an-innovative-hybrid-w-eemd-arima-model-for-drought-forecasting-using-the-standardized-precipitation-index-…-–-springer

An innovative hybrid W-EEMD-ARIMA model for drought forecasting using the standardized precipitation index … – Springer

  • Achite M et al (2023) Performance of machine learning techniques for meteorological drought forecasting in the Wadi Mina Basin, Algeria. Water 15(4):765. https://doi.org/10.3390/w15040765

    Article  Google Scholar 

  • Adib A, Zaerpour A, Lotfirad M (2021) On the reliability of a novel MODWT-based hybrid ARIMA-artificial intelligence approach to forecast daily snow depth (case study: the western part of the rocky mountains in the U.S.A). Cold Reg Sci Technol 189:103342. https://doi.org/10.1016/j.coldregions.2021.103342

    Article  Google Scholar 

  • Adib A, Zaerpour A, Kisi O, Lotfirad M (2021) A rigorous wavelet-packet transform to retrieve snow depth from SSMIS Data and evaluation of its reliability by uncertainty parameters. Water Resour Manag 35(9):2723–2740. https://doi.org/10.1007/s11269-021-02863-x

    Article  Google Scholar 

  • Ahmadi F, Mehdizadeh S, Mohammadi B (2021) Development of bio-inspired- and wavelet-based hybrid models for reconnaissance drought index modeling. Water Resour Manag 35(12):4127–4147. https://doi.org/10.1007/s11269-021-02934-z

    Article  Google Scholar 

  • Akaike H (1969) Fitting autoregressive models for prediction. Ann Inst Stat Math 21(1):243–247. https://doi.org/10.1007/BF02532251

    Article  Google Scholar 

  • Alquraish M, Ali K, Abuhasel AS, Alqahtani S, Khadr M (2021) SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN models for meteorological drought forecasting. Sustainability 13(22):12576. https://doi.org/10.3390/su132212576

    Article  Google Scholar 

  • Babu CN, Reddy BE (2014) A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Appl Soft Comput 23:27–38. https://doi.org/10.1016/j.asoc.2014.05.028

    Article  Google Scholar 

  • Barzkar A, Najafzadeh M, Homaei F (2022) Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model. Nat Hazards 110(3):1931–1952. https://doi.org/10.1007/s11069-021-05019-7

    Article  Google Scholar 

  • Belayneh A, Adamowski J (2013) Drought forecasting using new machine learning methods. J Water l Dev 18(9):3–12. https://doi.org/10.2478/jwld-2013-0001

    Article  Google Scholar 

  • Borji M, Malekian A, Salajegheh A, Ghadimi M (2016) Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN). Arab J Geosci 9(19):725. https://doi.org/10.1007/s12517-016-2750-x

    Article  Google Scholar 

  • Box GE, Jenkins G (1976) Time Series analysis: forecasting and control. Holden-Day, San Francisco

    Google Scholar 

  • Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting and control, 3rd ed, New Jersey

  • Cacciamani C, Morgillo A, Marchesi S, Pavan V (2007) Monitoring and forecasting drought on a regional scale: Emilia-Romagna region In: Methods and tools for drought analysis and management. Springer, Netherlands, pp. 29–48. https://doi.org/10.1007/978-1-4020-5924-7_2

  • Cancelliere A, Di Mauro G, Bonaccorso B, Rossi G (2007) Drought forecasting using the standardized precipitation index. Water Resour Manag 21(5):801–819. https://doi.org/10.1007/s11269-006-9062-y

    Article  Google Scholar 

  • Chan W-S (1999) A comparison of some of pattern identification methods for order determination of mixed ARMA models. Stat Probab Lett 42(1):69–79. https://doi.org/10.1016/S0167-7152(98)00195-3

    Article  Google Scholar 

  • Che J, Zhai H (2022) WT-ARIMA combination modelling for short-term load forecasting. IAENG Int J Comput Sci 49(2):542–548

    Google Scholar 

  • Conejo AJ, Plazas MA, Espinola R, Molina AB (2005) Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans Power Syst 20(2):1035–1042. https://doi.org/10.1109/TPWRS.2005.846054

