Hydrological Modeling Using Chicken Swarm Optimization Algorithm – Oued El Melah Case Study (NE of Algeria)

• Autorzy: Mohamed Sekkoum , Noureddine Oulad Naoui , El Amine Cherif

Identyfikatory

DOI

10.12912/27197050/189306

• Języki publikacji: EN

Abstrakty

EN

The aim of this article was hydrological modeling using chicken swarm optimization algorithm. Interactions between meteorological and hydrometric data been identified as a major conducting factor stationarity in rainfallrunoff relationships. The methodology employed in this work is rainfall-runoff modeling using chicken swarm optimization algorithm. This approach based on randomly selection and chicken society behavior. For the efficiency evaluation of chicken swarm optimization algorithm, a variety of statistical parameters have been used with acceptable values of Nash–Sutcliffe Efficiency (NSE) with 0.38% and index of agreement (IA) with 0.91%. The important and new results are the hydrological models (empirical models) adaptation to Algerian conditions especially in ungagged basins. Therefore, the proposal of the present modeling is a technique using the CSO method to model the rainfall-runoff relationship and discharge forecasting in the Oued El Melah basin located in Guelma city. CSO proves to be a valuable model for studies of the rainfall and flood runoff response to protection of agglomerations against flooding risks projections.

Słowa kluczowe

EN

water resources management; rainfall-runoff; Oued El Melah basin; flood forecasting; machine learning

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2024
• Tom: Vol. 25, iss. 8
• Strony: 109--119
• Opis fizyczny: Bibliogr. 47 poz., rys., tab.

Twórcy

autor

Mohamed Sekkoum

• Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Amar Telidji University, P. O. Box 37.G,03000 Laghouat, Algeria

• https://orcid.org/0000-0003-0325-8245

autor

Noureddine Oulad Naoui

• Material And Energies Reserch Laboratory, Faculty of Science and Technology, University Amine Elokkal El Hadj Moussa Eg Akhamouk Tamanrasset, Algeria

• https://orcid.org/0000-0003-0964-4614

autor

El Amine Cherif

• University of Science and Technology of Oran Mohamed Boudiaf, B.P. 1505, El M’Naouer Oran, Algeria

