurarslan, “A new partitioning approach for nonlinear Muskingum flood routing models with lateral flow contribution”, Journal of Hydrology, vol. 553, pp. 142-159, 2017, doi: 10.1016/j.jhydrol.2017.07.050.
- [21] T. Lillesand, R.W. Kiefer, and J. Chipman, Remote sensing and image interpretation. John Wiley & Sons, 2015.
- [22] G. Zucco, G. Tayfur, and T. Moramarco, “Reverse flood routing in natural channels using genetic algorithm”, Water Resources Management, vol. 29, no. 12, pp. 4241-4267, 2015, doi: 10.1007/s11269-015-1058-z.
- [23] M.A. Kadhim, N.K. Al-Bedyry, and I.I. Omran, “Evaluation of flood routing models and their relationship to the hydraulic properties of the Diyala River bed”, in IOP Conference Series: Earth and Environmental Science, vol. 961, no. 1, art. no. 12058, 2022, doi: 10.1088/1755-1315/961/1/012058.
- [24] M.A.A. Kadim, I.I. Omran, and A.A.S. Al-Taai, “Optimization of the nonlinear Muskingum model parameters for the river routing, Tigris River a case study”, International Journal of Design & Nature and Ecodynamics, vol. 16, no. 6, pp. 649-656, 2021, doi: 10.18280/ijdne.160605.
- [25] A.R. Vatankhah, “Discussion of ‘application of excel solver for parameter estimation of the nonlinear Muskingum models’ by Reza Barati”, KSCE Journal of Civil Engineering, vol. 19, no. 1, pp. 332-336, 2015, doi: 10.1007/s12205-014-1422-1.
- [26] M. Chybiński and Ł. Polus, “Experimental and numerical investigations of laminated veneer lumber panels”, Archives of Civil Engineering, vol. 67, no. 3, pp. 351-372, 2021, doi: 10.24425/ace.2021.138060.
- [27] D.J. Armaghani, et al., “On the use of neuro-swarm system to forecast the pile settlement”, Applied Sciences, vol. 10, no. 6, art. no. 1904, 2020, doi: 10.3390/app10061904.
- [28] A.H. Haghiabi, H.M. Azamathulla, and A. Parsaie, “Prediction of head loss on cascade weir using ANN and SVM”, ISH Journal of Hydraulic Engineering, vol. 23, no. 1, pp. 102-110, 2017, doi: 10.1080/09715010.2016.1241724.
- [29] H.-L. Nguyen, et al., “Development of hybrid artificial intelligence approaches and a support vector machine algorithm for predicting the marshall parameters of stone matrix asphalt”, Applied Sciences, vol. 9, no. 15, art. no. 3172, 2019, doi: 10.3390/app9153172.
- [30] A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications”, Future Generation Computer Systems, vol. 97, pp. 849-872, 2019, doi: 10.1016/j.future.2019.02.028.
- [31] A. Malik, A. Kumar, and O. Kisi, “Monthly pan-evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models”, Computers and Electronics in Agriculture, vol. 143, pp. 302-313, 2017, doi: 10.1016/j.compag.2017.11.008.
- [32] S. Rost, “Water management in Mesopotamia from the sixth till the first millennium BC”, Wiley Interdisciplinary Reviews: Water, vol. 4, no. 5, art. no. e1230, 2017, doi: 10.1002/wat2.1230.
- [33] R. Pappadà, E. Perrone, F. Durante, and G. Salvadori, “Spin-off Extreme Value and Archimedean copulas for estimating the bivariate structural risk”, Stochastic Environmental Research and Risk Assessment, vol. 30, no. 1, pp. 327-342, 2016, doi: 10.1007/s00477-015-1103-8.
- [34] N. Al-Ansari, “Hydro-politics of the Tigris and Euphrates basins”, Engineering, vol. 8, no. 3, pp. 140-172, 2016, doi: 10.4236/eng.2016.83015.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2bedc043-5e56-41da-9076-b85ce7382bdd