• Adamowski, J. F., 2008: Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J. Hydrol., 353, 247266, https://doi.org/10.1016/j.jhydrol.2008.02.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Afzali, A., and Coauthors, 2016: Spatio-temporal analysis of drought severity using drought indices and deterministic and geostatistical methods (case study: Zayandehroud River basin). Desert, 21, 165172, https://doi.org/10.22059/JDESERT.2016.60325.

    • Search Google Scholar
    • Export Citation
  • Aiguo, D., E. T. Kevin, and Q. Taotao, 2004: A global dataset of palmer drought severity index for 1870–2002 : Relationship with soil moisture and effects of surface warming. J. Hydrometeor., 5, 11171130, https://doi.org/10.1175/JHM-386.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 228232, https://doi.org/10.1038/NATURE01092.

    • Search Google Scholar
    • Export Citation
  • Asadi Zarch, M. A., B. Sivakumar, and A. Sharma, 2015: Droughts in a warming climate: A global assessment of standardized precipitation index (SPI) and reconnaissance drought index (RDI). J. Hydrol., 526, 183195, https://doi.org/10.1016/j.jhydrol.2014.09.071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asseng, S., and Coauthors, 2015: Rising temperatures reduce global wheat production. Nat. Climate Change, 5, 143147, https://doi.org/10.1038/nclimate2470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Belayneh, A., and J. Adamowski, 2012: Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression. Appl. Comput. Intell. Soft Comput., 2012, 794061, https://doi.org/10.1155/2012/794061.

    • Search Google Scholar
    • Export Citation
  • Belayneh, A., J. Adamowski, B. Khalil, and B. Ozga-Zielinski, 2014: Long-term SPI drought forecasting in the Awash River basin in Ethiopia using wavelet neural networks and wavelet support vector regression models. J. Hydrol., 508, 418429, https://doi.org/10.1016/j.jhydrol.2013.10.052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Belayneh, A., J. Adamowski, B. Khalil, and J. Quilty, 2016: Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmos. Res., 172–173, 3747, https://doi.org/10.1016/j.atmosres.2015.12.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Borji, M., A. Malekian, A. Salajegheh, and M. Ghadimi, 2016: Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN). Arab. J. Geosci., 9, 725, https://doi.org/10.1007/s12517-016-2750-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cacciamani, C., A. Morgillo, S. Marchesi, and V. Pavan, 2007: Monitoring and forecasting drought on a regional scale: Emilia-Romagna region. Methods and Tools for Drought Analysis and Management, G. Rossi, T. Vega, and B. Bonaccorso, Eds., Water Science and Technology Library, Vol. 62, Springer, 29–48, https://doi.org/10.1007/978-1-4020-5924-7_2.

    • Crossref
    • Export Citation
  • Cai, X., X. Wang, P. Jain, and M. D. Flannigan, 2019: Evaluation of gridded precipitation data and interpolation methods for forest fire danger rating in Alberta, Canada. J. Geophys. Res. Atmos., 124, 317, https://doi.org/10.1029/2018JD028754.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Che, J., and J. Wang, 2010: Short-term electricity prices forecasting based on support vector regression and auto-regressive integrated moving average modeling. Energy Convers. Manage., 51, 19111917, https://doi.org/10.1016/j.enconman.2010.02.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, H., and J. Sun, 2015: Changes in drought characteristics over China using the standardized precipitation evapotranspiration index. J. Climate, 28, 54305447, https://doi.org/10.1175/JCLI-D-14-00707.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., J. Y. Shin, and T. W. Kim, 2017: Probabilistic forecasting of drought: A hidden Markov model aggregated with the RCP 8.5 precipitation projection. Stochastic Environ. Res. Risk Assess., 31, 10611076, https://doi.org/10.1007/s00477-016-1279-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chevalier, R. F., G. Hoogenboom, R. W. McClendon, and J. A. Paz, 2011: Support vector regression with reduced training sets for air temperature prediction: A comparison with artificial neural networks. Neural Comput. Appl., 20, 151159, https://doi.org/10.1007/s00521-010-0363-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choubin, B., and A. Malekian, 2017: Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions. Environ. Earth Sci., 76, 538, https://doi.org/10.1007/s12665-017-6870-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choubin, B., A. Malekian, and M. Golshan, 2016: Application of several data-driven techniques to predict a standardized precipitation index. Atmósfera, 29, 121128, https://doi.org/10.20937/ATM.2016.29.02.02.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cortes, C., and V. Vapnik, 1995: Support-vector networks. Mach. Learn., 20, 273297, https://doi.org/10.1007/BF00994018.

