Stochastic Linear Modeling for the Forecast of Flows in Basin, Western Region of Cuba

Anaily Rivero-Villaverde, Gustavo Reinel Alonso-Brito, Andrés Lau-Quan

Abstract

This study focuses on the subwatershed "V Aniversario", belonging to Cuyaguateje River Basin, Pinar del Río Province. Its latitudinal position leads to the development of a greater volume of runoff, higher than other karstic regions in the country, then numerous damages related mainly with floods are done. Therefore, the objective of this work is to forecast the runoff in advance at the subwatershed, on annual and monthly scales, through linear stochastic modeling. In order to comply with the above, White Noise, AR (p), MA (q) and ARMA (p, q) models for the annual runoff series were implemented in R software, being the White Noise model the best adjusted to it. However, the monthly runoff series must be modeled by SARIMA, because it presented a seasonal behavior. It is interesting to note that the latter had a better memory and linear correlation compared to the annual series.

Keywords

time series; White Noise; AR (p); MA (q); autocorrelation

References

ALONSO, B.G.: Modeling discharge and sediment yield for extreme events in Western Cuba, ETH, Zurich, Master of Advanced Studies in Sustainable Water Resources (MAS-SWR), Switzerland, 2015.

ALONSO, B.G.R.: Estimación del riesgo de erosión hídrica en la subcuenca V Aniversario del río Cuyaguateje, Universidad Agraria de La Habana, Tesis (en opción al grado científico de Master en Ciencias), San José de las Lajas, Mayabeque, Cuba, 102 p., 2008.

ALONSO, B.G.R.: “Predicción probabilística del escurrimiento superficial y la pérdida de sedimento para eventos extremos. Parte II”, Revista Ciencias Técnicas Agropecuarias, 25(4): 4–16, 2016, ISSN: 1010-2760, E-ISSN: 2071-0054, DOI: http://dx.doi.org/10.13140/RG.2.2.26734.61764.

AVILES, A.; SOLERA, A.; PAREDES, J.: “Análisis del rendimiento de sistemas hídricos en desarrollo mediante el acople de modelos estocásticos hidrológicos y optimización de redes de flujo”, Ingenius, (15): 48–57, 2016, ISSN: 1390-650X, DOI: http://dx.doi.org/10.17163/ings.n15.2016.05.

CONSEJO TERRITORIAL DE CUENCAS HIDROGRÁFICAS (CTCH) DE PINAR DEL RÍO: Catálogo de Cuencas Hidrográficas Río Cuyaguateje, Pinar del Río, Cuba, 2000.

CRYER, J.D.; CHAN, K.S.: Time series analysis with applications in R, Ed. Springer, 2nd. ed., USA, 505 p., 2010, ISBN: 978-0-387-75958-6.

D’AMICO, G.; PETRONI, F.; PRATTICO, F.: “Economic performance indicators of wind energy based on wind speed stochastic modeling”, Applied Energy, 154: 290–297, 2015, ISSN: 0306-2619, DOI: http://dx.doi.org/10.1016/j.apenergy.2015.04.124.

DÍAZ, M.A.; GUEVARA, E.: “Modelación estocástica de los caudales medios anuales en la cuenca del rio Santa, Perú”, Revista Ingeniería UC, 23(2), 2016, ISSN: 1316-6832.

ESTRADA, V.; PACHECO, M.R.: “Modelación hidrológica con HEC-HMS en cuencas montañosas de la región oriental de Cuba”, Ingeniería Hidráulica y Ambiental, 33(1): 94-105, 2012, ISSN: 1815–591X.

FRY, L.M.; HUNTER, T.S.; PHANIKUMAR, M.S.; FORTIN, V.; GRONEWOLD, A.D.: “Identifying streamgage networks for maximizing the effectiveness of regional water balance modeling”, Water Resources Research, 49(5): 2689–2700, 2013, ISSN: 0043-1397, DOI: http://dx.doi.org/10.1002/wrcr.20233.

LIANG, H.; ZHUANG, W.: “Stochastic modeling and optimization in a microgrid: A survey”, Energies, 7(4): 2027–2050, 2014, ISSN: 0360-5442, DOI: http://dx.doi.org/10.3390/en7042027.

METCALFE, V.A.; COWPERTWAIT, P.S.: Introductory time series with R, [en línea], Ed. Springer, 1st. ed., 259 p., 2009, ISBN: 978-0-387-88697-8, Disponible en: 10.1007/978-0-387-88698-5.

NIEZGODA, S.R.; KANJARLA, A.K.; BEYERLEIN, I.J.; TOMÉ, C.N.: “Stochastic modeling of twin nucleation in polycrystals: an application in hexagonal close-packed metals”, International journal of plasticity, 56: 119–138, 2014, ISSN: 0749-6419, DOI: http://dx.doi.org/10.1016/j.ijplas.2013.11.005.

RODRÍGUEZ, L.Y.; MARRERO DE LEÓN, N.; GIL URRUTIA, L.: “Modelo lluvia-escurrimiento para la cuenca del río Reno”, Revista Ciencias Técnicas Agropecuarias, 19(2): 31–37, 2010, ISSN: 1010-2760, E-ISSN: 2071-0054.

RODRÍGUEZ, L.Y.; MARRERO, N.: “Simulación hidrológica en dos subcuencas de la cuenca del río Zaza de Cuba”, Ingeniería Hidráulica y Ambiental, 36(2): 109–123, 2015, ISSN: 1680-0338.

SANG, Y.-F.: “A review on the applications of wavelet transform in hydrology time series analysis”, Atmospheric research, 122: 8–15, 2013, ISSN: 0169-8095, DOI: http://dx.doi.org/10.1016/j.atmosres.2012.11.003.

SHUMWAY, R.H.; STOFFER, D.S.: Time series analysis and its applications with R examples, [en línea], Ed. Springer, 3rd. ed., USA, 604 p., 2011, ISBN: 978-1-4419-7864-6, Disponible en: DOI-http://dx.doi.org/10.1007/978-1-4419-7865-3.

SUN, K.; YAN, D.; HONG, T.; GUO, S.: “Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration”, Building and Environment, 79: 1–12, 2014, ISSN: 0360-1323, DOI: http://dx.doi.org/10.1016/j.buildenv.2014.04.030.

TRIVIÑO, A.; ORTIZ, S.: “Metodología para la modelación distribuida de la escorrentía superficial y la delimitación de zonas inundables en ramblas y ríos-rambla mediterráneos”, Investigaciones Geográficas (Esp), (35), 2004, ISSN: 0213-4691.

VALIPOUR, M.; BANIHABIB, M.E.; BEHBAHANI, S.M.R.: “Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir”, Journal of Hydrology, 473(7), 2013, ISSN: 0022-1694, DOI: http://dx.doi.org/10.1016/j.jhydrol.2012.11.017.

WILKS, D.S.: Statistical methods in the atmospheric sciences, Ed. Elsevier, 3rd. ed., vol. 100, 676 p., 2011, ISBN: 978-0-12-385022-5.

WU, C.L.; CHAU, K.-W.: “Prediction of rainfall time series using modular soft computingmethods”, Engineering applications of artificial intelligence, 26(3): 997–1007, 2013, ISSN: 0952-1976, DOI: http://dx.doi.org/10.1016/j.engappai.2012.05.023.

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