Bibliografia

• 1. Alam, F., Salam, M., Khalil, N.A., Khan, O., Khan, M. 2021. Rainfall trend analysis and weather forecast accuracy in selected parts of Khyber Pakhtunkhwa, Pakistan. SN Appl. Sci 3, 575. https://doi.org/10.1007/s42452-021-04457-z
• 2. Alzaatreh, A., Famoye, F., Lee, C. 2013. A new method for generating families of continuous distributions. Metron 71, 63–79.
• 3. Harrigan, S., Hannaford, J., Muchan K., Marsh, T.J. 2018. Designation and trend analysis of the updated UK benchmark network of river flow stations: the UKBN2 dataset. Hydrology Research 49, 552–567.
• 4. Chang, W., and Chen, X. 2018. Monthly rainfallrunoff modeling at watershed scale: a comparative study of data-driven and theory-driven approaches. Water 10, 1116–1137.
• 5. Criss, R.E, Winston, W.E. 2008. Do Nash values have value? Discussion and alternate proposals. Hydrological Processes 22(14), 2723–2725. Bibcode:2008HyPr...22.2723C. doi:10.1002/hyp.7072.
• 6. Chen, Y.L., He, P.L., Zhang, Y.H. 2015. Combining penalty function with modified chicken swarm optimization for constrained optimization. Advances in Intelligent Systems Research 126, 1899–1907.
• 7. Dariane, A.B., and Azimi, S. 2018. Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection. Journal of Hydroinformatics 20, 520–532.
• 8. Deb, S., Gao, X.Z., Tammi, K., Kalita, K., Mahanta, P. 2019. A New Teaching–Learning-based Chicken Swarm Optimization Algorithm. Soft Computing 24, 5313–5331.
• 9. Liu, D., Liu, C., Fu, Q., Li, T. 2018. Projection pursuit evaluation model of regional surface water environment based on improved chicken swarm optimization algorithm. Water Resources Management 32(4), 1325–1342.
• 10. Wu, D., Xu, S., and Kong, F. 2016. Convergence analysis and improvement of the chicken swarm optimization algorithm. IEEE Access 4, 9400–9412.
• 11. Fayomi, A., Khan, S., Tahir, M.H., Algarni, A., Jamal, F., Abu-Shanab R. 2022. A new extended gumbel distribution: Properties and application. PLOS ONE 17(5), e0267142. https://doi.org/10.1371/journal.,pone.0267142
• 12. Gupta, H.V., Sorooshian, S., Yapo, P.O. 2002. Status of automatic calibration for hydrologic models: Comparisonwith multilevel expert calibration. J. Hydrol. Eng 4, 135–143.
• 13. de Salis, H.H.C.A., da Costa, M., Vianna, J.H.M., Schuler, M.A., Künne, A., Fernandes, L.F.S., Fernando Pacheco, A.L. 2019. Hydrologic modeling for sustainable water resources management in urbanized karst areas, Int. J. Environ. Res. Public Health 16(14), 2542. https://doi.org/10.3390/ijerph16142542
• 14. Han, M., Liu, S. 2017. An Improved Binary Chicken Swarm Optimization Algorithm for Solving 0-1 Knapsack Problem. In Computational Intelligence and Security (CIS), 2017 13th International Conference on. 207–210.
• 15. Kumar, D., Singh, A., Samui, P., Jha, R.K. 2019. Forecasting monthly precipitation using sequential modelling. Hydrological Sciences Journal 64(6), 690– 700. https://doi.org/10.1080/02626667.2019.1595624
• 16. Wang, J., Cheng, Z., Ersoy, O.K., Zhang, M., Sun, K., Bi, Y..2019. Improvement and application of chicken swarm optimization for constrained optimization.
• 17. Kiem, A.S., Johnson, F., Westra, S., van Dijk, A., Evans, J.P., O’Donnell, A., Rouillard, A., Barr, C., Tyler, J., Thyer, M., Jakob, D., Woldemeskel, F., Sivakumar, B., Mehrotra, R. 2016. Natural hazards in Australia: droughts. Clim. Change 139, 37–54. https://doi.org/10.1007/s10584-016-1798-7
• 18. Fouad, M.M., Hafez, A.I., and Hassanien, A.E. 2019. Optimizing topologies in wireless sensor networks: a comparative analysis between the grey wolves and the chicken swarm optimization algorithms, Computer Network 163, 1–9.
• 19. Meng, X.B., Liu, Y., Gao, X., Zhang, H. 2014. A new bio-inspired algorithm: chicken swarm optimization. In International conference in swarm intelligence, 86–94. Springer, Cham.
• 20. McCuen, R.H., Knight, Z., Cutter, A.G. 2006. Evaluation of the Nash–Sutcliffe efficiency index. Journal of Hydrologic Engineering 11(6), 597–602. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(597).
• 21. Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE. 50(3), 885–900. https://doi.org/10.13031/2013.23153
• 22. Noureddine O.N., Cherif, E.A., Djehiche, A. 2017. Modeling of meteorological data optimization to study hydrological behavior of watersheds: case study – MZAB basin, southeast of Algeria. Desalination and Water Treatment 81, 95–104. https://doi.org/10.5004/dwt.2017.20811
• 23. Pignotti, G., Rathjens, H., Cibin, R., Chaubey, I., Crawford, M. 2017. Comparative analysis of HRU and grid-based SWAT models. Water 9, 272. https://doi.org/10.3390/w9040272
• 24. Radcliffe, D.E., Mukundan, R. 2017. PRISM vs. CFSR precipitation data effects on calibration and validation of SWAT models. Journal of the American Water Resources Association 53(1), 89–100. https://doi.org/10.1111/1752-1688.12484