  • Deo, R. C., and M. Şahin, 2015: Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia. Atmos. Res., 161–162, 6581, https://doi.org/10.1016/j.atmosres.2015.03.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deo, R. C., and M. Şahin, 2016: An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environ. Monit. Assess., 188, 124, https://doi.org/10.1007/s10661-016-5094-9.

    • Search Google Scholar
    • Export Citation
  • Durdu, Ö. F., 2010: Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey. Stochastic Environ. Res. Risk Assess., 24, 11451162, https://doi.org/10.1007/s00477-010-0366-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Esfahanian, E., A. P. Nejadhashemi, M. Abouali, U. Adhikari, Z. Zhang, F. Daneshvar, and M. R. Herman, 2017: Development and evaluation of a comprehensive drought index. J. Environ. Manage., 185, 3143, https://doi.org/10.1016/j.jenvman.2016.10.050.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fung, K. F., Y. F. Huang, and C. H. Koo, 2019: Coupling fuzzy–SVR and boosting–SVR models with wavelet decomposition for meteorological drought prediction. Environ. Earth Sci., 78, 693, https://doi.org/10.1007/s12665-019-8700-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fung, K. F., Y. F. Huang, C. H. Koo, and M. Mirzaei, 2020a: Improved SVR machine learning models for agricultural drought prediction at downstream of Langat River basin, Malaysia. J. Water Climate Change, jwc2019295, https://doi.org/10.2166/WCC.2019.295, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fung, K. F., Y. F. Huang, C. H. Koo, and Y. W. Soh, 2020b: Drought forecasting: A review of modelling approaches 2007–2017. J. Water Climate Change, jwc2019236, https://doi.org/10.2166/wcc.2019.236, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ganguli, P., and M. Janga Reddy, 2014: Ensemble prediction of regional droughts using climate inputs and the SVM-copula approach. Hydrol. Processes, 28, 49895009, https://doi.org/10.1002/hyp.9966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, P., P. X. Wang, S. Y. Zhang, and D. H. Zhu, 2010: Drought forecasting based on the remote sensing data using ARIMA models. Math. Comput. Modell., 51, 13981403, https://doi.org/10.1016/j.mcm.2009.10.031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heim, R. R., Jr., 2002: A review of twentieth-century drought indices used in the United States. Bull. Amer. Meteor. Soc., 83, 11491166, https://doi.org/10.1175/1520-0477-83.8.1149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, W., S. Tong, K. Mengersen, and D. Connell, 2007: Weather variability and the incidence of cryptosporidiosis: Comparison of time series Poisson regression and SARIMA models. Ann. Epidemiol., 17, 679688, https://doi.org/10.1016/j.annepidem.2007.03.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, Y. F., J. T. Ang, Y. J. Tiong, M. Mirzaei, and M. Z. M. Amin, 2016: Drought forecasting using SPI and EDI under RCP-8.5 climate change scenarios for Langat River basin, Malaysia. Procedia Eng., 154, 710717, https://doi.org/10.1016/j.proeng.2016.07.573.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jain, P., and M. D. Flannigan, 2017: Comparison of methods for spatial interpolation of fire weather in Alberta, Canada. Can. J. For. Res., 47, 16461658, https://doi.org/10.1139/cjfr-2017-0101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karavitis, C. A., C. Chortaria, S. Alexandris, C. G. Vasilakou, and D. E. Tsesmelis, 2012: Development of the standardised precipitation index for Greece. Urban Water J., 9, 401417, https://doi.org/10.1080/1573062X.2012.690431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karthika, M., Krishnaveni, and V. Thirunavukkarasu, 2017: Forecasting of meteorological drought using ARIMA model. Indian J. Agric. Res., 51, 103111, https://doi.org/10.18805/IJARE.V0IOF.7631.

