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Artificial neural network and energy budget method to predict daily evaporation of Boudaroua reservoir (northern Morocco)

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Evaporation is one of the main essential components of the hydrologic cycle. The study of this parameter has significant consequences for knowing reservoir level forecasts and water resource management. This study aimed to test the three artificial neural networks (feed-forward, Elman and nonlinear autoregressive network with exogenous inputs (NARX) models) and multiple linear regression to predict the rate of evaporation in the Boudaroua reservoir using the calculated values obtained from the energy budget method. The various combinations of meteorological data, including solar radiation, air temperature, relative humidity, and wind speed, are used for the training and testing of the model’s studies. The architecture that was finally chosen for three types of neural networks has the 4-10-1 structure, with contents of 4 neurons in the input layer, 10 neurons in the hidden layer and 1 neuron in the output layer. The calculated evaporation rate presents a typical annual cycle, with low values in winter and high values in summer. Moreover, air temperature and solar radiation were identified as meteorological variables that mostly influenced the rate of evaporation in this reservoir, with an annual average equal to 4.67 mm∙d-1. The performance evaluation criteria, including the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) approved that all the networks studied were valid for the simulation of evaporation rate and gave better results than the multiple linear regression (MLR) models in the study area.
Wydawca
Rocznik
Tom
Strony
107--115
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
  • Ibn Tofail University, Faculty of Science, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, University campus, B.P. 242, 14000 Kenitra, Morocco
autor
  • University Hassan II, Higher Normal School of Technical Education (ENSET), Computer Science, Artificial Intelligence and Cybersecurity (IIACS), Mohammedia, Casablanca, Morocco
  • Ibn Tofail University, Faculty of Science, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, University campus, B.P. 242, 14000 Kenitra, Morocco
  • Ibn Tofail University, Faculty of Science, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, University campus, B.P. 242, 14000 Kenitra, Morocco
  • Ibn Tofail University, Faculty of Science, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, University campus, B.P. 242, 14000 Kenitra, Morocco
  • Abdelmalek Essaadi University, Faculty of Science and Technique, Department of Earth and Environmental Sciences, Team of Applied Geosciences and Geological Engineering, Al Hoceima, Morocco
Bibliografia
  • Ali, J. and Saraf, S. (2015) “Evaporation modelling by using artificial neural network and multiple linear regression technique,” International Journal of Agricultural and Food Science, 5, pp. 125–133.
  • Al-Mukhtar, M. (2021) “Modeling the monthly pan evaporation rates using artificial intelligence methods: A case study in Iraq,” Environmental Earth Sciences, 80(1). Available at: https://doi.org/10.1007/s12665-020-09337-0.
  • Ansorge, L. and Beran, A. (2019) “Performance of simple temperature-based evaporation methods compared with a time series of pan evaporation measures from a standard 20 m2 tank,” Journal of Water and Land Development, 41, pp. 1–11. Available at: https://doi.org/10.2478/jwld-2019-0021.
  • Antonopoulos, V.Z., Gianniou, S.K. and Antonopoulos, A.V. (2016) “Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece,” Hydrological Sciences Journal, 61(14), pp. 2590–2599. Available at: https://doi.org/10.1080/02626667.2016.1142667.
  • Bowie, G.L. et al. (1985) Rates, constants, and kinetics formulations in surface water quality modeling. 2nd edn. Athens, Georgia: U.S. Environmental Protection Agency.
  • Bozorgi, A. et al. (2020) “Comparison of methods to calculate evaporation from reservoirs,” International Journal of River Basin Management, 18(1), pp. 1–12. Available at: https://doi.org/10.1080/15715124.2018.1546729.
  • El Azhari, K. et al. (2022) “Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco,” Journal of Water and Land Development, 54, pp. 13–20. Available at: https://doi.org/10.24425/jwld.2022.141550.
  • El Qryefy, M. et al. (2021) “Hydrochemical characteristics and water quality assessment of Lake Dayet Erroumi – Khemisset, Morocco,” Journal of Water and Land Development, 49, pp. 179–187. Available at: https://doi.org/10.24425/jwld.2021.137110.
  • Elman, J.L. (1990) “Finding structure in time,” Cognitive Science, 14(2), pp. 179–211. Available at: https://doi.org/10.1207/s15516709cog1402_1.
  • En-nkhili, H. et al. (2020) “Application of water quality index for the assessment of Boudaroua Lake in the Moroccan Pre-Rif,” Proceedings of the 4th Edition of International Conference on Geo-IT and Water Resources 2020, Geo-IT and Water Resources 2020, 36, pp. 1–5. Available at: https://doi.org/10.1145/3399205.3399248.
  • En-nkhili, H. et al. (2021) “Water hydrochemistry of Lake Boudaroua in the Moroccan Prerifwest Mediterranean region,” E3S Web of Conferences, 234, 00078.
  • En-nkhili, H., Igouzal, M. and Etebaai, I. (2022) “Water quality assessment of an artificial small-scale reservoir in the Moroccan Pre-Rif: a case study of Boudaroua Lake using multivariate statistical techniques and self-organizing maps,” Desalination and Water Treatment, 260, pp. 279–290. Available at: https://doi.org/10.5004/dwt.2022.28533.
  • Etebaai, I., Damnati, B. and Taieb, M. (2010) “L’environnement du plan d’eau sidi Boudaroua : physico-chimie des eaux et sédimentation actuelle (Région d’Ouezzane, Maroc) [The environment of the water body sidi Boudaroua: Physico-chemistry of the water and current sedimentation (Ouezzane region, Morocco)],” Geomaghreb, 6, pp. 69–78.
