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Flood estimation in ungauged catchments: application of artificial intelligence based methods for Eastern Australia

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Abstract

Regional flood frequency analysis (RFFA) is used to estimate design floods in ungauged and data poor gauged catchments, which involves the transfer of flood characteristics from gauged to ungauged catchments. In Australia, RFFA methods have focused on the application of empirical methods based on linear forms of traditional models such as the probabilistic rational method, the index flood method and the quantile regression technique (QRT). In contrast to these traditional linear-models, non-linear methods such as artificial neural networks (ANNs) and gene expression programming (GEP) can be applied to RFFA problems. The particular advantage of these techniques is that they do not impose a model structure on the data, and they can better deal with non-linearity of the input and output relationship in regional flood modelling. These non-linear techniques have been applied successfully in a wide range of hydrological problems; however, there have been only limited applications of these techniques in RFFA problems, particularly in Australia. This paper focuses on the development and testing of the ANNs and GEP based RFFA models for eastern parts of Australia. This involves relating flood quantiles to catchment characteristics so that the developed prediction models can be used to estimate design floods in ungauged site. A data set comprising of 452 stations from eastern Australia was used to develop the new RFFA models. An independent testing shows that the non-linear methods are quite successful in RFFA and can be used as an alternative method to the more traditional approaches currently used in eastern Australia. The results based on ANN and GEP-based RFFA techniques have been found to outperform the ordinary least squares based QRT (linear technique).

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References

  • Abrahart RJ, Kneale PE, See L (eds) (2004) Neural networks for hydrological modelling. Taylor & Francis, London

    Google Scholar 

  • Abrahart RJ, Heppenstall AJ, See LM (2007) Timing error correction procedure applied to neural network rainfall-runoff modelling. Hydrol Sci J 52(3):414–431

    Article  Google Scholar 

  • ASCE, Task Committee (2000) Artificial neural networks in hydrology-I: preliminary concepts. J Hydrol Eng, ASCE 5(2):115–123

    Article  Google Scholar 

  • Azamathulla HM, Ghani AA (2011) Genetic programming for longitudinal dispersion coefficients in streams. Water Resour Manag 25(6):1537–1544

    Article  Google Scholar 

  • Azamathulla HM, Ghani AA, Leow CS, Chang CK, Zakaria NA (2011) Gene-expression programming for the development of a stage-discharge curve of the Pahang River. Water Resour Manag 25(11):2901–2916

    Article  Google Scholar 

  • Aziz K, Rahman A, Fang G, Shreshtha S (2014) Application of artificial neural networks in regional flood frequency analysis: a case study for Australia. Stoch Environ Res Risk Assess 28(3):541–554

    Article  Google Scholar 

  • Aziz K, Rai S, Rahman A (2015) Design flood estimation in ungauged catchments using genetic algorithm based artificial neural network (GAANN) technique for Australia. Nat Hazards 77(2):805–821

    Article  Google Scholar 

  • Bates BC, Rahman A, Mein RG, Weinmann PE (1998) Climatic and physical factors that influence the homogeneity of regional floods in south-eastern Australia. Water Resour Res 34(12):3369–3382

    Article  Google Scholar 

  • Bayazit M, Onoz B (2004) Sampling variances of regional flood quantiles affected by inter-site correlation. J Hydrol 291:42–51

    Article  Google Scholar 

  • Benson MA (1962) Evolution of methods for evaluating the occurrence of floods. U.S. Geological Surveying Water Supply Paper, 1580-A

  • Besaw L, Rizzo DM, Bierman PR, Hackett WR (2010) Advances in ungauged streamflow prediction using artificial neural networks. J Hydrol 386(1–4):27–37

    Article  Google Scholar 

  • Chokmani K, Ouarda BMJT, Hamilton S, Ghedira MH, Gingras H (2008) Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques. J Hydrol 349:83–396

    Article  Google Scholar 

  • Dalrymple T (1960) Flood frequency analyses. U.S. Geological Survey water supply paper 1543-A, 11–51

  • Daniell TM (1991) Neural networks—applications in hydrology and water resources engineering. In: International hydrology & water resources symposium. Perth, Australia, 2–4 Oct 1991

  • Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25(1):80–108

    Article  Google Scholar 

  • Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319:391–409

    Article  Google Scholar 

  • Demirel MC, Venancio A, Kahya E (2009) Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv Eng Softw 40(7):467–473

    Article  Google Scholar 

  • Farmer JD, Sidorowich J (1987) Predicting chaotic time series. Phys Rev Lett 59(8):845–848