    • Search Google Scholar
    • Export Citation
  • Khan, M. S., and P. Coulibaly, 2006: Application of support vector machine in lake water level prediction. J. Hydrol. Eng., 11, 199205, https://doi.org/10.1061/(ASCE)1084-0699(2006)11:3(199).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kisi, O., H. Sanikhani, M. Zounemat-Kermani, and F. Niazi, 2015: Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Comput. Electron. Agric., 115, 6677, https://doi.org/10.1016/j.compag.2015.04.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kousari, M. R., M. E. Hosseini, H. Ahani, and H. Hakimelahi, 2017: Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities. Theor. Appl. Climatol., 127, 361380, https://doi.org/10.1007/s00704-015-1624-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lima, A. R., A. J. Cannon, and W. W. Hsieh, 2013: Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy. Comput. Geosci., 50, 136144, https://doi.org/10.1016/j.cageo.2012.06.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., Y. Wang, M. Shao, X. Jia, and X. Li, 2016: Spatiotemporal analysis of multiscalar drought characteristics across the Loess Plateau of China. J. Hydrol., 534, 281299, https://doi.org/10.1016/j.jhydrol.2016.01.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., A. Sibley, and J. Ivan Ortiz-Monasterio, 2012: Extreme heat effects on wheat senescence in India. Nat. Climate Change, 2, 186189, https://doi.org/10.1038/nclimate1356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manatsa, D., W. Chingombe, and C. H. Matarira, 2008: The impact of the positive Indian Ocean dipole on Zimbabwe droughts. Int. J. Climatol., 2029, 20112029, https://doi.org/10.1002/joc.1695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marj, A. F., and A. M. J. Meijerink, 2011: Agricultural drought forecasting using satellite images, climate indices and artificial neural network. Int. J. Remote Sens., 32, 97079719, https://doi.org/10.1080/01431161.2011.575896.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Eighth Conf. Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179–183.

