PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Weather drought index prediction using the support vector regression in the Ansegmir Watershed, Upper Moulouya, Morocco

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The purpose of this study is to develop mathematical models based on artificial intelligence: Models based on the support vectors regression (SVR) for drought forecast in the Ansegmir watershed (Upper Moulouya, Morocco). This study focuses on the prediction of the temporal aspect of the two drought indices (standardized precipitation index – SPI and standardized precipitation-evapotranspiration index – SPEI) using six hydro-climatic variables relating to the period 1979–2013. The model SVR3-SPI: RBF, ε = 0.004, C = 20 and γ = 1.7 for the index SPI, and the model SVR3-SPEI: RBF ε = 0.004, C = 40 and γ = 0.167 for the SPEI index are significantly better in comparison to other models SVR1, SVR2 and SVR4. The SVR model for the SPI index gave a correlation coefficient of R = 0.92, MSE = 0.17 and MAE = 0.329 for the learning phase and R = 0.90, MSE = 0.18 and MAE = 0.313 for the testing phase. As for the SPEI index, the overlay is slightly poorer only in the case of the SPI index between the observed values and the predicted ones by the SVR model. It shows a very small gap between the observed and predicted values. The correlation coefficients R = 0.88 for the learning, R = 0.86 for testing remain higher and corresponding to a quadratic error average MSE = 0.21 and MAE = 0.351 for the learning and MSE = 0.21 and MAE = 0.350 for the testing phase. The prediction of drought by SVR model remain useful and would be extremely important for drought risk management.
Wydawca
Rocznik
Tom
Strony
187--194
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
  • Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco
  • Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco
  • Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco
  • Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco
  • Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco
  • Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco
  • Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco
  • Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco
Bibliografia
  • ACHOUR K., MEDDI M., ZEROUAL A., BOUABDELLI S., MACCIONI P., MORAMARCO T. 2020. Spatio-temporal analysis and forecasting of drought in the plains of northwestern Algeria using the standardized precipitation index. Journal of Earth System Science. Vol. 129. No. 1 p. 1–2. DOI 10.1007/s12040-019-1306-3.
  • ALI Z., HUSSAIN I., FAISAL M., NAZIR H.M., HUSSAIN T., SHAD M.Y., HUSSAIN G.S. 2017. Forecasting drought using multilayer perceptron artificial neural network model. Advances in Meteorology. Vol. 2017 p. 1–9. DOI 10.1155/2017/5681308.
  • BEGUERÍA S., VICENTE SERRANO S.M., REIG F., LATORRE B. 2014. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology.Vol. 34. No. 10 p. 3001–3023. DOI 10.1002/joc.3887.
  • BELAYNEH A., ADAMOWSKI J. 2012. Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression. Applied Computational Intelligence and Soft Computing. Vol. 2012 p. 1–13. DOI 10.1155/2012/ 794061.
  • BELAYNEH A., ADAMOWSKI J., KHALIL B. 2016. Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustainable Water Resources Management. Vol. 2. No. 1 p. 87–101. DOI 10.1007/ s40899-015-0040-5.
  • BRERETON R.G., LLOYD G.R. 2010. Support vector machines for classification and regression. Analyst. Vol. 135. No. 2 p. 230– 267. DOI 10.1039/b918972f.
  • CHAHBOUNE M., CHAHLAOUI A., ZAID A. 2014. Étude de la qualité des eaux d’une retenue située sous climat aride : cas du barrage Hassan II (Province de Midelt, Maroc) [Study of the water quality of a reservoir located in an arid climate: Case of the Hassan II dam (Province of Midelt, Morocco)]. Afrique Science: Revue Internationale des Sciences et Technologie. Vol. 10. No. 2 p. 199– 212. DOI 10.4314/afsci.v10i2.
  • CHEVALIER R.F., HOOGENBOOM G., MCCLENDON R.W., PAZ J.A. 2011. Support vector regression with reduced training sets for air temperature prediction: A comparison with artificial neural networks. Neural Computing and Applications. Vol. 20. No. 1 p. 151–159. DOI 10.1007/s00521-010-0363-y.
  • COMBE M., SIMONOT M. 1971. La haute Moulouya, le sillon d’Itzer-Enjil et le massif de Bou-Mia-Aouli [The upper Moulouya, the Itzer- Enjil furrow and the Bou-Mia-Aouli massif]. Notes et Mémoires du Service Géologique. Service géologique du Maroc, Rabat, Maroc. Vol. 231 p. 193–201.
  • DIBIKE Y.B., VELICKOV S., SOLOMATINE D., ABBOTT M.B. 2001. Model induction with support vector machines: Introduction and applications. Journal of Computing in Civil Engineering. Vol. 15. No. 3 p. 208216. DOI 10.4314/afsci.v10i2.
  • DIKSHIT A., PRADHAN B., ALAMRI A.M. 2020. Temporal hydrological drought index forecasting for New South Wales, Australia using machine learning approaches. Atmosphere. Vol. 11. No. 6 p. 585. DOI 10.3390/atmos11060585.
  • EL IBRAHIMI A., BAALI A. 2017. Application of neural modeling and the SPI index for the prediction of weather drought in the Saïss Plain (Northern Morocco). International Journal of Intelligent Engineering and Systems. Vol. 10. No. 5 p. 1–10. DOI 10.22266/ ijies2017.1031.01.
