Rainfall-runoff modeling using artificial neural networks in the Mono River basin (Benin, West Africa)
Keywords:
Mono river basin, variability, modeling, artificial neural networks, Levenberg-Marquardt algorithm, non-linearity
Abstract
Hydrological models are developed to simulate river flows over a watershed for many practical applications in the field of water resource management. However, the rainfall-runoff models mostly used in the Mono river basin struggle to better simulate high river flows, especially after the installation of the Nangbéto dam. This paper presents a modeling approach based on Artificial Neural Networks (ANN) under different input meteorological parameters in the Mono River basin to better take into account the non-linearity of the relationship between rainfall and runoff. To this end, precipitation, potential evapotranspiration, and previously observed flow have been used for the daily flow simulation. The Levenberg-Marquardt algorithm is used to train the ANN rainfall-runoff models over the other optimization training algorithms mostly implemented in the study area. The analysis of the rainfall-runoff variability allowed us to show the strong correlation between rainfall and runoff and the impact of the Nangbéto dam on the flows at Athiémé. The results obtained after the training, validation, and testing of the ANN models are very good (e.g., the coefficient of correlation varies between 0.93 and 0.99). The most efficient model has been identified and implemented in the Mono river basin at Nangbéto. The satisfactory results obtained show that ANN models can be considered good alternatives for traditional rainfall-runoff modeling approaches.
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References
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34. Wilby, R.L., Abrahart, R.J., Dawson, C.W. 2003. Detection of conceptual model rainfall- runoff processes inside an artificial neural network. Hydrological Sciences Journal, 48(2), 163- 181.
35. Yao, K., Kouassi A., Amani K., Ouattara K., Loukou K., Jean, B. 2014. Application des réseaux de neurones formels pour la prévision des débits mensuels du Bandama blanc à la station de Tortiya (Nord de la Côte d’Ivoire), Afrique SCIENCE, 10(3), 134 - 145.
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2. Amoussou E. 2010. Variabilité pluviométrique et dynamique hydro-sédimentaire du bassin versant du complexe fluvio-lagunaire Mono-Ahémé-Couffo (Afrique de l’Ouest). Thèse de Doctorat Unique, Université de Bourgogne, Dijon, France, CRCCNRS UMR5 210p.
3. Amoussou, E., Tramblay, Y., Totin, H.S.V., Mahé, G., Camberlin, P. 2014. Dynamique et modélisation des crues dans le bassin du Mono à Nangbéto (Togo/Bénin), Hydrological Sciences Journal., 59, 2060–2071, https://doi.org/10.1080/02626667.2013.87101.
4. Amoussou, E., Awoye, H., Henri S., Vodounon, T., Obahoundje, S., Camberlin, P., Diedhiou, A., Kouadio, K., Mahé, G., Houndénou, C., Boko, M. 2020. Climate and Extreme Rainfall Events in the Mono River Basin (West Africa) Investigating Future Changes with Regional Climate Models, Water, 12: 833. https://doi.org/10.3390/w12030833.
5. Aoulmi, Y., Marouf, N., Amireche, M. 2020. The assessment of artificial neural network rainfall-runoff models under different input meteorological parameters Case study : Seybouse basin, Northeast Algeria, Journal of Water and Land Development, 50, 38 – 47, DOI : 10.24425/jwld.2021.138158.
6. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Artificial neural networks in hydrology. 2000a. I: preliminary concepts. Journal of Hydrologic Engineering, 5(2),115-123.
7. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Artificial neural networks in hydrology. 2000b. II: hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124-137.
8. Biao, I.E., Obada, E., Alamou, A.E., Zandagba, E.J., Chabi, A., Amoussou, E., Adounkpe, J., Afouda, A. 2021. Hydrological modeling of the Mono River basin at Athieme, Proc. IAHS, 98, 1–6, https://doi.org/10.5194/piahs-98-1-2021.
9. Boughton, W. 2004. The Australian water balance model. Environmental Modelling & Software, 19(10), 943-956. doi:https://doi.org/10.1016/j.envsoft.2003.10.007.
10. Chergui, A. 2019. Modélisation pluie-débit par les réseaux de neurones du bassin versant de SYBOUSSE. Mémoire de fin d’étude pour l’obtention du diplôme Master en hydraulique urbaine, Université Larbi Ben M'hidi O.E.B, Algérie, 113p.
11. Dawson, C.W., Wilby, R.L. 1998. An artificial neural network approach to rainfall-runoff modeling. Hydrological Sciences Journal, 43(1), 47-65.
12. Dawson, C.W., Wilby, R.L. 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography, 25(1), 80-108.
13. Giustolisi, O., Laucelli, D. 2005. Improving generalization of artificial neural networks in rainfall-runoff modelling. Hydrological Sciences Journal, 50(3), 439-457.
14. Hsu, K.L, Gupta, H.V., Sorooshian, S. 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31(10), 2517-2530.
15. Jain, A., Srinivasulu, S. 2006. Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. Journal of Hydrology, 317, 291-306.
16. Jakeman, A.J., Hornberger, G. M. 1993. How much complexity is warranted in a rainfall-runoff model?. Water Resources Research. 29, 2637–2649.
