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Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities

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Abstract

An effective forecast of the drought definitely gives lots of advantages in regard to the management of water resources being used in agriculture, industry, and households consumption. To introduce such a model applying simple data inputs, in this study a regional drought forecast method on the basis of artificial intelligence capabilities (artificial neural networks) and Standardized Precipitation Index (SPI in 3, 6, 9, 12, 18, and 24 monthly series) has been presented in Fars Province of Iran. The precipitation data of 41 rain gauge stations were applied for computing SPI values. Besides, weather signals including Multivariate ENSO Index (MEI), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), NINO1+2, anomaly NINO1+2, NINO3, anomaly NINO3, NINO4, anomaly NINO4, NINO3.4, and anomaly NINO3.4 were also used as the predictor variables for SPI time series forecast the next 12 months. Frequent testing and validating steps were considered to obtain the best artificial neural networks (ANNs) models. The forecasted values were mapped in verification sector then they were compared with the observed maps at the same dates. Results showed considerable spatial and temporal relationships even among the maps of different SPI time series. Also, the first 6 months forecasted maps showed an average of 73 % agreements with the observed ones. The most important finding and the strong point of this study was the fact that although drought forecast in each station and time series was completely independent, the relationships between spatial and temporal predictions remained. This strong point mainly referred to frequent testing and validating steps in order to explore the best drought forecast models from plenty of produced ANNs models. Finally, wherever the precipitation data are available, the practical application of the presented method is possible.

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References

  • Abramowitz M, Stegun A (1965) Handbook of mathematical formulas, graphs, and mathematical tables. Dover Publications Inc, New York

    Google Scholar 

  • Ahani H, Kherad M, Kousari M, Rezaeian-Zadeh M, Karampour M, Ejraee F, Kamali S (2012) An investigation of trends in precipitation volume for the last three decades in different regions of Fars Province, Iran. Theor Appl Climatol 109:361–382

    Article  Google Scholar 

  • Aherne J, Larssen T, Cosby BJ, Dillon PJ (2006) Climate variability and forecasting surface water recovery from acidification: modelling drought-induced sulphate release from wetlands. Sci Total Environ 365:186–199

    Article  Google Scholar 

  • Anderson ML, Kavvas ML, Mierzwa MD (2001) Probabilistic/ensemble forecasting: a case study using hydrologic response distributions associated with El Niño/Southern Oscillation (ENSO). J Hydrol 249:134–147

    Article  Google Scholar 

  • ArcGIS (2008) ArcGIS desktop 9.3 Demos. http://esri.com. 2008-06-26. Retrieved 2009-04-16

  • Bari Abarghouei H, Kousari M, Asadi Zarch M (2011) Prediction of drought in dry lands through feedforward artificial neural network abilities. Arab J Geosci:1–17

  • Barua S, Perera BJC, Ng AWM, Tran D (2010) Drought forecasting using an aggregated drought index and artificial neural network. Journal of Water and Climate Change 1:193–206

    Article  Google Scholar 

  • 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 2012:13

    Article  Google Scholar 

  • Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J Hydrol 508:418–429

    Article  Google Scholar 

  • Beven KJ (2003) Rainfall-runoff modelling: the primer. Wiley, Chichester

    Google Scholar 

  • Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  • Cancelliere A, Mauro GD, Bonaccorso B, Rossi G (2007a) Drought forecasting using the Standardized Precipitation Index. Water Resour Manage 21:801–819

    Article  Google Scholar 

  • Cancelliere A, Mauro GD, Bonaccorso B, Rossi G (2007b) Stochastic forecasting of drought indices. In: Rossi G, Vega T, Bonaccorso B (eds) Methods and tools for drought analysis and management. Springer, Netherlands, pp 83–100

    Chapter  Google Scholar 

  • Chen F-W, Liu C-W (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10:209–222

    Article  Google Scholar 

  • Chen J, Jin Q, Chao J (2012) Design of deep belief networks for short-term prediction of drought index using data in the Huaihe River basin. Math Probl Eng 2012:16

    Google Scholar 

  • Choi K-S, Oh S-B, Byun H-R, Kripalani RH, Kim D-W (2011) Possible linkage between East Asian summer drought and North Pacific Oscillation. Theor Appl Climatol 103:81–93

    Article  Google Scholar 

  • Chow TWS, Cho SY (1997) Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong. Neural Comput & Applic 5:66–75

    Article  Google Scholar 

  • Chung C, Salas J (2000) Drought occurrence probabilities and risks of dependent hydrologic processes. J Hydrol Eng 5:259–268

    Article  Google Scholar 

  • Cutore P, Di Mauro G, Cancelliere A (2009) Forecasting Palmer Index using neural networks and climatic indexes. J Hydrol Eng 14:588–595