    Article  CAS  Google Scholar 

  • Fernando DA K, Shamseldin AY, Abrahart RJ (2009) Using gene expression programming to develop a combined runoff estimate model from conventional rainfall-runoff model outputs. In: 18th World IMACS/MODSIM Congress, Cairns, Australia 13–17 July 2009

  • Ferreira C (2001a) Gene expression programming in problem solving. In: 6th Online world conference on soft computing in industrial applications (invited tutorial)

  • Ferreira C (2001b) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    Google Scholar 

  • Ferreira C (2006) Gene-expression programming; mathematical modeling by an artificial intelligence. Springer, Berlin

    Google Scholar 

  • Gao C, Gemmer M, Zeng X, Liu B, Su B, Wen Y (2010) Projected streamflow in the Huaihe River Basin (2010-2100) using artificial neural network. Stoch Environ Res Risk Assess 24:685–697

    Article  Google Scholar 

  • Giustolisi O (2004) Using genetic programming to determine Chèzy resistance coefficient in corrugated channels. J Hydroinformatics 6(3):157–173

    Google Scholar 

  • Govindaraju RS (2000) Artificial neural networks in hydrology II. Hydrological applications. J Hydrol Eng 5(2):124–137

    Article  Google Scholar 

  • Griffis VW, Stedinger JR (2007) The use of GLS regression in regional hydrologic analyses. J Hydrol 344:82–95

    Article  Google Scholar 

  • Grubbs FE, Beck G (1972) Extension of sample sizes and percentage points for significance tests of outlying observations. Technometrics 14:847–854

    Article  Google Scholar 

  • Guven A (2009) Linear genetic programming for time-series modeling of daily flow rate. J Earth Syst Sci 118(2):137–146

    Article  Google Scholar 

  • Guven A, Gunal M (2008) A genetic programming approach for prediction of local scour downstream hydraulic structures. J Irrig Drain Eng 132(4):241–249

    Article  Google Scholar 

  • Guven A, Kisi O (2011) Estimation of suspended sediment yield in natural rivers using machine-coded linear genetic programming. Water Resour Manag 25(2):691–704

    Article  Google Scholar 

  • Guven A, Talu NE (2010) Gene-expression programming for estimating suspended sediment in middle euphrates basin, Turkey. Clean Soil Air Water 38(12):1159–1168

    Article  CAS  Google Scholar 

  • Guven A, Avtek A, Yuce MI, Aksoy H (2008) Genetic programming based empirical model for daily reference evapotranspiration estimation. CLEAN Soil Air Water 36(10–11):905–912

    Article  CAS  Google Scholar 

  • Hackelbusch A, Micevski T, Kuczera G, Rahman A, Haddad K (2009) Regional flood frequency analysis for Eastern New South Wales: a region of influence approach using generalized least squares based parameter regression. In: Proceedings 31st hydrology and water resources symposium, Newcastle

  • Haddad K, Rahman A (2011) Regional flood estimation in New South Wales Australia using generalised least squares quantile regression. J Hydrol Eng ASCE 16(11):920–925

    Article  Google Scholar 

  • Haddad K, Rahman A (2012) Regional flood frequency analysis in eastern Australia: bayesian GLS regression-based methods within fixed region and ROI framework: quantile regression vs. parameter regression technique. J Hydrol 20:142–161

    Article  Google Scholar 

  • Haddad K, Rahman A, Weinmann PE, Kuczera G, Ball JE (2010) Streamflow data preparation for regional flood frequency analysis: lessons from south-east Australia. Aust J Water Resour 14(1):17–32

    Google Scholar 

  • Haddad K, Rahman A, Stedinger JR (2012) Regional flood frequency analysis using bayesian generalized least squares: a comparison between quantile and parameter regression techniques. Hydrol Process 26:1008–1021

    Article  Google Scholar 

  • Haddad K, Rahman A, Ling F (2015) Regional flood frequency analysis method for Tasmania, Australia: a case study on the comparison of fixed region and region-of-influence approaches. Hydrol Sci J 60(12):2086–2101

    Article  Google Scholar 

  • Hosking JRM, Wallis JR (1997) Regional frequency analysis—an approach based on L-moments. Cambridge University Press, New York

    Book  Google Scholar 

  • Huo Z, Feng S, Kang S, Huang G, Wang F, Guo P (2012) Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China. J Hydrol 420–421:159–170

    Article  Google Scholar 

  • Institution of Engineers Australia (I.E. Aust.) (1987, 2001). In: Pilgrim DH (ed), Australian Rainfall and Runoff: a guide to flood estimation, vol 1, I. E. Aust., Canberra

