Improving accuracy of neuro fuzzy and support vector regression for drought modelling using grey wolf optimization

References

• Abbasi, A., et al., 2019. Drought monitoring and prediction using SPEI index and gene expression programming model in the west of Urmia Lake. Theoretical and Applied Climatology, 138 (1–2), 553–567. doi:10.1007/s00704-019-02825-9
   Web of Science ®Google Scholar
• Abramowitz, M. and Stegun, I.A., 1965. Handbook of mathematical functions. Vol. 361, New York: Dover Publications.
   Google Scholar
• Adamowski, J.F., 2008. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. Journal of Hydrology, 353 (3–4), 247–266. doi:10.1016/j.jhydrol.2008.02.013
   Web of Science ®Google Scholar
• Aghelpour, P., Bahrami-Pichaghchi, H., and Kisi, O., 2020. Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Computers and Electronics in Agriculture, 170, 105279. doi:10.1016/j.compag.2020.105279
   Web of Science ®Google Scholar
• Alam, N.M., et al., 2014. Stochastic model for drought forecasting for Bundelkhand region in Central India. Indian Journal of Agricultural Sciences, 84 (1), 79–84.
   Google Scholar
• Angelidis, P., et al., 2012. Computation of drought index SPI with alternative distribution functions. Water Resources Management, 26 (9), 2453–2473. doi:10.1007/s11269-012-0026-0
   Web of Science ®Google Scholar
• Asefa, T., et al., 2006. Multi-time scale stream flow predictions: the support vector machines approach. Journal of Hydrology, 318 (1–4), 7–16. doi:10.1016/j.jhydrol.2005.06.001
   Web of Science ®Google Scholar
• Bacanli, U.G., Firat, M., and Dikbas, F., 2009. Adaptive neuro-fuzzy inference system for drought forecasting. Stochastic Environmental Research and Risk Assessment, 23 (8), 1143–1154. doi:10.1007/s00477-008-0288-5
   Web of Science ®Google Scholar
• Belayneh, A. and Adamowski, J., 2012. Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression. Applied Computational Intelligence and Soft Computing, 2012, 1–13. doi:10.1155/2012/794061
   Google Scholar
• Belayneh, A. and Adamowski, J., 2013. Drought forecasting using new machine learning methods/Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod. Journal of Water and Land Development, 18 (9), 3–12. doi:10.2478/jwld-2013-0001
   Google Scholar
• Belayneh, A., et al., 2014. Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Journal of Hydrology, 508, 418–429. doi:10.1016/j.jhydrol.2013.10.052
   Web of Science ®Google Scholar
• Belayneh, A., et al., 2016. Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmospheric Research, 172-173, 37–47. doi:10.1016/j.atmosres.2015.12.017
   Web of Science ®Google Scholar
• Bokde, N.D., Yaseen, Z.M., and Andersen, G.B., 2020. Forecast TB—An R package as a test-bench for time series forecasting—Application of wind speed and solar radiation modelling. Energies, 13 (10), 2578. doi:10.3390/en13102578
   Web of Science ®Google Scholar
• Box, G.E.P. and Jenkins, G.M., 1976. Time Series Analysis, Forecasting and Control. San Francisco, NJ: Holden Day, Prentice-Hall.
   Google Scholar
• Box, G.E.P., Jenkins, G.M., and Reinsel, G.C., 1994. Time Series Analysis, Forecasting and. Englewood Cliffs, NJ, USA: Prentice Hall.
   Google Scholar
• Chai, T. and Draxler, R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? –Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7 (3), 1247–1250. doi:10.5194/gmd-7-1247-2014
   Web of Science ®Google Scholar
• Choubin, B., Malekian, A., and Golshan, M., 2016. Application of several data-driven techniques to predict a standardized precipitation index. Atmósfera, 29 (2), 121–128.
   Web of Science ®Google Scholar
• Çimen, M. and Kisi, O., 2009. Comparison of two different data-driven techniques in modelling lake level fluctuations in Turkey. Journal of Hydrology, 378 (3–4), 253–262. doi:10.1016/j.jhydrol.2009.09.029
   Web of Science ®Google Scholar
• Dai, A., 2011. Drought under global warming: a review. Wiley Interdisciplinary Reviews. Climate Change, 2 (1), 45–65.