  • Mishra, A. K., and V. R. Desai, 2005: Drought forecasting using stochastic models. Stochastic Environ. Res. Risk Assess., 19, 326339, https://doi.org/10.1007/s00477-005-0238-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mishra, A. K., and V. R. Desai, 2006: Drought forecasting using feed-forward recursive neural network. Ecol. Modell., 198, 127138, https://doi.org/10.1016/j.ecolmodel.2006.04.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mishra, A. K., V. R. Desai, and V. P. Singh, 2007: Drought forecasting using a hybrid stochastic and neural network model. J. Hydrol. Eng., 12, 626638, https://doi.org/10.1061/(ASCE)1084-0699(2007)12:6(626).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mossad, A., and A. A. Alazba, 2015: Drought forecasting using stochastic models in a hyper-arid climate. Atmosphere, 6, 410430, https://doi.org/10.3390/atmos6040410.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nash, J. E., 1970: River flow forecasting through conceptual models: Part I. A discussion of principles. J. Hydrol., 10, 282290, https://doi.org/10.1016/0022-1694(70)90255-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ochoa-Rivera, J. C., 2008: Prospecting droughts with stochastic artificial neural networks. J. Hydrol., 352, 174180, https://doi.org/10.1016/j.jhydrol.2008.01.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortega-Gómez, T., M. A. Pérez-Martín, and T. Estrela, 2018: Improvement of the drought indicators system in the Júcar River basin, Spain. Sci. Total Environ., 610–611, 276290, https://doi.org/10.1016/j.scitotenv.2017.07.250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Piao, S., and Coauthors, 2010: The impacts of climate change on water resources and agriculture in China. Nature, 467, 4351, https://doi.org/10.1038/nature09364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rafiei-Sardooi, E., M. Mohseni-Saravi, S. Barkhori, A. Azareh, B. Choubin, and M. Jafari-Shalamzar, 2018: Drought modeling: A comparative study between time series and neuro-fuzzy approaches. Arab. J. Geosci., 11, 487, https://doi.org/10.1007/s12517-018-3835-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seibert, M., B. Merz, and H. Apel, 2017: Seasonal forecasting of hydrological drought in the Limpopo Basin: A comparison of statistical methods. Hydrol. Earth Syst. Sci., 21, 16111629, https://doi.org/10.5194/hess-21-1611-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, B., X. Zhu, Y. Hu, and Y. Yang, 2017: Drought characteristics of Henan Province in 1961-2013 based on standardized precipitation evapotranspiration index. J. Geogr. Sci., 27, 311325, https://doi.org/10.1007/s11442-017-1378-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shin, K. S., T. S. Lee, and H. J. Kim, 2005: An application of support vector machines in bankruptcy prediction model. Expert Syst. Appl., 28, 127135, https://doi.org/10.1016/j.eswa.2004.08.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sigaroodi, S. K., Q. Chen, S. Ebrahimi, A. Nazari, and B. Choobin, 2013: Long-term precipitation forecast for drought relief using atmospheric circulation factors: A study on the Maharloo Basin in Iran. Hydrol. Earth Syst. Sci. Discuss., 10, 13 33313 361, https://doi.org/10.5194/hessd-10-13333-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soh, Y. W., C. H. Koo, Y. F. Huang, and K. F. Fung, 2018: Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat River basin, Malaysia. Comput. Electron. Agric., 144, 164173, https://doi.org/10.1016/j.compag.2017.12.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tabari, H., O. Kisi, A. Ezani, and P. Hosseinzadeh Talaee, 2012: SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. J. Hydrol., 444–445, 7889, https://doi.org/10.1016/j.jhydrol.2012.04.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tarpley, J. D., S. R. Schneider, and R. L. Money, 1984: Global vegetation indices from the NOAA-7 meteorological satellite. J. Climate Appl. Meteor., 23, 491494, https://doi.org/10.1175/1520-0450(1984)023<0491:GVIFTN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsakiris, G., and H. Vangelis, 2004: Towards a drought watch system based on spatial SPI. Water Resour. Manage., 18, 112, https://doi.org/10.1023/B:WARM.0000015410.47014.a4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., and Coauthors, 2016: Monitoring winter wheat drought threat in Northern China using multiple climate-based drought indices and soil moisture during 2000–2013. Agric. For. Meteor., 228–229, 112, https://doi.org/10.1016/j.agrformet.2016.06.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Q., and Coauthors, 2015: The alleviating trend of drought in the Huang-Huai-Hai Plain of China based on the daily SPEI. Int. J. Climatol., 35, 37603769, https://doi.org/10.1002/joc.4244.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watanabe, K., K. Ishibashi, K. Iki, Y. Nakashima, M. Hayashida, and K. Amako, 1987: Cell surface characteristics of some phage-resistant strains of Lactobacillus casei. J. Appl. Bacteriol., 63, 197200, https://doi.org/10.1111/j.1365-2672.1987.tb04936.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yurekli, K., A. Kurunc, and F. Ozturk, 2005: Application of linear stochastic models to monthly flow data of Kelkit Stream. Ecol. Modell., 183, 6775, https://doi.org/10.1016/j.ecolmodel.2004.08.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, B., B. Long, Z. Wu, and Z. Wang, 2017: An evaluation of the performance and the contribution of different modified water demand estimates in drought modeling over water-stressed regions. Land Degrad. Dev., 28, 11341151, https://doi.org/10.1002/ldr.2655.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 163 163 114
Full Text Views 8 8 6
PDF Downloads 9 9 7

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

View More View Less
  • 1 College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, China
  • 2 College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, China
  • 3 College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, China
© Get Permissions
Restricted access

Abstract

Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

Denotes content that is immediately available upon publication as open access.

Corresponding author: Qi Zhang, 895300576@qq.com

Abstract

Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

Denotes content that is immediately available upon publication as open access.

Corresponding author: Qi Zhang, 895300576@qq.com
Save