  • EMBERGER A. 1965. Introduction à l’étude des minéralisations plombifères de la Haute-Moulouya [Introduction to the study of lead mineralization in Haute-Moulouya]. Notes et Mémoires, Service Géologique, Rabat, Maroc. Vol. 181 p. 167–171.
  • GHUMMAN A.R., AHMAD S., HASHMI H.N. 2018. Performance assessment of artificial neural networks and support vector regression models for stream flow predictions. Environmental monitoring and assessment. Vol. 190. No. 12 p. 1–20. DOI 10.1007/s10661- 018-7012-9.
  • HARGREAVES G.H. 1994. Defining and using reference evapotranspiration. Journal of Irrigation and Drainage Engineering. Vol. 120. No. 6 p. 1132–1139. DOI 10.1061/(ASCE)0733-9437(1994)120:6 (1132).
  • KISI O., CIMEN M. 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology. Vol. 399. No. 1–2 p. 132–140. DOI 10.1016/j.jhydrol.2010.12.041.
  • LIMA A.R., CANNON A.J., HSIEH W.W. 2013. Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy. Computers and Geosciences. Vol. 50 p. 136–144. DOI 10.1016/j.cageo.2012.06.023.
  • LIONG S.Y., SIVAPRAGASAM C. 2002. Flood stage forecasting with support vector machines. Journal of the American Water Resources Association. Vol. 38. No. 1 p. 173–186. DOI 10.1111/j.1752-1688 .2002.tb01544.x.
  • MAVROMATIS T. 2007. Drought index evaluation for assessing future wheat production in Greece. International Journal of Climatol-ogy: A Journal of the Royal Meteorological Society. Vol. 27. No. 7 p. 911–924. DOI 10.1002/joc.1444.
  • MCKEE T., DOESKEN N., KLEIST J. 1993. The relationship of drought frequency and duration to time scale. 17–22.01.1993 Anaheim, Californie. Actes de la 8th Conference on Applied Climatology p. 179–184.
  • NALBANTIS I., TSAKIRIS G. 2009. Assessment of hydrological drought revisited. Water Resources Management. Vol. 23 p. 881–897. DOI 10.1007/s11269-008-9305-1.
  • PALMER W.C. 1965. Meteorological drought. Research Paper. No. 45. Washington, D.C. U.S. Department of Commerce Weather Bureau pp. 58.
  • PARRELLA F. 2007. Online support vector regression. MSc.Thesis. Genoa. University of Genoa pp. 93.
  • PENMAN H.L. 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences. Vol. 193 p. 120–145. DOI 10.1098/rspa.1948.0037.
  • PEREIRA L.S., CORDERY I., IACOVIDES I. 2009. Coping with water scarcity: Addressing the challenges. Springer Science. ISBN 978-90-481- 8161-2 pp. 382.
  • SHAFER B.A., DEZMAN L.E. 1982. Development of a surface water supply index (SWSI) to assess the severity of drought condition in snowpack runoff areas. In: Proceeding of the western snow conference. Fort Collins, Colorado. Colorado State University p. 164–175.
  • SHARMA T.C., PANU U.S. 2010. Analytical procedures for weekly hydrological droughts: A case of Canadian rivers. Hydrological Sciences Journal. Vol. 55. No. 1 p. 79–92. DOI 10.1080/ 02626660903526318.
  • SMOLA A.J., SCHÖLKOPF B. 2004. A tutorial on support vector regression. Statistics and Computing. Vol. 14. No. 3 p. 199–222. DOI 10.1023/B:STCO.0000035301.49549.88.
  • THORNTHWAITE C.W. 1948. An approach toward a rational classification of climate. Geographical Review. Vol. 38. No. 1 p. 55–94. DOI 10.2307/210739.
  • TIAN Y., XU Y.P., WANG G. 2018. Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin. Science of the Total Environment. Vol. 622 p. 710–720. DOI 10.1016/j.scitotenv.2017.12.025.
  • VAPNIK N.V. 1995. The nature of statistical learning theory. New York, NY, USA. Springer-Verlag Inc. ISBN 978-1-4757-2442-4 pp. 188. DOI 10.1007/978-1-4757-2440-0.
  • VAPNIK N.V. 1999. An overview of statistical learning theory. IEEE Transactions on Neural Networks. Vol. 10. No. 5 p. 988–999. DOI 10.1109/72.788640.
  • VICENTE-SERRANO S.M., BEGUERÍA S., LÓPEZ-MORENO J.I. 2010. A multi-scalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index–SPEI. Journal of Climate. Vol. 23. No. 7 p. 1696–1718. DOI 10.1175/ 2009JCLI2909.1.
  • WOLI P., JONES J.W., INGRAM K.T., FRAISSE C.W. 2012. Agricultural reference index for drought. Agronomy Journal. Vol. 104. No. 2 p. 287–300. DOI 10.2134/agronj2011.0286.
  • ZAHRAIE B., NASSERI M. 2011. Basin scale meteorological drought forecasting using support vector machine (SVM). In: International conference on drought management strategies in arid and semi arid regions. Muscat, Oman p. 1–16.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c0f8b50b-0836-4222-802c-cfc41d7c7518
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.