17. Joshi J., Patel V.M. 2011. Rainfall-Runoff Modeling Using Artificial Neural Network (A Literature Review), In Proceedings of the National Conference on Recent Trends in Engineering & Technology, Anand, Gujarat, India, 13–14 May.
18. Kalteh, A.M. 2008. Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding, Caspian Journal of Environmental Sciences, Vol. 6 No.1 pp.53~58.
19. Kharroubi, O., Blanpain, O., Masson, E., Lallahem, S. 2016. Application du réseau des neurones artificiels à la prévision des débits horaires : Cas du bassin versant de l’Eure, France, Hydrological Sciences Journal, 61:3, 541-550, DOI:10.1080/02626667.2014.933225.
20. Koubodana, H.D., Atchonouglo, K., Adounkpe, G.J., Amoussou, E., Kodja, D.J., Koungbanane, D., Afoudji, K.Y., Lombo, Y., Kpemoua, K.E. 2021. Surface runoff prediction and comparison using IHACRES and GR4J lumped models in the Mono catchment, West Africa. 4th Edition of the FRIEND WATER AOC International Conference on the Hydrology of the Great Rivers of Africa, Cotonou, 16 – 18.
21. Kumar, P.S., Praveen, T.V., Prasad, M.A. 2016. Artificial Neural Network Model for Rainfall-Runoff -A Case Study, International Journal of Hybrid Information Technology, Vol.9, No.3, pp. 263-272, http://dx.doi.org/10.14257/ijhit.2016.9.3.24.
22. Lawin, A. E., Hounguè, N. R., Biaou, C. A., Badou, D. F. 2019. Statistical Analysis of Recent and Future Rainfall and Temperature Variability in the Mono RiverWatershed (Benin, Togo), Climate, 7, 1, https://doi.org/10.3390/cli7010008.
23. Lek, S., Dimopoulos, I., Derraz, M., Yel, G. 1996. Modélisation de la relation pluie-débit à l’aide des réseaux de neurones artificiels, Revue des Sciences de l’Eau, 1996, Vol. 9, N°3, 319-331.
24. Lorrai, M., Sechi, G.M. 1995. Neural nets for modelling rainfall-runoff transformations. Water Resources Management, 9, 299-313.
25. Maier, H.R., Dandy, G.C. 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling Software, 15, 101-124.
26. Marquard D. 1963. An algorithm for the least-squares estimation of nonlinear. Journal of Applied Mathematics, 11, 431-441.
27. Minns, A.W., Hall, M.J. 1996. Artificial neural networks as rainfall runoff models.Hydrological Sciences Journal, 41(3), 399-417.
28. Mohseni, U., Muskula, S.B. 2023. Rainfall-Runoff Modeling Using Artificial Neural Network- A Case Study of Purna Sub-Catchment of Upper Tapi Basin, India, Environmental Sciences Proceedings, 25, 1, https: //doi.org/10.3390/ECWS-7-14232.
29. Perrin, C., Michel, C., Andréassian, V. (2007). Modèles hydrologiques du Génie Rural (GR) CEMAGREF Disponible. http://www.cemagref.fr/webgr.
30. Rajurkar, M.P., Kothyari, U.C., Chaube, U.C. 2002. Artificial neural networks for daily rainfall-runoff modelling. Hydrological Sciences Journal, 47(6), 865-877.
31. Riad, S., Mania, J., Bouchaou, L., Najjar, Y. 2004. Predicting Catchment Flow in Semi- arid Region via Artificial Neural Network Technique, Hydrological Process, vol.18, pp. 2387-2393.
32. Seibert J. 2005. HBV light. Version 2. User’ s Manual. Stockholm.
33. Tokar, A.S., Johnson, P.A. 1999. Rainfall-runoff modeling using artificial neural networks.Journal of Hydrologic Engineering, 4(3), 232- 239.
34. Wilby, R.L., Abrahart, R.J., Dawson, C.W. 2003. Detection of conceptual model rainfall- runoff processes inside an artificial neural network. Hydrological Sciences Journal, 48(2), 163- 181.
35. Yao, K., Kouassi A., Amani K., Ouattara K., Loukou K., Jean, B. 2014. Application des réseaux de neurones formels pour la prévision des débits mensuels du Bandama blanc à la station de Tortiya (Nord de la Côte d’Ivoire), Afrique SCIENCE, 10(3), 134 - 145.
36. Zohou, P.J, Biao, I.E., Aoga, J., Houessou, O., Alamou, A.E., Ezin, C.E. 2023. Modeling River Discharge using Deep Learning in the Ouémé catchment at Savè outlet (Benin, West Africa), SSRG International Journal of Geo-informatics and Geological Science, Volume10 Issue 1, 29-35.
Published
2024-07-18
How to Cite
Biao Iboukoun, E., Ezechiel, O., Djibril Aliou, M., Segbede, A., & Alamou Adechina, E. (2024). Rainfall-runoff modeling using artificial neural networks in the Mono River basin (Benin, West Africa). European Scientific Journal, ESJ, 31, 313. Retrieved from https://eujournal.org/index.php/esj/article/view/18344
Section
ESI Preprints
Copyright (c) 2024 Eliezer Biao Iboukoun, Obada Ezechiel, Moussa Djibril Aliou, Armand Segbede, Eric Alamou Adechina
This work is licensed under a Creative Commons Attribution 4.0 International License.
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