   Web of Science ®Google Scholar
• Danandeh Mehr, A., 2018. An improved gene expression programming model for streamflow forecasting in intermittent streams. Journal of Hydrology, 563, 669–678. doi:10.1016/j.jhydrol.2018.06.049
   Web of Science ®Google Scholar
• Dehghani, M., et al., 2014. Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte‐Carlo simulation. International Journal of Climatology, 34 (4), 1169–1180. doi:10.1002/joc.3754
   Web of Science ®Google Scholar
• Deo, R.C., Kisi, O., and Singh, V.P., 2017a. Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmospheric Research, 184, 149–175. doi:10.1016/j.atmosres.2016.10.004
   Web of Science ®Google Scholar
• Deo, R.C., et al., 2018. Drought prediction with standardized precipitation and evapotranspiration index and support vector regression models. In: Integrating disaster science and management. Amsterdam, Netherlands: Elsevier, 151–174.
   Google Scholar
• Djerbouai, S. and Souag-Gamane, D., 2016. Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resources Management, 30 (7), 2445–2464. doi:10.1007/s11269-016-1298-6
   Web of Science ®Google Scholar
• Durdu, Ö.F., 2010. Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey. Stochastic Environmental Research and Risk Assessment, 24 (8), 1145–1162. doi:10.1007/s00477-010-0366-3
   Web of Science ®Google Scholar
• Edwards, D.C., 1997. Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report 97‐2. Fort Collins: Department of Atmospheric Science, Colorado State University.
   Google Scholar
• Edwards, C.A., 2020. Sustainable agricultural systems. Boca Raton, FL: CRC Press.
   Google Scholar
• Emadodin, I., Reinsch, T., and Taube, F., 2019. Drought and desertification in Iran. Hydrology, 6 (3), 66. doi:10.3390/hydrology6030066
   Web of Science ®Google Scholar
• European drought observatory, 2020. Edo indicator factsheet. Available from: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_spi.pdf [Accessed 1 May 2020].
   Google Scholar
• Farahmand, A. and AghaKouchak, A., 2015. A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76, 140–145. doi:10.1016/j.advwatres.2014.11.012
   Web of Science ®Google Scholar
• Fung, K.F., et al., 2020. Drought forecasting: a review of modelling approaches 2007–2017. Journal of Water and Climate Change, 11 (3), 771–799. doi:10.2166/wcc.2019.236
   Web of Science ®Google Scholar
• Ghorbani, H., Vali, A., and Zarepour, H., 2020. Prediction and investigation of meteorological drought using SARIMA time series and SPI index in Isfahan Province. JWSS-Isfahan University of Technology, 23 (4), 313–328.
   Google Scholar
• Gidey, E., et al., 2018. Modelling the spatio-temporal meteorological drought characteristics using the standardized precipitation index (SPI) in raya and its environs, northern Ethiopia. Earth Systems and Environment, 2 (2), 281–292. doi:10.1007/s41748-018-0057-7
   Google Scholar
• Guenang, G.M. and Kamga, F.M., 2014. Computation of the standardized precipitation index (SPI) and its use to assess drought occurrences in Cameroon over recent decades. Journal of Applied Meteorology and Climatology, 53 (10), 2310–2324. doi:10.1175/JAMC-D-14-0032.1
   Web of Science ®Google Scholar
• Guenang, G.M., et al., 2019. Sensitivity of SPI to distribution functions and correlation between its values at different time scales in Central Africa. Earth Systems and Environment, 3 (2), 203–214. doi:10.1007/s41748-019-00102-3
   Google Scholar
• Gumus, V. and Algin, H.M., 2017. Meteorological and hydrological drought analysis of the Seyhan− Ceyhan River Basins, Turkey. Meteorological Applications, 24 (1), 62–73. doi:10.1002/met.1605
   Web of Science ®Google Scholar
• Guttman, N.B., 1998. Comparing the palmer drought index and the standardized precipitation index 1. JAWRA Journal of the American Water Resources Association, 34 (1), 113–121. doi:10.1111/j.1752-1688.1998.tb05964.x
   Web of Science ®Google Scholar
• Hamed, K.H. and Rao, A.R., 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204 (1–4), 182–196. doi:10.1016/S0022-1694(97)00125-X