  • Ishak E, Haddad K, Zaman M, Rahman A (2011) Scaling property of regional floods in New South Wales Australia. Nat Hazards 58:1155–1167. doi:10.1007/s11069-011-9719-6

    Article  Google Scholar 

  • Kendall MG (1970) Rank correlation methods, 2nd edn. Hafner, New York

    Google Scholar 

  • Kisi O, Shiri J (2011) Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour Manag 25(13):3135–3152

    Article  Google Scholar 

  • Kjeldsen TR, Jones D (2009) An exploratory analysis of error components in hydrological regression modelling. Water Resour Res 45:W02407. doi:10.1029/2007WR006283

    Article  Google Scholar 

  • Kothyari UC (2004) Estimation of mean annual flood from ungauged catchments using artificial neural networks. In: Hydrology: science and practice for the 21st century, vol 1, British Hydrological Society

  • Kuczera G (1999) Comprehensive at-site flood frequency analysis using Monte Carlo Bayesian inference. Water Resour Res 35(5):1551–1557

    Article  Google Scholar 

  • Kuichling E (1889) The relation between the rainfall and the discharge of sewers in populous districts. Trans Am Soc Civ Eng 20:1–56

    Google Scholar 

  • Luk KC, Ball JE, Sharma A (2001) An application of artificial neural networks for rainfall forecasting. Math Comput Modell 33:683–693

    Article  Google Scholar 

  • Madsen H, Pearson CP, Rosbjerg D (1997) Comparison of annual maximum series and partial duration series for modeling extreme hydrological events—2. Regional modeling. Water Resour Res 33(4):771–781

    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(1):101–123

    Article  Google Scholar 

  • McCulloch WS, Pitts W (1943) A logic calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  Google Scholar 

  • Micevski T, Hackelbusch A, Haddad K, Kuczera G, Rahman A (2015) Regionalisation of the parameters of the log-Pearson 3 distribution: a case study for New South Wales, Australia. Hydrol Process 29(2):250–260

    Article  Google Scholar 

  • Muttiah RS, Srinivasan R, Allen PM (1997) Prediction of two year peak stream discharges using neural networks. J Am Water Resour Assoc 33(3):625–630

    Article  Google Scholar 

  • Najafi MR, Moradkhani H (2013) Analysis of runoff extremes using spatial hierarchical Bayesian modeling. Water Resour Res 49(10):6656–6670

    Article  Google Scholar 

  • Najafi MR, Moradkhani H (2014) A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes. Hydrol Process 28(26):6292–6308

    Article  Google Scholar 

  • Ouarda TBMJ, Bâ KM, Diaz-Delgado C, Cârsteanu C, Chokmani K, Gingras H, Quentin E, Trujillo E, Bobée B (2008) Intercomparison of regional flood frequency estimation methods at ungauged sites for a Mexican case study. J Hydrol 348:40–58

    Article  Google Scholar 

  • Pandey GR, Nguyen VTV (1999) A comparative study of regression based methods in regional flood frequency analysis. J Hydrol 225:92–101

    Article  Google Scholar 

  • Pilgrim DH, McDermott GE (1982) Design floods for small rural catchments in eastern New South Wales. Civil Eng Trans Inst Engrs Aust CE24:226–234

    Google Scholar 

  • Pirozzi J, Ashraf M, Rahman A, Haddad K (2009) Design flood estimation for ungauged catchments in Eastern NSW: evaluation of the probabilistic rational method. In: Proceedings of 31st hydrology and water resources symposium, Newcastle

  • Rabunal JR, Puertas J, Suarez J, Rivero D (2007) Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol Process 27(4):476–485

    Article  Google Scholar 

  • Rahman A (2005) A quantile regression technique to estimate design floods for ungauged catchments in South-East Australia. Aust J Water Resour 9(1):81–89

    Google Scholar 

  • Rahman A, Hollerbach D (2003) Study of Runoff Coefficients Associated with the probabilistic rational method for flood estimation in South-East Australia. In: Proceedings of 28th international hydrology and water resources symposium, I. E. Aust., vol 1, pp 199–203, Wollongong, 10–13 Nov, 2003

  • Rahman A, Bates BC, Mein RG, Weinmann PE (1999) Regional flood frequency analysis for ungauged basins in south–eastern Australia. Aust J Water Resour 3(2):199–207

    Google Scholar 

  • Rahman A, Haddad K, Caballero W, Weinmann PE (2008) Progress on the enhancement of the probabilistic rational method for Victoria in Australia. In: 31st hydrology and water resources symposium, pp 940–951, Adelaide, 15–17 April 2008

