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The main objective of this work is to select the most reliable machine learning model to predict the generated solid flow in the Tafna basin (North-West of Algeria). It is about the artificial neural networks (ANN) and long short-term memory (LSTM). The sediment load is recorded through three hydrometric stations. The efficiency and performance of the two models is verified using the correlation coefficient (R2), the Nash-Sutcliffe coefficient (NSC) and the root mean square error (RMSE). The obtained simulated solids load shows a very good correlation in terms of precision although the ANN model gave relatively better results compared to the LSTM model where low RMSE values were recorded, which confirms that the artificial intelligence models remain also effective for the treatment and the prediction of hydrological phenomena such as the estimation of the solid load in a such watershed.
Wydawca
Rocznik
Tom
Strony
275--286
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
- Civil Engineering and Environment Laboratory, Djillali Liabes University of Sidi Bel Abbes, Bp 89 Sidi Bel Abbes 22000, Algeria
autor
- Civil Engineering and Environment Laboratory, Djillali Liabes University of Sidi Bel Abbes, Bp 89 Sidi Bel Abbes 22000, Algeria
autor
- Laboratory of Ecology and Environment, University of Larbi Ben M’hidi, University of Larbi Ben M’hidi, Oum El Bouaghi 04000, Algeria
Bibliografia
- 1. Adib A., Mahmoodi, A. 2017. Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions. KSCE Journal of Civil Engineering, 21(1), 447–457. https://doi.org/10.1007/s12205-016-0444-2
- 2. Beddal D., Achite M., Baahmed D. 2020. Streamflow prediction using data-driven models: Case study of Wadi Hounet, northwestern Algeria. Journal of Water and Land Development, 47(1), 16–24. https://doi.org/10.24425/jwld.2020.135027
- 3. Bengio Y., Simard P., Frasconi P. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw., 5(2), 157–166. https://doi.org/10.1109/72.279181
- 4. De Vente J., Poesen J. 2005. Predicting soil erosion and sediment yield at the basin scale: Scale issues and semi-quantitative models. Earth-Science Reviews, 71(1–2), 95–125. https://doi.org/10.1016/j. earscirev.2005.02.002
- 5. Fan J., Liu X., Li W. 2023. Daily suspended sediment concentration forecast in the upper reach of Yellow River using a comprehensive integrated deep learning model. Journal of Hydrology, 623. https://doi.org/10.1016/j.jhydrol.2023.129732
- 6. Fang L.Z., Shao D.G. 2022. Application of Long Short-Term Memory (LSTM) on the Prediction of Rainfall-Runoff in Karst Area. Front. Phys., 9. https://doi.org/10.3389/fphy.2021.790687
- 7. Gers F.A., Schmidhuber J., Cummins F. 1999. Learning to forget: continual prediction with LSTM, 1999. Proceedings of the Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), vol. 2, pp. 850-855. https://doi.org/10.1049/cp:19991218
- 8. Gwapedza D., Nyamela N., Hughes D.A., Slaughter A.R., Mantel S.K., van der Waal B. 2021. Prediction of sediment yield of the Inxu River catchment (South Africa) using the MUSLE. International Soil and Water Conservation Research, 9(1), 37–48. https://doi.org/10.1016/J.ISWCR.2020.10.003
- 9. Hu C., Wu Q., Li H., Jian S., Li N., Lou Z. 2018. Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation. Water2018,10, 1543. https://doi.org/10.3390/w10111543
- 10. Kaveh K., Kaveh H., Bui M.D., Rutschmann P. 2021. Long short-term memory for predicting daily suspended sediment concentration. Eng Comput, 37(3), 2013–2027. https://doi.org/10.1007/s00366-019-00921-y
- 11. Kumar A.R.S., Ojha C.S.P., Goyal, M.K., Singh R.D., Swamee P.K. 2012. Modeling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic, and decision tree algorithms. Journal of Hydrologic Engineering, 17(3), 394e404. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000445
- 12. Latif S.D., Chong K.L., Ahmed A.N., Huang Y.F., Sherif M., El-Shafie A. 2023. Sediment load prediction in Johor river: deep learning versus machine learning models. Applied Water Science, 13(3). https://doi.org/10.1007/s13201-023-01874-w
- 13. Meddi M.M., Assani A.A., Meddi H. 2010. Temporal Variability of Annual Rainfall in the Macta and Tafna Catchments, Northwestern Algeria. Water Resources Management, 24(14), 3817–3833. https://doi.org/10.1007/s11269-010-9635-7
- 14. Nadia T., Boulemtafes B. 2018. Extreme rainfall and flooding in the watershed of the middle Sebaou. In Rev. Sci. Technol., Synthesis, 36. [ in French]
- 15. Pham B.T., Shirzadi A., Bui D.T., Prakash I., Dholakia M.B. 2017. A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: A case study in the himalayan area, India. International Journal of Sediment Research, 33(2), 157e170. https://doi.org/10.1016/j.ijsrc.2017.09.008
- 16. Rahman K.U., Pham Q.B., Jadoon K.Z., Shahid M., Kushwaha D.P., Duan Z., Mohammadi B., Khedher K.M., Anh D.T. 2022. Comparison of machine learning and process-based SWAT model in simulating streamfow in the Upper Indus Basin. Appl Water Sci., 12(8), 1–19. https://doi.org/10.1007/s13201-022-01692-6
- 17. Semari K., Korichi K. 2023. Water erosion mapping using several erosivity factors in the Macta Basin (North-West of Algeria). In Acta Hydrologica Slovaca, 24(1), 101-112. 2644-4690. https://doi.org/10.31577/ahs-2023-0024.01.0012
- 18. Shadkani S., Abbaspour A., Samadianfard S., Hashemi S., Mosavi A., Band S.S. 2021. Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: The case study of the Mississippi River, U.S. International Journal of Sediment Research, 36(4), 512–523. https://doi.org/10.1016/j.ijsrc.2020.10.001
- 19. Sirabahenda Z., St-Hilaire A., Courtenay S.C., van den Heuvel M.R., 2020. Assessment of the effective width of riparian buffer strips to reduce suspended sediment in an agricultural landscape using ANFIS and SWAT models. Catena 195, 104762. https://doi.org/10.1016/j.catena.2020.104762
- 20. Tabatabaei M., Salehpour Jam A., Hosseini S.A. 2019. Suspended sediment load prediction using non-dominated sorting genetic algorithm II. International Soil and Water Conservation Research, 7(2), 119–129. https://doi.org/10.1016/J.ISWCR.2019.01.004
- 21. Valentine A., Kalnins L. 2016. An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics, Earth Surf. Dynam., 4, 445–460, https://doi.org/10.5194/esurf-4-445-2016
- 22. Zeyneb T., Nadir M., Boualem, R. 2022. Modeling of suspended sediment concentrations by artificial neural network and adaptive neuro fuzzy interference system method–study of five largest basins in Eastern Algeria. Water Practice and Technology, 17(5), 1058–1081. https://doi.org/10.2166/wpt.2022.050
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-29b95720-825d-4971-8c9e-7d8856a10ef6