   Web of Science ®Google Scholar
• Han, P., Wang, P.X., and Zhang, S.Y., 2010. Drought forecasting based on the remote sensing data using ARIMA models. Mathematical and Computer Modelling, 51 (11–12), 1398–1403. doi:10.1016/j.mcm.2009.10.031
   Google Scholar
• Hao, P.Y., Chiang, J.H., and Lin, Y.H., 2009. A new maximal-margin spherical-structured multi-class support vector machine. Applied Intelligence, 30 (2), 98–111. doi:10.1007/s10489-007-0101-z
   Web of Science ®Google Scholar
• Hayes, M.J., et al., 1999. Monitoring the 1996 drought using the standardized precipitation index. Bulletin of the American Meteorological Society, 80 (3), 429–438. doi:10.1175/1520-0477(1999)080<0429:MTDUTS>2.0.CO;2
   Web of Science ®Google Scholar
• Hayes, A.F., Glynn, C.J., and Shanahan, J., 2005. Validating the willingness to self-censor scale: individual differences in the effect of the climate of opinion on opinion expression. International Journal of Public Opinion Research, 17 (4), 443–455. doi:10.1093/ijpor/edh072
   Web of Science ®Google Scholar
• Jalalkamali, A., Moradi, M., and Moradi, N., 2015. Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index. International Journal of Environmental Science and Technology, 12 (4), 1201–1210. doi:10.1007/s13762-014-0717-6
   Web of Science ®Google Scholar
• Jang, J.S., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23 (3), 665–685. doi:10.1109/21.256541
   Web of Science ®Google Scholar
• Kalantar, B., et al., 2018. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, 9 (1), 49–69. doi:10.1080/19475705.2017.1407368
   Web of Science ®Google Scholar
• Karimi, M., et al., 2019. Analysis and prediction of meteorological drought using SPI index and ARIMA model in the Karkheh River Basin, Iran. In: A.M. Melesse, W. Abtew, and G. Senay, eds. Extreme Hydrology and Climate Variability. Amsterdam, Netherlands: Elsevier, 343–353.
   Google Scholar
• Keshavarz, M., Karami, E., and Vanclay, F., 2013. The social experience of drought in rural Iran. Land Use Policy, 30 (1), 120–129. doi:10.1016/j.landusepol.2012.03.003
   Web of Science ®Google Scholar
• Kia, M.B., et al., 2012. An artificial neural network model for flood simulation using GIS: johor River Basin, Malaysia. Environmental Earth Sciences, 67 (1), 251–264. doi:10.1007/s12665-011-1504-z
   Web of Science ®Google Scholar
• Kisi, O. and Cimen, M., 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399 (1–2), 132–140. doi:10.1016/j.jhydrol.2010.12.041
   Web of Science ®Google Scholar
• Kisi, O., et al., 2015. A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Applied Mathematics and Computation, 270, 731–743. doi:10.1016/j.amc.2015.08.085
   Web of Science ®Google Scholar
• Kisi, O., et al., 2019. Drought forecasting using novel heuristic methods in a semi-arid environment. Journal of Hydrology, 578, 124053. doi:10.1016/j.jhydrol.2019.124053
   Web of Science ®Google Scholar
• Legates, D.R. and McCabe, G.J., Jr, 1999. Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35 (1), 233–241. doi:10.1029/1998WR900018
   Web of Science ®Google Scholar
• Li, D. and Simske, S., 2010. Example based single-frame image super-resolution by support vector regression. Journal of Pattern Recognition Research, 5 (1), 104–118. doi:10.13176/11.253
   Google Scholar
• Liong, S.Y. and Sivapragasam, C., 2002. Flood stage forecasting with support vector machines 1. JAWRA Journal of the American Water Resources Association, 38 (1), 173–186. doi:10.1111/j.1752-1688.2002.tb01544.x
   Web of Science ®Google Scholar
• Lloyd‐Hughes, B. and Saunders, M.A., 2002. A drought climatology for Europe. International Journal of Climatology: A Journal of the Royal Meteorological Society, 22 (13), 1571–1592. doi:10.1002/joc.846
   Web of Science ®Google Scholar
• Massey, F.J., Jr, 1951. The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46 (253), 68–78. doi:10.1080/01621459.1951.10500769
   Web of Science ®Google Scholar
• McKee, T.B., Doesken, N.J., and Kleist, J., 1993. The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 17 (22), 179–183. January.