  • Gianniou, S.K. and Antonopoulos, V.Z. (2007) “Evaporation and energy budget in Lake Vegoritis, Greece,” Journal of Hydrology, 345(3–4), pp. 212–223. Available at: https://doi.org/10.1016/j.jhydrol.2007.08.007.
  • Hojjati, E. et al. (2021) “Estimating evaporation from reservoirs using energy budget and empirical methods: Alavian Dam reservoir, NW Iran,” Italian Journal of Agrometeorology, 2, pp. 19–34. Available at: https://doi.org/10.13128/IJAM-1033.
  • Hunt, A. et al. (2020) “Predicting water cycle characteristics from percolation theory and observational data,” International Journal of Environmental Research and Public Health, 17(3), p. 734. Available at: https://doi.org/10.3390/ijerph17030734.
  • Hussein, M.A.M. (2017) “Evaporation and evaluation of seven estimation methods: Results from Brullus Lake, north of Nile Delta, Egypt,” Hydrology, 5(4), pp. 58–66. Available at: https://doi.org/10.11648/j.hyd.20170504.12.
  • Igouzal, M. and Maslouhi, A. (2005) “Elaboration of management tool of a reservoir dam on the Sebou river (Morocco) using an implicit hydraulic model,” Journal of Hydraulic Research, 43(2), pp. 125–130. Available at: https://doi.org/10.1080/00221686.2005.9641228.
  • Jhajharia, D. et al. (2009) “Temporal characteristics of pan evaporation trends under the humid conditions of northeast India,” Agricultural and Forest Meteorology, 149(5), pp. 763–770. Available at: https://doi.org/10.1016/j.agrformet.2008.10.024.
  • Karami, H. et al. (2021) “Investigating the performance of neural network based group method of data handling to pan’s daily evaporation estimation (Case study: Garmsar City),” Journal of Soft Computing in Civil Engineering, 5, pp. 1–18. Available at: https://doi.org/10.22115/SCCE.2021.274484.1282.
  • Kisi, O. (2015) “Pan evaporation modeling using least square suport vector machine, multivariate adaptive regression splines and M5 model tree,” Journal of Hydrology, 528, pp. 312–320. Available at: https://doi.org/10.1016/j.jhydrol.2015.06.052.
  • Majidi, M. et al. (2015) “Estimating evaporation from lakes and reservoirs under limited data condition in a semi-arid region,” Water Resources Management, 29(10), pp. 3711–3733. Available at: https://doi.org/10.1007/s11269-015-1025-8.
  • Malik, A., Kumar, A. and Kisi, O. (2017) “Monthly pan-evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models,” Computers and Electronics in Agriculture, 143, pp. 302–313. Available at: https://doi.org/10.1016/j.compag.2017.11.008.
  • Malik, A., Kumar, A. and Rai, P. (2018) “Weekly pan-evaporation simulation using MLP, CANFIS, MLR and climate-based models at Pantnagar,” Indian Journal of Ecology, 45(2), pp. 292–298.
  • National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce (2015) NCEP GDAS/FNL 0.25 degree global tropospheric analyses and forecast grids. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Available at: https://doi.org/10.5065/D65Q4T4Z. [Updated daily].
  • Patle, G.T., Chettri, M. and Jhajharia, D. (2020) “Monthly pan evaporation modelling using multiple linear regression and artificial neural network techniques,” Water Science & Technology: Water Supply, 20(3), pp. 800–808. Available at: https://doi.org/10.2166/ws.2019.189.
  • Rosenberry, D.O. et al. (2007) “Comparison of 15 evaporation methods applied to a small mountain lake in the northeastern USA,” Journal of Hydrology, 340(3–4), pp. 149–166. Available at: https://doi.org/10.1016/j.jhydrol.2007.03.018.
  • Shafaei, M. et al. (2016) “A wavelet-SARIMA-ANN hybrid model for precipitation forecasting,” Journal of Water and Land Development, 28, pp. 27–36. Available at: https://doi.org/10.1515/jwld-2016-0003.
  • Sturrock, A.M., Winter, T.C. and Rosenberry, D.O. (1992) “Energy budget evaporation from Williams Lake: A closed lake in North central Minnesota,” Water Resources Research, 28 (6), pp. 1605–1617. Available at: https://doi.org/10.1029/92WR00553.
  • Tayfur, G. and Singh, V.P.R. (2005) “Predicting longitudinal dispersion coefficient in natural streams by artificial neural network,” Journal of Hydraulic Engineering, 131, pp. 991–1000. Available at: https://doi.org/10.1061/(ASCE)0733-9429(2005)131:11(991).
  • Wang, B. et al. (2019) “Evaluation of ten methods for estimating evaporation in a small high-elevation lake on the Tibetan Plateau,” Theoretical and Applied Climatology, 136(3–4), pp. 1033–1045. Available at: https://doi.org/10.1007/s00704-018-2539-9.
  • Wang, L. et al. (2017) “Pan evaporation modeling using six different heuristic computing methods in different climates of China,” Journal of Hydrology, 544, pp. 407–427. Available at: https://doi.org/10.1016/j.jhydrol.2016.11.059.
  • Winter, T.C. et al. (2003) “Evaporation determined by the energy-budget method for Mirror Lake, New Hampshire,” Limnology and Oceanography, 48(3), pp. 995–1009. Available at: https://doi.org/10.4319/lo.2003.48.3.0995.
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-4aba6809-3f2f-4687-99b3-59c7baaad673
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