    Article  Google Scholar 

  • Dastorani MT, Afkhami H, Sharifidarani H, Dastorani M (2010) Application of ANN and ANFIS models on dryland precipitation prediction (case study: Yazd in Central Iran). J Appl Sci 10:2387–2394

    Article  Google Scholar 

  • Dezfuli A, Karamouz M, Araghinejad S (2010) On the relationship of regional meteorological drought with SOI and NAO over southwest Iran. Theor Appl Climatol 100:57–66

    Article  Google Scholar 

  • Durdu Ö (2010) Application of linear stochastic models for drought forecasting in the Büyük Menderes River basin, western Turkey. Stoch Environ Res Ris Assess 24:1145–1162

    Article  Google Scholar 

  • Easterling WE (1989) Coping with drought hazard: recent progress and research priorities. In: Siccardi F, Bras RL (eds) Natural disasters in European Mediterranean countries. US National Science Foundation and National Research Council of Italy, Perugia, pp 231–270

    Google Scholar 

  • Edwards D, McKee T (1997) Characteristics of 20th century drought in the United States at multiple timescales. Colorado State University Fort Collins, Climatology Report

    Google Scholar 

  • Erol Keskin M, Terzi Ö, Dilek Taylan E, Küçükyaman D (2011) Meteorological drought analysis using artificial neural networks. Sci Res Essays 6:4469–4477

    Article  Google Scholar 

  • Farokhnia A, Morid S, Byun H-R (2011) Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques. Theor Appl Climatol 104:71–81

    Article  Google Scholar 

  • Gelcer E, Fraisse C, Dzotsi K, Hu Z, Mendes R, Zotarelli L (2013) Effects of El Niño Southern Oscillation on the space–time variability of Agricultural Reference Index for Drought in midlatitudes. Agric For Meteorol 174–175:110–128

    Article  Google Scholar 

  • Guttman NB (1998) Comparing the Palmer drought index and the Standardized Precipitation Index. J Am Water Resour Assoc 34:113–121

    Article  Google Scholar 

  • Hammer GL, Hansen JW, Phillips JG, Mjelde JW, Hill H, Love A, Potgieter A (2001) Advances in application of climate prediction in agriculture. Agric Syst 70:515–553

    Article  Google Scholar 

  • Han P, Wang PX, Zhang SY, Zhu DH (2010) Drought forecasting based on the remote sensing data using ARIMA models. Math Comput Model 51:1398–1403

    Article  Google Scholar 

  • Hao Z, AghaKouchak A, Nakhjiri N, Farahmand A (2014) Global integrated drought monitoring and prediction system. Scientific Data

    Google Scholar 

  • Hayes M, Wilhite DA, Svodoba M, Vanyarkho O (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80:429–438

    Article  Google Scholar 

  • Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, Menlo Park

    Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Network:359–366

  • Hughes B, Saunders M (2002) A drought climatology for Europe. Int J Climatol 22:1571–1592

    Article  Google Scholar 

  • Jiang X-C, Chen S-F (2009) Application of weighted Markov SCGM(1,1)c model to predict drought crop area. Systems Engineering - Theory & Practice 29:179–185

    Article  Google Scholar 

  • Jones JW, Hansen JW, Royce FS, Messina CD (2000) Potential benefits of climate forecasting to agriculture. Agric Ecosyst Environ 82:169–184

    Article  Google Scholar 

  • Karamouz M, Rasouli K, Nazif S (2009) Development of a hybrid index for drought prediction: case study. J Hydrol Eng 14:617–627

  • Karmakar S, Kowar MK, Guhathakurta P (2008) Development of an 8-parameter probabilistic artificial neural network model for long-range monsoon rainfall pattern recognition over the smaller scale geographical region–district, 2008 ICETET ‘08 First International Conference on Emerging Trends in Engineering and Technology., pp 569–574

    Google Scholar 

  • Karmakar S, Kowar MK, Guhathakurta P. (2009a) Evolving and evaluation of 3LP FFBP deterministic ANN model for district level long range monsoon rainfall prediction. J Environ Sci Eng 51(2):137-144

  • Karmakar S, Kowar MK, Guhathakurta P (2009b) Long-range monsoon rainfall pattern recognition and prediction for the subdivision ‘EPMB’ Chhattisgarh using deterministic and probabilistic neural network, 2009 ICAPR ‘09 Seventh International Conference on Advances in Pattern Recognition., pp 367–370

    Google Scholar 

  • Kendall DR, Dracup JA (1992) On the generation of drought events using an alternating renewal-reward model. Stochastic Hydrol Hydraul 6:55–68

    Article  Google Scholar 

  • Kisi O, Sanikhani H (2015) Prediction of long-term monthly precipitation using several soft computing methods without climatic data. Int J Climatol. doi: 10.1002/joc.4273