  • Rahman A, Haddad K, Zaman M, Kuczera G, Weinmann PE (2011) Design flood estimation in ungauged catchments: a comparison between the probabilistic rational method and quantile regression technique for NSW. Aust J Water Resour 14(2):127–137

    Google Scholar 

  • Rahman A, Haddad K, Zaman M, Ishak E, Kuczera G, Weinmann PE (2012) Australian Rainfall and Runoff Revision Projects, Project 5 Regional flood methods, Stage 2 Report No. P5/S2/015, Engineers Australia, Water Engineering

  • Rahman A, Haddad K, Kuczera G, Weinmann PE (2015a) Regional flood methods. In: Ball JE (ed), Australian Rainfall & Runoff, Chapter 3, Book 3, Engineers Australia, pp 78–114

  • Rahman A, Haddad K, Haque M, Kuczera G, Weinmann PE (2015b) Australian Rainfall and Runoff Project 5: Regional flood methods: Stage 3 Report, Technical Report, No. P5/S3/025, Engineers Australia, Canberra

  • Seckin N, Guven A (2012) Estimation of peak flood discharges at ungauged sites across Turkey. Water Resour Manag 26:2569–2581

    Article  Google Scholar 

  • Selle B, Muttil N (2011) Testing the structure of hydrological models using genetic programming. J Hydrol 397(1–2):1–9

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Shoaib M, Shamseldin AY, Melville BW, Khan MM (2016) A comparison between wavelet based static and dynamic neural network approaches for runoff prediction. J Hydrol 535:211–225

    Article  Google Scholar 

  • Shu C, Ouarda TBMJ (2007) Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. Water Resour Res 43:W07438. doi:10.1029/2006WR005142

    Article  Google Scholar 

  • Stedinger JR, Tasker GD (1985) Regional hydrologic analysis—1. Ordinary, weighted and generalized least squares compared. Water Resour Res 21:1421–1432

    Article  Google Scholar 

  • Tasker GD (1980) Hydrologic regression with weighted least squares. Water Resour Res 16(6):1107–1113

    Article  Google Scholar 

  • Tasker GD, Eychaner JH, Stedinger JR (1986) Application of generalised least squares in regional hydrologic regression analysis. US Geological Survey Water Supply Paper 2310, pp 107–115

  • Thomas DM, Benson MA (1970) Generalization of streamflow characteristics from drainage-basin characteristics, U.S. Geological Survey Water Supply Paper 1975, US Governmental Printing Office

  • Turan ME, Yurdusev MA (2009) River flow estimation from upstream flow records by artificial intelligence methods. J Hydrol 369:71–77

    Article  Google Scholar 

  • Vogel RM, McMahon TA, Chiew FHS (1993) Flood flow frequency model selection in Australia. J Hydrol 146:421–449

    Article  Google Scholar 

  • Weeks WD (1991) Design floods for small rural catchments in Queensland. Civil Eng Trans IEAust CE33(4):249–260

    Google Scholar 

  • Wu J, Li N, Yang H, Li C (2008) Risk evaluation of heavy snow disasters using BP artificial neural network: the case of Xilingol in Inner Mongolia. Stoch Environ Res Risk Assess 22:719–725

    Article  Google Scholar 

  • Yan H, Moradkhani H (2015) A regional Bayesian hierarchical model for flood frequency analysis. Stoch Env Res Risk Assess 29(3):1019–1036

    Article  Google Scholar 

  • Yan H, Moradkhani H (2016) Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling. Nat Hazards 81(1):203–205

    Article  Google Scholar 

  • Zhang B, Govindaraju RS (2003) Geomorphology-based artificial neural networks for estimation of direct runoff over watersheds. J Hydrol 273(1):18–34

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing, Engineering and Mathematics, Western Sydney University, Kingswood, NSW, Australia

    K. Aziz & A. Rahman

  2. Institute for Infrastructure Engineering, Western Sydney University, Kingswood, NSW, Australia

    M. M. Haque & A. Rahman

  3. Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand

    A. Y. Shamseldin & M. Shoaib

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  1. K. Aziz
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  5. M. Shoaib
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Correspondence to A. Rahman.

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Aziz, K., Haque, M.M., Rahman, A. et al. Flood estimation in ungauged catchments: application of artificial intelligence based methods for Eastern Australia. Stoch Environ Res Risk Assess 31, 1499–1514 (2017). https://doi.org/10.1007/s00477-016-1272-0

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