    Article  Google Scholar 

  • Das P, Naganna SR, Deka PC, Pushparaj J (2020) Hybrid wavelet packet machine learning approaches for drought modeling. Environ Earth Sci 79(10):221. https://doi.org/10.1007/s12665-020-08971-y

    Article  Google Scholar 

  • Dawson CW, Abrahart RJ, See LM (2007) HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ Model Softw 22(7):1034–1052. https://doi.org/10.1016/j.envsoft.2006.06.008

    Article  Google Scholar 

  • Debert S, Pachebat M, Valeau V, Gervais Y (2011) Ensemble-empirical-mode-decomposition method for instantaneous spatial-multi-scale decomposition of wall-pressure fluctuations under a turbulent flow. Exp Fluids 50(2):339–350. https://doi.org/10.1007/s00348-010-0925-x

    Article  Google Scholar 

  • Dehghani M, Saghafian B, Rivaz F, Khodadadi A (2017) Evaluation of dynamic regression and artificial neural networks models for real-time hydrological drought forecasting. Arab J Geosci. https://doi.org/10.1007/s12517-017-2990-4

    Article  Google Scholar 

  • Deo RC, Tiwari MK, Adamowski JF, Quilty JM (2017) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Environ Res Risk Assess. 31(5):1211–1240. https://doi.org/10.1007/s00477-016-1265-z

    Article  Google Scholar 

  • Di C, Yang X, Wang X (2014) A four-stage hybrid model for hydrological time series forecasting. PLoS ONE 9(8):e104663. https://doi.org/10.1371/journal.pone.0104663

    Article  CAS  Google Scholar 

  • Durdu ÖF (2010) Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, Western Turkey. Stoch Environ Res Risk Assess 24(8):1145–1162. https://doi.org/10.1007/s00477-010-0366-3

    Article  Google Scholar 

  • Elliott G, Rothenberg TJ, Stock JH (1996) Efficient tests for an autoregressive unit root. Econometrica 64(4):813. https://doi.org/10.2307/2171846

    Article  Google Scholar 

  • Fung KF, Huang YF, Koo CH (2019) Coupling fuzzy–SVR and boosting–SVR models with wavelet decomposition for meteorological drought prediction. Environ Earth Sci 78(24):693. https://doi.org/10.1007/s12665-019-8700-7

    Article  Google Scholar 

  • Fung KF, Huang YF, Koo CH (2018) Improvement of SVR-based drought forecasting models using wavelet pre-processing technique. E3S Web Conf 65:07007. https://doi.org/10.1051/e3sconf/20186507007

    Article  Google Scholar 

  • Guo W et al (2023) Quantifying the effects of nonlinear trends of meteorological factors on drought dynamics. Nat Hazards 117(3):2505–2526. https://doi.org/10.1007/s11069-023-05954-7

    Article  Google Scholar 

  • Guttman NB (1998) Comparing the palmer drought index and the standardized precipitation index. J Am Water Resour As 34(1):113–121. https://doi.org/10.1111/j.1752-1688.1998.tb05964.x

    Article  Google Scholar 

  • Hayes M, Svoboda M, Wall N, Widhalm M (2011) The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bull Am Meteorol Soc 92(4):485–488. https://doi.org/10.1175/2010BAMS3103.1

    Article  Google Scholar 

  • Heim RR (2002) A review of twentieth-century drought indices used in the United States. Bull Am Meteorol Soc 83(8):1149–1166. https://doi.org/10.1175/1520-0477-83.8.1149

    Article  Google Scholar 

  • Hu J, Wang J, Zeng G (2013) A hybrid forecasting approach applied to wind speed time series. Renew Energy 60:185–194. https://doi.org/10.1016/j.renene.2013.05.012

    Article  Google Scholar 

  • Huo Z, Dai X, Feng S, Kang S, Huang G (2013) Effect of climate change on reference evapotranspiration and aridity index in arid region of China. J Hydrol 492:24–34. https://doi.org/10.1016/j.jhydrol.2013.04.011

    Article  Google Scholar 

  • Ismail S, Shabri A (2014) Time series forecasting using least square support vector machine for Canadian lynx data. J Teknol 70(5):11–15. https://doi.org/10.11113/jt.v70.3510