   Google Scholar
• Mehdizadeh, S., Behmanesh, J., and Khalili, K., 2017a. A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model. Journal of Hydrology, 554, 721–742. doi:10.1016/j.jhydrol.2017.09.056
   Web of Science ®Google Scholar
• Mehdizadeh, S. and Sales, A.K., 2018. A comparative study of autoregressive, autoregressive moving average, gene expression programming and Bayesian networks for estimating monthly streamflow. Water Resources Management, 32 (9), 3001–3022. doi:10.1007/s11269-018-1970-0
   Web of Science ®Google Scholar
• Mehdizadeh, S., 2018. Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: offering a new approach for lagged ETo data-based modelling. Journal of Hydrology, 559, 794–812. doi:10.1016/j.jhydrol.2018.02.060
   Web of Science ®Google Scholar
• Mehdizadeh, S., et al., 2020. Drought modelling using classic time series and hybrid wavelet-gene expression programming models. Journal of Hydrology, 587, 125017. doi:10.1016/j.jhydrol.2020.125017
   Web of Science ®Google Scholar
• Mehr, A.D., Kahya, E., and Özger, M., 2014. A gene–wavelet model for long lead time drought forecasting. Journal of Hydrology, 517, 691–699. doi:10.1016/j.jhydrol.2014.06.012
   Web of Science ®Google Scholar
• Mirjalili, S., Mirjalili, S.M., and Lewis, A., 2014. Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. doi:10.1016/j.advengsoft.2013.12.007
   Web of Science ®Google Scholar
• Mishra, A.K. and Desai, V.R., 2006. Drought forecasting using feed-forward recursive neural network. Ecological Modelling, 198 (1–2), 127–138. doi:10.1016/j.ecolmodel.2006.04.017
   Web of Science ®Google Scholar
• Mishra, A.K., Desai, V.R., and Singh, V.P., 2007. Drought forecasting using a hybrid stochastic and neural network model. Journal of Hydrologic Engineering, 12 (6), 626–638. doi:10.1061/(ASCE)1084-0699(2007)12:6(626)
   Web of Science ®Google Scholar
• Moazenzadeh, R., et al., 2018. Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Engineering Applications of Computational Fluid Mechanics, 12 (1), 584–597. doi:10.1080/19942060.2018.1482476
   Web of Science ®Google Scholar
• Modarres, R. and da Silva, V.D.P.R., 2007. Rainfall trends in arid and semi-arid regions of Iran. Journal of Arid Environments, 70 (2), 344–355. doi:10.1016/j.jaridenv.2006.12.024
   Web of Science ®Google Scholar
• Mokhtarzad, M., et al., 2017. Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environmental Earth Sciences, 76 (21), 729. doi:10.1007/s12665-017-7064-0
   Web of Science ®Google Scholar
• Morid, S., Smakhtin, V., and Moghaddasi, M., 2006. Comparison of seven meteorological indices for drought monitoring in Iran. International Journal of Climatology: A Journal of the Royal Meteorological Society, 26 (7), 971–985. doi:10.1002/joc.1264
   Web of Science ®Google Scholar
• Muro, C., et al., 2011. Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behavioural Processes, 88 (3), 192–197. doi:10.1016/j.beproc.2011.09.006
   PubMed Web of Science ®Google Scholar
• Naresh Kumar, M., et al., 2009. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Meteorological Applications: A Journal of Forecasting, Practical Applications, Training Techniques and Modelling, 16 (3), 381–389. doi:10.1002/met.136
   Web of Science ®Google Scholar
• Nguyen, L.B., et al., 2015. Adaptive Neuro–Fuzzy inference system for drought forecasting in the Cai River Basin in Vietnam. Journal of the Faculty of Agriculture, Kyushu University, 60 (2), 405–415. doi:10.5109/1543403
   Web of Science ®Google Scholar
• Nourani, V., et al., 2014. Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358–377. doi:10.1016/j.jhydrol.2014.03.057
   Web of Science ®Google Scholar
• Olsson, M. and Soder, L., 2008. Modelling real-time balancing power market prices using combined SARIMA and Markov processes. IEEE Transactions on Power Systems, 23 (2), 443–450. doi:10.1109/TPWRS.2008.920046
   Web of Science ®Google Scholar
• Rafferty, G., 2021. Forecasting time series data with facebook prophet. Birmingham, UK: Packt Publishing.
