Skip to main content
SpringerLink
Log in

Long-term seasonal rainfall forecasting using linear and non-linear modelling approaches: a case study for Western Australia

  • Original Paper
  • Published:
Meteorology and Atmospheric Physics Aims and scope Submit manuscript

Abstract

This paper presents the comparison of the performances between the linear and non-linear modelling techniques in re-generating the patterns of long-term seasonal rainfall in Western Australia. To construct the linear and non-linear models, commonly adopted multiple linear regression (MLR) and artificial neural network (ANN) modelling approaches were applied. Lagged (past) values of the oceanic climate drivers, El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) were considered to be the prospective forecasters of seasonal rainfall. The MLR models which were statistically significant and not susceptible to multicollinearity problems were considered as the potential models. The Lavenberg–Marquardt algorithm with Multilayer Perceptron training rule was adopted to construct the non-linear ANN models. The capability of both the MLR and ANN models were evaluated through commonly used statistical parameters. Since rainfall vary not only temporally but also spatially, the analysis were performed on regional scale. The methods were applied to three Western Australian rainfall stations. As expected, in estimating Western Australian spring rainfalls, non-linear ANN models performed much better compared to MLR models in regards to Pearson correlation as well as statistical errors.

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

Similar content being viewed by others

References

  • Abbot J, Marohasy J (2012) Application of Artificial Neural Networks to rainfall forecasting in Queensland Australia. Adv Atmos Sci 29(4):717–730

    Article  Google Scholar 

  • Abbot J, Marohasy J (2014) Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos Res 138:166–178

    Article  Google Scholar 

  • Anwar MR, Rodriguez D, Liu DL, Power S, O’leary GJ (2008) Quality and potential utility of ENSO-based forecasts of spring rainfall and wheat yield in south-eastern Australia. Aust J Agric Res 59:112–126

    Article  Google Scholar 

  • Ashok K, Guan Z, Yamagata T (2003) Influence of the Indian Ocean Dipole on the Australian winter rainfall. Geophys Res Lett 30:1821

    Article  Google Scholar 

  • Cancelliere A, Giuliano G, Ancarani A, Rossi G (2002) A neural networks approach for deriving irrigation reservoir operating rules. Water Resour Manage 16:71–88

    Article  Google Scholar 

  • Chiang Y, Chang L, Tsai M, Wang Y, Chang F (2010) Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites. Hydrol Earth Syst Sci 14:1309–1319

    Article  Google Scholar 

  • Chiew F, Piechota TC, Dracup J, McMahon T (1998) El Nino/Southern oscillation and Australian rainfall, streamflow and drought: links and potential for forecasting. J Hydrol 204:138–149

    Article  Google Scholar 

  • Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108

    Article  Google Scholar 

  • de Vos and Rientjes (2008) Multiobjective training of artificial neural networks for rainfall-runoff modelling. Water Resour Res 44

  • Demuth H, Beale B (2004) Neural network toolbox user guide. The Math Works Inc, Natick

    Google Scholar 

  • Dibike YB, Solomatine DP (2001) River flow forecasting using artificial neural networks. Phys Chem Earth Part B 26:1–7

    Article  Google Scholar 

  • Esha RI, Imteaz AM (2017) Seasonal streamflow prediction using large scale climate drivers for NSW region. In: Proceeding of the 22nd international congress on modelling and simulation, Hobart, Tasmania Australia, pp 1593–1599.

  • Field AP (2009) Discovering statistics using SPSS. SAGE publications Ltd, New York.

  • Ghiassi M, Zimbra DK, Saidane H (2008) Urban water demand forecasting with a dynamic artificial neural network model. J Water Resour Plan Manage 134:138–146

    Article  Google Scholar 

  • He X, Guan H, Zhang X, Simmons CT (2014) A wavelet-based multiple linear regression model for forecasting monthly rainfall. Int J Climatol 34:1898–1912

    Article  Google Scholar 

  • Hendon HH, Thompson DWJ, Wheeler MC (2007) Australian rainfall and surface temperature variations associated with the southern annular mode. J Clim 20:2452–2467

    Article  Google Scholar 

  • Hossain I, Rasel HM, Imteaz MA, Moniruzzaman M (2015) Statistical correlations between rainfall and climate indices in Western Australia. In: Proceeding of the 21st international congress on modelling and simulation, Gold Coast, Australia, pp 1991–1997

  • Hossain, I, Rasel, HM, Imteaz, MA, Mekanik F (2018) Long-term seasonal rainfall forecasting: efficiency of linear modelling technique. Environ Earth Sci 77:280 (1–10)

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

    Article  Google Scholar 

  • Hudson D, Alves O, Hendon HH, Wang G (2011) The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST. Clim Dyn 36:1155–1171

    Article  Google Scholar 

  • Ihara C, Kushnir Y, Cane MA, De La Pena VH (2007) Indian summer monsoon rainfall and its link with ENSO and Indian Ocean climate indices. Int J Climatol 27:179–187