  • Kousari MR, Ahani H, Hakimelahi H (2013) An investigation of near surface wind speed trends in arid and semiarid regions of Iran. Theor Appl Climatol 1–16

  • Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4:4–22

    Article  Google Scholar 

  • Lohani VK, Loganathan GV (1997) An early warning system for drought management using the Palmer drought index. J Am Water Resour Assoc 33:1375–1386

    Article  Google Scholar 

  • Lohani VK, Loganathan GV, Mostaghimi S (1998) Long-term analysis and short-term forecasting of dry spells by Palmer drought severity index. Nord Hydrol 29:21–40

    Google Scholar 

  • Long J, Ying L, Zhenshan L (1997) Comparison of long-term forecasting of June–August rainfall over Changjiang-Huaihe Valley. Adv Atmos Sci 14:87–92

    Article  Google Scholar 

  • Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34:1044–1055

    Article  Google Scholar 

  • Mabaso MLH, Kleinschmidt I, Sharp B, Smith T (2007) El Niño Southern Oscillation (ENSO) and annual malaria incidence in Southern Africa. Trans R Soc Trop Med Hyg 101:326–330

    Article  Google Scholar 

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124

    Article  Google Scholar 

  • Maier HR, Dandy GC (2001) Neural network based modelling of environmental variables: a systematic approach. Math Comput Model 33:669–682

    Article  Google Scholar 

  • Masinde M (2013) Artificial neural networks models for predicting effective drought index: factoring effects of rainfall variability. Mitig Adapt Strateg Glob Change:1–24

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas imminent in nervous activity. Bulletin and Mathematical Biophysics 5:115–133

    Article  Google Scholar 

  • McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proc 8th conference on applied climatology, Anaheim, California., pp 179–184

    Google Scholar 

  • Meza FJ (2005) Variability of reference evapotranspiration and water demands. Association to ENSO in the Maipo River basin, Chile. Glob Planet Chang 47:212–220

    Article  Google Scholar 

  • Mishra AK, Desai VR (2005a) Drought forecasting using stochastic models. Stoch Environ Res Ris Assess 19:326–339

    Article  Google Scholar 

  • Mishra AK, Desai VR (2005b) Drought forecasting using stochastic models. Stoch Environ Res Risk Assessment 19:326–339

    Article  Google Scholar 

  • Mishra AK, Desai VR (2006) Drought forecasting using feed-forward recursive neural network. Ecol Model 198:127–138

    Article  Google Scholar 

  • Mo KC, Schemm J-KE, Yoo S-H (2009) Influence of ENSO and the Atlantic Multidecadal Oscillation on drought over the United States. J Clim 22:5962–5982

    Article  Google Scholar 

  • Modarres R (2007) Streamflow drought time series forecasting. Stoch Environ Res Ris Assess 21:223–233

    Article  Google Scholar 

  • Moradi HR, Rajabi M, Faragzadeh M (2011) Investigation of meteorological drought characteristics in Fars Province, Iran. CATENA 84:35–46

    Article  Google Scholar 

  • Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27:2103–2111

    Article  Google Scholar 

  • Ochola WO, Kerkides P (2003) A Markov chain simulation model for predicting critical wet and dry spells in Kenya: analysing rainfall events in the Kano Plains. Irrig Drain 52:327–342

    Article  Google Scholar 

  • Özger M, Mishra AK, Singh VP (2009) Low frequency drought variability associated with climate indices. J Hydrol 364:152–162

    Article  Google Scholar 

  • Özger M, Mishra AK, Singh VP (2012) Long lead time drought forecasting using a wavelet and fuzzy logic combination model: a case study in Texas. J Hydrometeorol 13:284–297

    Article  Google Scholar 

  • Palmer WC (1965) Meteorological drought. Research paper no 45. US Department of Commerce Weather Bureau, Washington

    Google Scholar 

  • Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manage 21:1813–1827

    Article  Google Scholar 

  • Rahimikhoob A (2010) Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran. Theor Appl Climatol 101:83–91

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing. MIT Press, Cambridge

    Google Scholar 

  • Saldariaga J, Yevjevich V (1970) Application of run-lengths to hydrologic series Hydrol Paper. Colorado State University Publication, Colorado State University, Fort Collins, CO

    Google Scholar 

  • Sen Z (1976) Wet and dry periods of annual flow series. J Hydrol Div ASCE 106:99–115

    Google Scholar 

  • Sen Z (1977) Run sums of annual flow series. J Hydrol 35:311–324

    Article  Google Scholar 

  • Sen Z (1990) Critical drought analysis by second-order Markov chain. J Hydrol 120:183–202

    Article  Google Scholar 

  • Shrestha A, Kostaschuk R (2005) El Niño/Southern Oscillation (ENSO)-related variability in mean-monthly streamflow in Nepal. J Hydrol 308:33–49

