Skip to main content
SpringerLink
Log in

Drought Forecasting using Markov Chain Model and Artificial Neural Networks

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Water resources management is a complex task. It requires accurate prediction of inflow to reservoirs for the optimal management of surface resources, especially in arid and semi-arid regions. It is in particular complicated by droughts. Markov chain models have provided valuable information on drought or moisture conditions. A complementary method, however, is required that can both evaluate the accuracy of the Markov chain models for predicted drought conditions, and forecast the values for ensuing months. To that end, this study draws on Artificial Neural Networks (ANNs) as a data-driven model. The employed ANNs were trained and tested by means of a statistically-based input selection procedure to accurately predict reservoir inflow and consequently drought conditions. Thirty three years’ data of inflow volume on a monthly time resolution were selected to enable calculation of the standardized streamflow index (SSI) for the Markov chain model. Availability of hydro-climatic data from the Doroodzan reservoir in the Fars province, Iran, allowed us to develop a reservoir specific ANN model. Results demonstrated that both models accurately predicted drought conditions, by employing a randomization procedure that facilitated the selection of the required data for the ANN to forecast reservoir inflow close to the observed values over a validation period. The results confirmed that combining the two models improved short-term prediction reliability. This was in contrast to single model applications that resulted into substantial uncertainty. This research emphasized the importance of the correct selection of data or data mining, prior to entering a specific modeling routine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Araghinejad S (2011) An approach for probabilistic hydrological drought forecasting. Water Resour Manag 25:191–200

  • Chen CLP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Information Sciences 275:314–347

  • Çinlar E (1975) Introduction to stochastic processes. Prentice-Hall, New Jersey, p 402

    Google Scholar 

  • Coulibali P, Anctil F, Bobe’e B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257

    Article  Google Scholar 

  • Coulibali P, Anctil F, Bobee B (2001) Multivariate reservoir inflow forecasting using temporal neural network. J Hydrol Eng 6(5):367–376

    Article  Google Scholar 

  • Coulibaly P, Hache´ M, Fortin V, Bobe´e B (2005). Improving daily reservoir inflow forecasts with model combination. J Hydrol Eng 10(2):91–99

  • Dawson CW, Wilby R (1998). “An artificial neural network approach to rainfall–runoff modeling.” Hydrol Sci J 43(1):47–66

  • Edwards CD, McKee TB (1997). Characteristics of 20th century drought in the United States at multiple time scales. Atmospheric Science Paper No. 634, Climatology Report, No. 97-2, Department of Atmospheric Sciences, Colorado State University

  • Emamgholizadeh S, Moslemi K, Karami G (2014) Prediction the groundwater level of bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Water ResourManag 28(15):5433–5446

  • Hayes MJ, Svoboda MD, Wihite DA, Vanyarhko OV (1999). Monitoring the 1996 drought using the standardized precipitation index, Bulletin of American Meteorological Society 80(3):429–438

  • Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Article  Google Scholar 

  • Isik S, Kalin L, Schoonover J, Srivastava P, Lockaby B (2013) Modeling effects of changing land use/cover on daily streamflow: an artificial neural network and curve number based hybrid approach. J Hydrol 485:103–12

    Article  Google Scholar 

  • Jain SK, Das A, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plann Manag 125(5):263–271

    Article  Google Scholar 

  • Kalin L, Isik S, Schoonover JE, Lockaby BG (2010). “Predicting water quality in unmonitored watersheds using artificial neural networks.” J Environ Qual 39(4):1429–1440

  • Keskin ME, Terzi O, Taylan ED, Küçükyaman D (2011) Meteorological drought analysis using artificial neural networks. Sci Res Essays 6(21):4469–4477

    Google Scholar 

  • Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539

    Article  Google Scholar 

  • Krishna B (2014) Comparison of wavelet-based ANN and regression models for reservoir inflow forecasting. J Hydrol Eng 19(7):1385–1400

    Article  Google Scholar 

  • Kumar APS, Sudheer KP, Jain SK, Agarwal PK (2005) Rainfall-runoff modeling using artificial neural networks: comparison of network types. Hydrol Process 19:1277–1291

    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(6):1375–1386

  • McKee TB, Doesken NJ, Kleist J (1993). The relationship of drought frequency and duration to time scales. In, Proc. 8th Conf. on Applied Climatology, January 17 – 22, 1993. American Meteorological Society, Massachusetts, pp. 179 – 184

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

    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 

  • Nalbantis I, Tsakiris G (2009) Assessment of hydrological droughts revisited. Water Resour Manag 23:881–897

  • Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21(10):1813–1827

    Article  Google Scholar 

  • Rezaeianzadeh M, Tabari H (2012) MLP-based drought forecasting in different climatic regions. Theor Appl Climatol 109(3–4):407–414

    Article  Google Scholar 

  • Rezaeianzadeh M, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multi-layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manag 24(11):2673–2688

    Article  Google Scholar 

  • Rezaeianzadeh M, Stein A, Tabari H, Abghari H, Jalalkamali N, Hosseinipour EZ, Singh VP (2013a) Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. Int J Environ Sci Technol 10(6):1181–1192