    Article  Google Scholar 

  • Islam F, Imteaz MA, Rasel HM (2017) Analysing the effect of lagged climate indices on rainfall predictability for Western Australian North Coast Region. In: Proceeding of the 22nd international congress on modelling and simulation, Hobart, Tasmania Australia, pp 1600–1606

  • Karthikeyan L, Kumar DN, Graillot D, Gaur S (2013) Prediction of ground water levels in the uplands of a tropical coastal riparian wetland using artificial neural networks. Water Resour Manage 27:871–883

    Article  Google Scholar 

  • Khashei M, Hejazi SR, Bijari M (2008) A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets Syst 159:769–786

    Article  Google Scholar 

  • Kirono DGC, Chiew FHS, Kent DM (2010) Identification of best predictors for forecasting seasonal rainfall and runoff in Australia. Hydrol Process 24(10):1237–1247

    Google Scholar 

  • Latt ZZ, Wittenberg H (2014) Improving flood forecasting in a developing country: a comparative study of stepwise multiple linear regression and artificial neural network. Water Resour Manage 28:2109–2128

    Article  Google Scholar 

  • Lin FJ (2008) Solving multicollinearity in the process of fitting regression model using the nested estimate procedure. Qual Quant 42(3):417–426

    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 

  • Maqsood I, Khan MR, Abraham A (2004) An ensemble of neural networks for weather forecasting. Neural Comput Appl 13:112–122

    Article  Google Scholar 

  • Mekanik F, Imteaz MA, Gato-Trinidad S, Elmahdi A (2013) Multiple linear regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503:11–21

    Article  Google Scholar 

  • Meneghini B, Simmonds I, Smith IN (2007) Association between Australian rainfall and the southern annular mode. Int J Climatol 27:109–121

    Article  Google Scholar 

  • Meyers G, McIntosh P, Pigot L, Pook M (2007) The years of El Niño, La Niña, and interactions with the tropical Indian Ocean. J Clim 20:2872–2880

    Article  Google Scholar 

  • Peel MC, McMahon TA, Finlayson BL, Watson TA (2001) Identification and explanation of continental differences in the variability of annual runoff. J Hydrol 250:224–240

    Article  Google Scholar 

  • Ramirez MCV, Velho HFC, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. J Hydrol 301:146–162

    Article  Google Scholar 

  • Rasel, HM, Imteaz, MA, Mekanik F (2015) Evaluating the effects of lagged ENSO and SAM as potential predictors for long-term rainfall forecasting. In: Proceedings of the international conference on water resources and environment (WRE 2015), Beijing, China, 25–28 July, Taylor and Francis Group, London/Miklas Scholz (ed), pp 125–129

    Chapter  Google Scholar 

  • Risbey JS, Pook MJ, McIntosh PC, Wheeler MC, Hendon HH (2009) 2009, On the remote drivers of rainfall variability in Australia. Mon Weather Rev 137(10):3233–3253

    Article  Google Scholar 

  • Tarboton DG (2003) Rainfall-runoff processes. Utah State University, Logan

    Google Scholar 

  • Tokar AS, Markus M (2000) Precipitation-runoff modelling using artificial neural networks and conceptual models. J Hydrol Eng 5:156–161

    Article  Google Scholar 

  • Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239:132–147

    Article  Google Scholar 

  • Ummenhofer CC, Gupta A, Pook MJ, England MH (2008) Anomalous rainfall over southwest Western Australia forced by Indian Ocean Sea surface temperatures. J Clim 21(19):5113–5134

    Article  Google Scholar 

  • Verdon DC, Wyatt AM, Kiem AS, Franks SW (2004) Multidecadal variability of rainfall and streamflow: Eastern Australia. Water Resour Res 40(10):W10201

    Article  Google Scholar 

  • Vitart F (2004) Monthly forecasting at ECMWF. Mon Weather Rev 132:2761–2779

    Article  Google Scholar 

  • Wang G, Hendon HH (2007) Sensitivity of Australian rainfall to inter—El Niño variations. J Clim 20:4211–4226

    Article  Google Scholar 

  • Willmott CJ (1984) On the validation of models. Phys Geogr 2:184–194

    Article  Google Scholar 

  • Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil and Construction Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC, 3122, Australia

    Iqbal Hossain, Monzur Alam Imteaz & F. Mekanik

  2. Department of Civil Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204, Bangladesh

    H. M. Rasel

Authors
  1. Iqbal Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. M. Rasel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monzur Alam Imteaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. Mekanik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iqbal Hossain.

Additional information

Responsible Editor: E.-K. Jin.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hossain, I., Rasel, H.M., Imteaz, M.A. et al. Long-term seasonal rainfall forecasting using linear and non-linear modelling approaches: a case study for Western Australia. Meteorol Atmos Phys 132, 131–141 (2020). https://doi.org/10.1007/s00703-019-00679-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00703-019-00679-4

Search

Navigation