Missing Precipitation Data Estimation Using Long Short-Term Memory Deep Neural Networks

• Autorzy: Djerbouai Salim

Identyfikatory

DOI

10.12911/22998993/147322

• Języki publikacji: EN

Abstrakty

EN

Due to the spatiotemporal variability of precipitation and the complexity of physical processes involved, missing precipitation data estimation remains as a significant problem. Algeria, like other countries in the world, is affected by this problem. In the present paper, Long Short-Term Memory (LSTM) deep neural Networks model was tested to estimate missing monthly precipitation data. The application was presented for the K'sob basin, Algeria. In the present paper, the optimal architecture of LSTM model was adjusted by trial-and-error-procedure. The LSTM model was compared with the most widely used classical methods including inverse distance weighting method (IDWM) and the coefficient of correlation weighting method (CCWM). Finally, it was concluded that the LSTM model performed better than the other methods.

Słowa kluczowe

EN

Hodna; K'sob basin; missing precipitation data; long short-term memory; CCWM; coefficient of correlation weighting method; IDWM; inverse distance weighting method

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2022
• Tom: Vol. 23, nr 5
• Strony: 216--225
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Djerbouai Salim

• CEHSD Laboratory, Hydraulics Department, University of M'sila, Ichebila, P.O. Box 166, 28000 M'sila, Algeria

• https://orcid.org/0000-0002-5451-4993

Bibliografia

• 1. Aguilera H., Guardiola-Albert, C., Serrano-Hidalgo C. 2020. Estimating extremely large amounts of missing precipitation data. Journal of Hydroinformatics, 22(3), 578–592.
• 2. Bárdossy A., Pegram G. 2014. Infilling missing precipitation records – A comparison of a new copula-based method with other techniques. Journal of Hydrology, 519, 1162–1170.
• 3. Barrios A., Trincado G., Garreaud R.2018. Alternative approaches for estimating missing climate data: Application to monthly precipitation records in South-Central Chile. Forest Ecosystems, 5(1), 28.
• 4. Coulibaly P., Evora N. D. 2007. Comparison of neural network methods for infilling missing daily weather records. Journal of Hydrology, 341(1–2), 27–41.
• 5. Garcia M., Peters-Lidard C. D., Goodrich D. C. 2008. Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States: MONSOON RAINFALL INTERPOLATION. Water Resources Research, 44(5).
• 6. Hasanpour Kashani M., Dinpashoh, Y. 2012. Evaluation of efficiency of different estimation methods for missing climatological data. Stochastic Environmental Research and Risk Assessment, 26(1), 59–71.
• 7. Hochreiter S., Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9(8), 1735–1780.
• 8. Hurtado S.I., Zaninelli P.G., Agosta E.A., Ricetti L. 2021. Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina. Atmospheric Research, 254, 105482.
• 9. Kajornrit J., Wong K. W., Fung C. C. 2012. Estimation of missing precipitation records using modular artificial neural networks. International Conference on Neural Information Processing, 52–59.
• 10. Kim J.-W., Pachepsky Y. A. 2010. Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. Journal of Hydrology, 394(3–4), 305–314.
• 11. Legates D.R., McCabe J.G.J. 1999. Evaluating the use of "goodness‐of‐fit" measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233–241.
• 12. Londhe S., Dixit P., Shah S., Narkhede, S. 2015. Infilling of missing daily rainfall records using artificial neural network. ISH Journal of Hydraulic Engineering, 21(3), 255–264.
• 13. Ly S., Charles C., Degré A. 2011. Geostatistical interpolation of daily rainfall at catchment scale: The use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrology and Earth System Sciences, 15(7), 2259–2274.
• 14. Mital U., Dwivedi D., Brown J. B., Faybishenko B., Painter S. L., Steefel C. I. 2020. Sequential Imputation of Missing Spatio-Temporal Precipitation Data Using Random Forests. Frontiers in Water, 2, 20.
• 15. Nash J.E., Sutcliffe J.V. 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282–290.
• 16. Paulhus J.L.H., Kohler M.A. 1952. Interpolation OF Missing Precipitation Records. Monthly Weather Review, 80(8), 129–133.
• 17. Rahimzad M., Moghaddam Nia A., Zolfonoon H., SoltaniJ., Danandeh Mehr A., Kwon H.-H. 2021a. Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting. Water Resources Management, 35(12), 4167–4187.
• 18. Shepard D. 1968. A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 1968 23rd ACM National Conference, 517–524.
• 19. Simanton J.R., Osborn H.B. 1980. Reciprocaldistance estimate of point rainfall. Journal of the Hydraulics Division, 106(7), 1242–1246.
• 20. Suhaila J., Sayang M.D., Jemain A.A. 2008. Revised spatial weighting methods for estimation of missing rainfall data. Asia-Pacific Journal of Atmospheric Sciences, 44(2), 93–104.
• 21. Teegavarapu R.S. 2007. Use of universal function approximation in variance-dependent surface interpolation method: An application in hydrology. Journal of Hydrology, 332(1–2), 16–29.
• 22. Teegavarapu R.S. 2012. Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records. Hydrological Sciences Journal, 57(3), 383–406.
• 23. Teegavarapu R.S., Aly A., Pathak C.S., Ahlquist J., Fuelberg H., Hood J. 2018. Infilling missing precipitation records using variants of spatial interpolation and data‐driven methods: Use of optimal weighting parameters and nearest neighbour‐based corrections. International Journal of Climatology, 38(2), 776–793.
• 24. Teegavarapu R.S. Tufail M., Ormsbee L. 2009. Optimal functional forms for estimation of missing precipitation data. Journal of Hydrology, 374(1–2), 106–115.
• 25. Teegavarapu R.S.V. 2007. Use of universal function approximation in variance-dependent surface interpolation method: An application in hydrology. Journal of Hydrology, 332(1–2), 16–29.
• 26. Teegavarapu R.S.V. 2014. Missing precipitation data estimation using optimal proximity metricbased imputation, nearest-neighbour classification and cluster-based interpolation methods. Hydrological Sciences Journal, 59(11), 2009–2026.
• 27. Teegavarapu R.S.V. 2020. Precipitation imputation with probability space-based weighting methods. Journal of Hydrology, 581, 124447.
• 28. Teegavarapu R.S.V., Chandramouli V. 2005. Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. Journal of Hydrology, 312(1–4), 191–206.
• 29. Vieux B.E. 2001. Distributed hydrologic modeling using GIS. In Distributed hydrologic modeling using GIS. Springer, 1–17.
• 30. Wei T.C. 1973. Reciprocal Distance Squared Method, A computer technique for estimating areal precipitation (Vol. 8). US Department of Agriculture, Agricultural Research Service, North Central.
• 31. Xu W., Zou Y., Zhang, G., Linderman M. 2015. A comparison among spatial interpolation techniques for daily rainfall data in Sichuan Province, China. International Journal of Climatology, 35(10), 2898–2907.