    Article  Google Scholar 

  • Jalalkamali A, Moradi M, Moradi N (2015) Application of several artificial intelligence models and ARIMAX model for forecasting drought using the standardized precipitation index. Int J Environ Sci Technol 12(4):1201–1210. https://doi.org/10.1007/s13762-014-0717-6

    Article  Google Scholar 

  • Jehanzaib M, Sattar MN, Lee J-H, Kim T-W (2020) Investigating effect of climate change on drought propagation from meteorological to hydrological drought using multi-model ensemble projections. Stoch Environ Res Risk Assess 34(1):7–21. https://doi.org/10.1007/s00477-019-01760-5

    Article  Google Scholar 

  • Jehanzaib M, Shah SA, Kim JE, Kim T-W (2023) Exploring spatio-temporal variation of drought characteristics and propagation under climate change using multi-model ensemble projections. Nat Hazards 115(3):2483–2503. https://doi.org/10.1007/s11069-022-05650-y

    Article  Google Scholar 

  • Kantz H, Schreiber T (2003) Nonlinear time series analysis. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511755798

    Book  Google Scholar 

  • Khan N, Sachindra DA, Shahid S, Ahmed K, Shiru MS, Nawaz N (2020) Prediction of droughts over Pakistan using machine learning algorithms. Adv Water Resour 139:103562. https://doi.org/10.1016/j.advwatres.2020.103562

    Article  Google Scholar 

  • Khan MMH, Muhammad NS, El-Shafie A (2020) Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting. J Hydrol 590:125380. https://doi.org/10.1016/j.jhydrol.2020.125380

    Article  Google Scholar 

  • Kisi O, Latifoğlu L, Latifoğlu F (2014) Investigation of empirical mode decomposition in forecasting of hydrological time series. Water Resour Manag 28(12):4045–4057. https://doi.org/10.1007/s11269-014-0726-8

    Article  Google Scholar 

  • Komasi M, Sharghi S, Safavi HR (2018) Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using standardized precipitation index (case study: Urmia Lake, Iran). J Hydroinformatics 20(4):975–988. https://doi.org/10.2166/hydro.2018.115

    Article  Google Scholar 

  • Kousari MR, Hosseini ME, Ahani H, Hakimelahi H (2017) Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities. Theor Appl Climatol 127(1–2):361–380. https://doi.org/10.1007/s00704-015-1624-6

    Article  Google Scholar 

  • Koycegiz C, Buyukyildiz M (2019) Calibration of SWAT and two data-driven models for a data-scarce mountainous headwater in semi-arid Konya closed basin. Water 11(1):147. https://doi.org/10.3390/w11010147

    Article  Google Scholar 

  • Koycegiz C, Buyukyildiz M (2022) Investigation of precipitation and extreme indices spatiotemporal variability in Seyhan Basin, Turkey. Water Supply 22(12):8603–8624. https://doi.org/10.2166/ws.2022.391

    Article  Google Scholar 

  • Koycegiz C, Buyukyildiz M (2023) Investigation of spatiotemporal variability of some precipitation indices in Seyhan Basin, Turkey: monotonic and sub-trend analysis. Nat Hazards 116(2):2211–2244. https://doi.org/10.1007/s11069-022-05761-6

    Article  Google Scholar 

  • Li J, Zhou S, Hu R (2016) Hydrological drought class transition using SPI and SRI time series by loglinear regression. Water Resour Manag 30(2):669–684. https://doi.org/10.1007/s11269-015-1184-7

    Article  Google Scholar 

  • Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693. https://doi.org/10.1109/34.192463

    Article  Google Scholar 

  • McKee TB, Doesken NJ, Kleist J (1995) Drought monitoring with multiple time scales In: Ninth Proceedings of the conference on applied climatology 11(7) 233–236

  • Mélard G, Pasteels J-M (2000) Automatic ARIMA modeling including interventions, using time series expert software. Int J Forecast 16(4):497–508. https://doi.org/10.1016/S0169-2070(00)00067-4

    Article  Google Scholar 

  • Mayar MA (2021) Droughts on the horizon: can Afghanistan manage this risk? https://www.afghanistan-analysts.org.

  • Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Environ Res Risk Assess 19(5):326–339. https://doi.org/10.1007/s00477-005-0238-4

    Article  Google Scholar 

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012

    Article  Google Scholar 

  • Mishra AK, Singh VP (2011) Drought modeling-a review. J Hydrol 403(1–2):157–175. https://doi.org/10.1016/j.jhydrol.2011.03.049

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153

    Article  Google Scholar 

  • Mossad A, Alazba A (2015) Drought forecasting using stochastic models in a hyper-arid climate. Atmosphere 6(4):410–430. https://doi.org/10.3390/atmos6040410

    Article  Google Scholar 

  • Ntale HK, Gan TY (2003) Drought indices and their application to East Africa. Int J Climatol 23(11):1335–1357. https://doi.org/10.1002/joc.931

    Article  Google Scholar 

  • Ozger M, Mishra AK, Singh VP (2011) Estimating Palmer drought severity index using a wavelet fuzzy logic model based on meteorological variables. Int J Climatol 31(13):2021–2032. https://doi.org/10.1002/joc.2215

    Article  Google Scholar 

  • Pandhiani SM, Sihag P, Bin Shabri A, Singh B, Pham QB (2020) Time-series prediction of streamflows of malaysian rivers using data-driven techniques. J Irrig Drain Eng 146(7):04020013. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001463

    Article  Google Scholar 

  • Park S, Im J, Jang E, Rhee J (2016) Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agric For Meteorol 216:157–169. https://doi.org/10.1016/j.agrformet.2015.10.011

    Article  Google Scholar 

  • Rahmat SN, Jayasuriya N, Bhuiyan MA (2017) Short-term droughts forecast using Markov chain model in Victoria. Aust Theor Appl Climatol 129(1–2):445–457. https://doi.org/10.1007/s00704-016-1785-y

    Article  Google Scholar 

  • Rezaiy R, Shabri A (2023) Using the ARIMA/SARIMA Model for Afghanistan’s Drought Forecasting Based on Standardized Precipitation Index. Matematika 39(3):239–261. https://doi.org/10.11113/matematika.v39.n3.1478

    Article  Google Scholar 

  • Rezaiy R, Shabri A (2023) Drought forecasting using W-ARIMA model with standardized precipitation index. J Water Clim Chang 14(9):3345–3367. https://doi.org/10.2166/wcc.2023.431

    Article  Google Scholar 

  • Rezaiy R, Shabri A (2024) Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index. Water Sci Technol 89(3):745–770. https://doi.org/10.2166/wst.2024.028

    Article  Google Scholar 

  • Roushangar K, Ghasempour R, Nourani V (2021) The potential of integrated hybrid pre-post-processing techniques for short- to long-term drought forecasting. J Hydroinformatics 23(1):117–135. https://doi.org/10.2166/hydro.2020.088

    Article  Google Scholar 

  • Schwarz G (1978) Estimating the Dimension of a Model. Ann Stat 6(2):461–464

    Article  Google Scholar 

  • Shaari MA, Samsudin R, Ilman AS, Yahya AE (2020) Drought forecasting using gaussian process regression (GPR) and empirical wavelet transform (EWT)-GPR in Gua Musang, pp 152–161. https://doi.org/10.1007/978-3-030-33582-3_15.

  • Shabri A (2014) A hybrid wavelet analysis and adaptive neuro-fuzzy inference system for drought forecasting. Appl Math Sci 8:6909–6918. https://doi.org/10.12988/ams.2014.48263

    Article  Google Scholar 

  • Sheffield J et al (2014) A drought monitoring and forecasting system for Sub-Sahara African water resources and food security. Bull Am Meteorol Soc 95(6):861–882. https://doi.org/10.1175/BAMS-D-12-00124.1

    Article  Google Scholar 

  • Shibata R (1976) Selection of the order of an autoregressive model by Akaike’s information criterion. Biometrika 63(1):117. https://doi.org/10.2307/2335091

    Article  Google Scholar 

  • Shirmohammadi B, Moradi H, Moosavi V, Semiromi MT, Zeinali A (2013) Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran). Nat Hazards 69(1):389–402. https://doi.org/10.1007/s11069-013-0716-9