    Article  Google Scholar 

  • Rezaeianzadeh M, Tabari H, ArabiYazdi A, Isik S, Kalin L (2013b) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput & Applic 25(1):25–37

    Article  Google Scholar 

  • Rezaeianzadeh M, Tabari H, Abghari H (2013c) Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J Geosci 6(7):2529–2537

  • Rezaeianzadeh M, Kalin L, Anderson C (2015) Wetland water-level prediction using ANN in conjunction with base-flow recession analysis. J Hydrol Eng doi:10.1061/(ASCE)HE.1943-5584.0001276, D4015003

  • Sattari MT, Yurekli K, Pal M (2012) Performance evaluation of artificial neural network approaches in forecasting reservoir inflow. Appl Math Model 36(6):2649–2657

    Article  Google Scholar 

  • Shamseldin AY (1997) Application of a neural network technique to rainfall-runoff modelling. J Hydrol 199:272–294

    Article  Google Scholar 

  • Singh KP, Basant A, Malik A, Jain G (2009). “Artificial neural network modeling of the river water quality: A case study.” Ecol Modell 220:888–895

  • Sonnadara DUJ, Jayewardene DR (2015) A Markov chain probability model to describe wet and dry patterns of weather at Colombo. Theor Appl Climatol 119(1–2):333–340

    Article  Google Scholar 

  • Tabari H, Nikbakht J, Hosseinzade P (2013) Hydrological drought assessment in Northwesterm Iran based on streamflow drought index (SDI). Water Resour Manag 27:137–151

  • Tabari H, Zamani R, Rahmati H, Willems P (2015) Markov Chains of different orders for streamflow drought analysis. Water Resour Manag 29:3441–3457

  • Tabrizi AA, Khalili D, Kamgar-Haghighi AA, Zand-Parsa S (2010) Utilization of time-based meteorologicaldroughts to investigate occurrence of streamflow droughts. Water Resour Manag 24:4287–4306

    Article  Google Scholar 

  • Tokar AS, Johnson A (1999) Rainfall–runoff modeling using artificial neural networks. J Hydrol Eng 4(3):232–239

    Article  Google Scholar 

  • Tsakiris G, Vangelis H (2004) Towards a drought watch system based on spatial SPI. Water Resour Manag 18:1–12

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

  • Tsakiris G, Pangalou D, Vangelis H (2006) Regional drought assessment based on the reconnaissance drought index (RDI). Water Resour Manag 21(5):821–833

  • Tsakiris G, Nalbantis I, Vangelis H, Verbeiren B, Huysmans M, Tychon B, Jacquemin I, Canters F, VanderhaegenS EG, Poelmans L, De Becker P, Batelaan O (2013) A system-based paradigm of drought analysisfor operational management. Water Resour Manag 27(15):5281–5297

  • Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J Hydrol 476:433–441

    Article  Google Scholar 

  • Wilks DS (1995) Statistical methods in the atmospheric sciences. Academic Press

Download references

Acknowledgments

This project was funded by Fars Regional Water Authority with the contract number of FAW-88028. The first author acknowledges the support of Ms. Armina Soleymani for her help in manuscript preparation.

Author information

Authors and Affiliations

  1. School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL, 36849, USA

    Mehdi Rezaeianzadeh

  2. Faculty of Geo-Information Science and Earth Observation (ITC), Twente University, Enschede, The Netherlands

    Alfred Stein

  3. Caribbean Institute for Meteorology and Hydrology, West Indies, Barbados

    Jonathan Peter Cox

Authors
  1. Mehdi Rezaeianzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alfred Stein
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan Peter Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Rezaeianzadeh.

Ethics declarations

Ethical Statement

This manuscript entitled “Drought Forecasting using Markov Chain Model and Artificial Neural Networks” conforms to all ethical rules listed below:

• The manuscript has not been submitted to more than one journal for simultaneous consideration.

• The manuscript has not been published previously (partly or in full), unless the new work concerns an expansion of previous work (please provide transparency on the re-use of material to avoid the hint of text-recycling (“self-plagiarism”)).

• A single study is not split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time (e.g., “salami-publishing”).

• No data have been fabricated or manipulated (including images) to support your conclusions

• No data, text, or theories by others are presented as if they were the author’s own (“plagiarism”). Proper acknowledgements to other works must be given (this includes material that is closely copied (near verbatim), summarized and/or paraphrased), quotation marks are used for verbatim copying of material, and permissions are secured for material that is copyrighted.

• Consent to submit has been received explicitly from all co-authors, as well as from the responsible authorities - tacitly or explicitly - at the institute/organization where the work has been carried out, before the work is submitted.

• Authors whose names appear on the submission have contributed sufficiently to the scientific work and therefore share collective responsibility and accountability for the results.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rezaeianzadeh, M., Stein, A. & Cox, J.P. Drought Forecasting using Markov Chain Model and Artificial Neural Networks. Water Resour Manage 30, 2245–2259 (2016). https://doi.org/10.1007/s11269-016-1283-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-016-1283-0

Keywords

Search

Navigation