• 25. Zarlis, R.M., and Yanto, I.T.R. 2016. A framework of training anfis using chicken swarm optimization for solving classification problem, in Proceedings of 1st International Conference on Informatics and Computing (ICIC), 437– 441, Mataram, Indonesia.
• 26. Ritter, A., Muñoz-Carpena, R. 2013. Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodnessof-fit assessments. Journal of Hydrology 480(1), 33–45. Bibcode:2 013JHyd..480...33R. https://doi.org/10.1016/j.jhydrol.2012.12.004
• 27. Saiki, S.-T., Ishida, A., Yoshimura, K., Yazaki, K. 2017. Physiological mechanisms of drought-induced tree die-off in relation to carbon, hydraulic and respiratory stress in a drought-tolerant woody plant. Sci. Rep.-UK 7, 2995. https://doi.org/10.1038/s41598-017-03162-5
• 28. Surinaidu, L., Gurunadha Rao, V.V.S., Srinivasa Rao, N., Srinu, S. 2014. Hydrogeological and groundwater modeling studies to estimate the groundwater inflows into the coal Mines at different mine development stages using MODFLOW, Andhra Pradesh, India. Water Res. Ind 7(8), 49–65. https://doi.org/10.1016/J.WRI.2014.10.002
• 29. Saft, M., Peel, M.C., Western, A.W., Perraud, J.- M., Zhang, L. 2016. Bias in streamflow projections due to climate-induced shifts in catchment response. Geophys. Res. Lett 43, 1574–1581. https://doi.org/10.1002/2015GL067326
• 30. Savenije, H.H.G. 2010. Topography driven conceptual modelling (FLEX-Topo). Hydrol. Earth Syst. Sci 14: 2681–2692. https://doi.org/10.5194/hess-14-2681-2010
• 31. Seibert, J. and van Meerveld, H.J.I. 2016. Hydrological change modeling: Challenges and opportunities, Hydrol. Process 30, 4966– 4971. https://doi.org/10.1002/hyp.10999
• 32. Savenije, H.H.G. 2010. Topography driven conceptual modelling (FLEX-Topo). Hydrol. Earth Syst. Sci 14, 2681–2692. https://doi.org/10.5194/hess-14-2681-2010
• 33. Santos, L., Thirel, G., and Perrin, C. 2018. Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0).Geosci. Model Dev 11, 1591–1605. https://doi.org/10.5194/gmd-11-1591-2018
• 34. Samadi, S.Z. 2017. Assessing the sensitivity of SWAT physical parameters to potential evapotranspiration estimation methods over a coastal plain watershed in the southeastern United States. Hydrology Research 48(2), 395–415.
• 35. Tahir, M.H., Nadarajah, S. 2015. Parameter induction in continuous univariate distributions: Wellestablished G families. An. Acad. Bras. Ciênc 87, 539–568. pmid:26131628.
• 36. Tapiador, F.J. Turk, F.J., Petersen, W., Hou, A,Y., García-Ortega, E., Machado, L.A.T., Angelis, C.F., Salio, P., Kidd C., Huffman, G.J., de Castro M. 2012. Global precipitation measurement: Methods, datasets and applications. Atmospheric Research 104–105(1), 70–97.
• 37. Tan, M.L., Duan, Z. 2017. Assessment of GPM and TRMM precipitation products over singapore. Remote Sensing 9(7), 720.
• 38. Tasir, K., Fazeela M., Mohammad A., Owais K., Muhammad A., Waheed U. 2023. Determination of suitable probability distribution of rainfall in Pakistan considering multiplicity. Trends in Ecological and Indoor Environmental Engineering 1(1), 24–34. https://doi.org/10.62622/TEIEE.023.1.1.24-34
• 39. Tian, Y., Zheng, Y., Wu, B., Wu, X., Liu, J., Zheng, C. 2015. Modeling surface watergroundwater interaction in arid and semi-arid regions with intensive agriculture. Environ. Modell. Software 63, 170–184. https://doi.org/10.1016/J.ENVSOFT.2014.10.011
• 40. Uhlenbrook, S., Mohamed, Y., Gragne, A. 2010. Analyzing catchment behavior through catchment modeling in the Gilgel Abay, upper Blue Nile River basin, Ethiopia. Hydrology and Earth System Sciences 14(10), 2153–2165.
• 41. Wagener, T., Sivapalan, M., Troch, P.A., McGlynn, B.L., Harman, C.J., Gupta, H.V., Kumar, Rao, P.S.C., Basu, N.B., and Wilson, J.S. 2010. The future of hydrology: An evolving science for a changing world, Water Resour. Res 46: W05301, https://doi.org/10.1029/2009WR008906
• 42. Orqlul, A.W. 2014. Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with groundobserved data for the Lake Tana basin in Ethiopia. Hydrology and Earth System Sciences 18(12), 4871–4881.
• 43. Deng, W., Xu, J.J., Song, Y.J., Zhao, H.M. 2020. Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem. Applied Soft Computing Journal 100, 1–16.
• 44. Deng, W., Liu, H.L., Xu, J.J., Zhao, H.M., and Song, Y.J. 2020. An improved quantum-inspired differential evolution algorithm for deep belief network, IEEE Transactions on Instrumentation and Measurement, 69(10), 7319–7327.
• 45. Fu, W., Wang, B., Li, X., Liu, L., and Wang Y. 2019. Ascent trajectory optimization for hypersonic vehicle based on improved chicken swarm optimization.
• 46. Li, X.L., Shao, Z.J., Qian, J.X. 2002. An optimization method based on autonomous animats: fish-swarm algorithm. Systems Engineering-@ eory&Practice, 22(11): 32–38.
• 47. Li, Y.T., Wu, Y., and Qu, X.J. 2017. Chicken swarmbased method for ascent trajectory optimization of hypersonic vehicles. Journal of Aerospace Engineering 30(5), 1–12.