   Google Scholar
• Ramezani, Y. and Tahroudi, M.N., 2020. Improving the performance of the SPEI using four-parameter distribution function. Theoretical and Applied Climatology, 139 (3), 1151–1162. doi:10.1007/s00704-019-03034-0
   Web of Science ®Google Scholar
• Rezaeianzadeh, M., Stein, A., and Cox, J.P., 2016. Drought forecasting using Markov chain model and artificial neural networks. Water Resources Management, 30 (7), 2245–2259. doi:10.1007/s11269-016-1283-0
   Web of Science ®Google Scholar
• Rhee, J. and Im, J., 2017. Meteorological drought forecasting for ungauged areas based on machine learning: using long-range climate forecast and remote sensing data. Agricultural and Forest Meteorology, 237-238, 105–122. doi:10.1016/j.agrformet.2017.02.011
   Web of Science ®Google Scholar
• Sachindra, D.A., et al., 2018. Statistical downscaling of precipitation using machine learning techniques. Atmospheric Research, 212, 240–258. doi:10.1016/j.atmosres.2018.05.022
   Web of Science ®Google Scholar
• Shiau, J.T., 2020. Effects of gamma-distribution variations on SPI-based stationary and nonstationary drought analyses. Water Resources Management, 34, 2081–2095.
   Web of Science ®Google Scholar
• Shiri, J. and Kisi, O., 2010. Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. Journal of Hydrology, 394 (3–4), 486–493. doi:10.1016/j.jhydrol.2010.10.008
   Web of Science ®Google Scholar
• Shirmohammadi, B., et al., 2013. Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran). Natural Hazards, 69 (1), 389–402. doi:10.1007/s11069-013-0716-9
   Web of Science ®Google Scholar
• Spinoni, J., et al., 2014. World drought frequency, duration, and severity for 1951–2010. International Journal of Climatology, 34 (8), 2792–2804. doi:10.1002/joc.3875
   Web of Science ®Google Scholar
• Stagge, J.H., et al., 2015. Candidate distributions for climatological drought indices (SPI and SPEI). International Journal of Climatology, 35 (13), 4027–4040. doi:10.1002/joc.4267
   Web of Science ®Google Scholar
• Sudheer, C., et al., 2014. A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Computing & Applications, 24 (6), 1381–1389. doi:10.1007/s00521-013-1341-y
   Web of Science ®Google Scholar
• Takagi, T. and Sugeno, M., 1985. Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15 (1), 116–132. doi:10.1109/TSMC.1985.6313399
   Web of Science ®Google Scholar
• Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106 (D7), 7183–7192. doi:10.1029/2000JD900719
   Web of Science ®Google Scholar
• Thom, H.C., 1958. A note on the gamma distribution. Monthly Weather Review, 86 (4), 117–122. doi:10.1175/1520-0493(1958)086<0117:ANOTGD>2.0.CO;2
   Google Scholar
• Tikhamarine, Y., et al., 2020. Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm. Journal of Hydrology, 582, 124435. doi:10.1016/j.jhydrol.2019.124435
   Web of Science ®Google Scholar
• Tosunoglu, F. and Kisi, O., 2017. Trend analysis of maximum hydrologic drought variables using Mann–Kendall and Şen’s innovative trend method. River Research and Applications, 33 (4), 597–610. doi:10.1002/rra.3106
   Web of Science ®Google Scholar
• Vapnik, V., Golowich, S., and Smola, A., 1996. Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems, 9, 281–287.
   Google Scholar
• Vicente-Serrano, S.M., Beguería, S., and López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23 (7), 1696–1718. doi:10.1175/2009JCLI2909.1
   Web of Science ®Google Scholar
• Willmott, C.J., 1981. On the validation of models. Physical Geography, 2 (2), 184–194. doi:10.1080/02723646.1981.10642213
   Google Scholar
• Willmott, C.J., Robeson, S.M., and Matsuura, K., 2012. A refined index of model performance. International Journal of Climatology, 32 (13), 2088–2094. doi:10.1002/joc.2419
   Web of Science ®Google Scholar
• Zhang, G.P., 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. doi:10.1016/S0925-2312(01)00702-0
   Web of Science ®Google Scholar
• Zhang, F., Dai, H., and Tang, D., 2014. A conjunction method of wavelet transform-particle swarm optimization-support vector machine for streamflow forecasting. Journal of Applied Mathematics, 2014, 1–10.
   Google Scholar
• Zhang, Y., et al., 2017. Multi-models for SPI drought forecasting in the north of Haihe River Basin, China. Stochastic Environmental Research and Risk Assessment, 31 (10), 2471–2481. doi:10.1007/s00477-017-1437-5
   Web of Science ®Google Scholar