    Article  Google Scholar 

  • Soh YW, Koo CH, Huang YF, Fung KF (2018) Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat river basin Malaysia. Comput Electron Agric 144(164):173. https://doi.org/10.1016/j.compag.2017.12.002

    Article  Google Scholar 

  • Soltani S (2002) On the use of the wavelet decomposition for time series prediction. Neurocomputing 48(1–4):267–277. https://doi.org/10.1016/S0925-2312(01)00648-8

    Article  Google Scholar 

  • Sun W, Wang Y (2018) Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network. Energy Convers Manag 157:1–12. https://doi.org/10.1016/j.enconman.2017.11.067

    Article  Google Scholar 

  • Tongal H (2013) Nonlinear forecasting of stream flows using a chaotic approach and artificial neural networks. Earth Sci Res J 17(2):119–126

    Google Scholar 

  • Wang W, Chau K, Xu D, Chen X-Y (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manag 29(8):2655–2675. https://doi.org/10.1007/s11269-015-0962-6

    Article  Google Scholar 

  • Wu Z, Huang NE (2004) A study of the characteristics of white noise using the empirical mode decomposition method. Proc R Soc London Ser A Math Phys Eng Sci. 460(2046):1597–1611. https://doi.org/10.1098/rspa.2003.1221

    Article  Google Scholar 

  • Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 01(01):1–41. https://doi.org/10.1142/S1793536909000047

    Article  Google Scholar 

  • Yeh HF, Hsu HL (2019) Stochastic model for drought forecasting in the Southern Taiwan basin. Water (Switzerland) 11(10):2041. https://doi.org/10.3390/w11102041

    Article  Google Scholar 

  • Yihdego Y, Vaheddoost B, Al-Weshah RA (2019) Drought indices and indicators revisited. Arab J Geosci. https://doi.org/10.1007/s12517-019-4237-z

    Article  Google Scholar 

  • Zargar A, Sadiq R, Naser B, Khan FI (2011) A review of drought indices. Environ Rev 19:333–349. https://doi.org/10.1139/a11-013

    Article  Google Scholar 

  • Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175. https://doi.org/10.1016/S0925-2312(01)00702-0

    Article  Google Scholar 

  • Zhang J, Yan R, Gao RX, Feng Z (2010) Performance enhancement of ensemble empirical mode decomposition. Mech Syst Signal Process 24(7):2104–2123. https://doi.org/10.1016/j.ymssp.2010.03.003

    Article  Google Scholar 

  • Zhang X, Peng Y, Zhang C, Wang B (2015) Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? some experiment evidences. J Hydrol 530:137–152. https://doi.org/10.1016/j.jhydrol.2015.09.047

    Article  Google Scholar 

  • Zhang H, Zhang S, Wang P, Qin Y, Wang H (2017) Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China. J Air Waste Manage As 67(7):776–788. https://doi.org/10.1080/10962247.2017.1292968

    Article  CAS  Google Scholar 

  • Zhao X, Chen X (2015) Auto regressive and ensemble empirical mode decomposition hybrid model for annual runoff forecasting. Water Resour Manag 29(8):2913–2926. https://doi.org/10.1007/s11269-015-0977-z

    Article  Google Scholar 

  • Zhao L et al (2014) Impact of meteorological drought on streamflow drought in Jinghe river basin of China. Chin Geogr Sci 24(6):694–705. https://doi.org/10.1007/s11769-014-0726-x

    Article  Google Scholar 

  • Zhu C, Byrd RH, Lu P, Nocedal J (1997) Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans Math Softw 23(4):550–560. https://doi.org/10.1145/279232.279236

    Article  Google Scholar 

  • Zhu S, Zhou J, Ye L, Meng C (2016) Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China. Environ Earth Sci 75(6):531. https://doi.org/10.1007/s12665-016-5337-7

    Article  Google Scholar 

  • Zhu S, Luo X, Chen S, Xu Z, Zhang H, Xiao Z (2020) Improved hidden markov model incorporated with copula for probabilistic seasonal drought forecasting. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001901

    Article  Google Scholar