    Article  Google Scholar 

  • Shrivastava G, Sanjeev K, Manoj Kumar K, Guhathakurta P (2012) Application of artificial neural networks in weather forecasting: a comprehensive literature review. International Journal of Computer Applications 51:17–29

    Article  Google Scholar 

  • Singh P, Borah B (2013) Indian summer monsoon rainfall prediction using artificial neural network. Stoch Environ Res Ris Assess 27:1585–1599

    Article  Google Scholar 

  • Soufi M (2004) Morpho-climatic classification of gullies in Fars Province, southwest of Iran. In: ISCO 2004e 13th international soil conservation organisation conference, Brisbane

    Google Scholar 

  • Steinemann A (2003) Drought indicators and triggers: a stochastic approach to evaluation 1. JAWRA 39:1217–1233

    Google Scholar 

  • Tang Z, Fishwick PA (1993) Feedforward neural nets as models for time series forecasting. ORSA J Comput 5:374–385

    Article  Google Scholar 

  • Thom H (1958) A note on gamma distribution. Mon Weather Rev 86:117–122

    Article  Google Scholar 

  • Tsakiris G, Vangelis H (2005) Establishing a drought index incorporating evapotranspiration. European Water 9(10):3–11

    Google Scholar 

  • Tsakiris G, Pangalou D, Vangelis H (2007) Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour Manage 21:821–833

    Article  Google Scholar 

  • Tsakiris G, Nalbantis I, Pangalou D, Tigkas D, Vangelis H (2008) Drought meteorological monitoring network design for the Reconnaissance Drought Index (RDI), 1st International Conference “Drought Management: Scientific and Technological Innovations”. Zaragoza–Spain

    Google Scholar 

  • Tsakiris G, Nalbantis I, Vangelis H, Verbeiren B, Huysmans M, Tychon B, Jacquemin I, Canters F, Vanderhaegen S, Engelen G, Poelmans L, Becker P, Batelaan O (2013) A system-based paradigm of drought analysis for operational management. Water Resour Manage 27:5281–5297

    Article  Google Scholar 

  • Ubilava D, Helmers CG (2013) Forecasting ENSO with a smooth transition autoregressive model. Environ Model Softw 40:181–190

    Article  Google Scholar 

  • Van Rooy MP (1965) A rainfall anomaly index independent of time and space. Notos 14:43

    Google Scholar 

  • Vicente-Serrano SM, Begueria S, Lopez-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718

    Article  Google Scholar 

  • Voinov AA (2008) Sensitivity, calibration, validation, verification. In: Sven Erik J, Brian F (eds) Encyclopedia of ecology. Academic, Oxford, pp 3221–3227

    Chapter  Google Scholar 

  • Woli P, Jones J, Ingram K, Paz J (2013) Forecasting drought using the agricultural reference index for drought (ARID): a case study. Weather Forecast 28:427–443

    Article  Google Scholar 

  • Wolter K, Timlin MS (1998) Measuring the strength of ENSO—how does 1997/98 rank? Weather 53:315–324

    Article  Google Scholar 

  • Wong FS (1991) Time series forecasting using backpropagation neural networks. Neurocomputing 2:147–159

    Article  Google Scholar 

  • Woong Kim T, Valdés JB (2003) A nonlinear model for drought forecasting based on conjunction of wavelet transforms and neural networks. ASCE J Hydrolog Eng 8:319–328

    Article  Google Scholar 

  • Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389:146–167

    Article  Google Scholar 

  • Yevjevich V (1967) An objective approach to definitions and investigations of continental hydrologic droughts. Hydrol Papers Colorado State University, Fort Collins, CO

    Google Scholar 

  • Yurekli K, Kurunc A (2006) Simulating agricultural drought periods based on daily rainfall and crop water consumption. J Arid Environ 67:629–640

    Article  Google Scholar 

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Acknowledgments

The authors gratefully appreciate the Yazd University and gratefully appreciate the Cadastre group (Management Center for Strategic Projects) in Fars Organization of Agricultural Jahad for their support and providing research facilities.

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Authors and Affiliations

  1. Yazd University, Yazd, Iran

    Mohammad Reza Kousari & Mitra Esmaeilzadeh Hosseini

  2. Management Center for Strategic Projects, Fars Organization Center of Jahad Agriculture, Shiraz, Iran

    Hossein Ahani

  3. Shiraz University, Shiraz, Iran

    Hossein Ahani & Hemila Hakimelahi

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  1. Mohammad Reza Kousari
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  2. Mitra Esmaeilzadeh Hosseini
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  3. Hossein Ahani
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  4. Hemila Hakimelahi
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Correspondence to Hossein Ahani.

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

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