Abstract
A neural network-based regionalization approach using catchment descriptors was proposed for flood management of ungauged catchments in a developing country with low density of the hydrometric network. Through the example of the Chindwin River basin in Myanmar, the study presents the application of principal components and clustering techniques for detecting hydrological homogeneous regions, and the artificial neural network (ANN) approach for regional index flood estimation. Based on catchment physiographic and climatic attributes, the principal component analysis yields three component solutions with 79.2 % cumulative variance. The Ward’s method was used to search initial cluster numbers prior to k-means clustering, which then objectively classifies the entire catchment into four homogeneous groups. For each homogeneous region clustered by the leading principal components, the regional index flood models are developed via the ANN and regression methods based on the longest flow path, basin elevation, basin slope, soil conservation curve number and mean annual rainfall. The ANN approach captures the nonlinear relationships between the index floods and the catchment descriptors for each cluster, showing its superiority towards the conventional regression method. The results would contribute to national water resources planning and management in Myanmar as well as in other similar regions.
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References
Abdolhay A, Saghafian B, Soom MAM, Ghazali AHB (2012) Identification of homogenous regions in Gorganrood basin (Iran) for the purpose of regionalization. Nat Hazards 61:1427–1442
Aziz K, Rahman A, Fang G, Shrestha S (2013) Application of artificial neural networks in regional flood frequency analysis: a case study for Australia. Stoch Env Res Risk A. doi:10.1007/s00477-013-0771-5
Berz G (2000) Flood disasters: lessons from the past – worries for the future. Proceeding of the ICE. Water Marit Eng 142(1):3–8
Besaw LE, Rizzo DM, Bierman PA, Hackett WR (2010) Advances in ungauged streamflow prediction using artificial neural networks. J Hydrol 386:27–37
Bocchiola D, Michele SD, Rosso R (2003) Review of recent advances in index flood estimation. Hydrol Earth Syst Sci 7(3):283–296
Cerny CA, Kaiser HF (1977) A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivar Behav Res 12(1):43–47
Chokmani K, Ouarda TBMJ (2004) Physiographical space-based kriging for regional flood frequency estimation at ungauged sites. Water Resour Res 40, W12514. doi:10.1029/2003WR002983
Cigizoglu HK (2003) Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrol Sci 48(3):349–361
Cunnane C (1988) Methods and merits of regional flood frequency analysis. J Hydrol 100:269–290
Cutore P, Cristaudo G, Campisano A et al (2007) Regional models for the estimation of streamflow series in ungauged basins. Water Resour Manag 21(5):789–800. doi:10.1007/s11269-006-9110-7
Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319:391–409
Dutta D, Herath S (2004) Trends of Floods in Asia and Flood Risk Management with Integrated River Basin Approach. Proceedings of the Second International Conference of Asian-Pacific Hydrology and Water Resources Association, Singapore. Vol.1: 55–63
Eng K, Milly PCD, Tasker GD (2007) Flood regionalization: a hybrid geographic and predictor-variable region-of-influence regression method. ASCE J Hydrol Eng 12:585–591
Gingras D, Adamowski K (1993) Homogeneous region delineation based on annual flood generation mechanisms. Hydrol Sci 38(2):103–121
Goyal MK, Gupta V (2014) Identification of homogeneous rainfall regimes in Northeast Region of India using fuzzy cluster analysis. Water Resour Manag. doi:10.1007/s11269-014-0699-7
GREHYS (1996) Presentation and review of some methods for regional flood frequency analysis. J Hydrol 186:63–84
Hosking JRM, Wallis JR (1997) Regional frequency analysis. Cambridge University Press, UK
IUSS Working Group WRB (2006) World reference base for soil resources 2006. World Soil Resources Reports No. 103. FAO, Rome
Jolliffe IT (2004) Principal component analysis, 2nd edn. Springer Series in Statistics, USA
Kirpich ZP (1940) Time of concentration of small agricultural watersheds. Civ Eng 10(6):362
Kohnová S, Szolgay J (2003) Regional estimation of the index flood and the standard deviation of the summer floods in the Tatry mountains. J Hydrol Hydromech 51(4):241–255
Latt ZZ, Wittenberg H (2014a) Improving flood forecasting in a developing country: a comparative study of stepwise multiple linear regression and artificial neural network. Water Resour Manag 28(8):2109–2128. doi:10.1007/s11269-014-0600-8
Latt ZZ, Wittenberg H (2014b) Hydrology and flood probability of the monsoon-dominated Chindwin River in northern Myanmar. J Water Clim Chang. doi:10.2166/wcc.2014.075
MacQueen J (1967) Some methods for classification and analysis of multivariate observations, proceedings of 5th Berkeley symposium on mathematical statistics and probability, vol 1. University of California Press, Berkeley, pp 281–297
Mishra BK, Takara K, Yamashiki Y, Tachikawa Y (2009) Estimation of index flood in hydrologic regions with limited flood data availability. Ann J Hydraul Eng JSCE 53:55–60
National Commission for Environmental Affairs (NCEA) (2009) Fourth national report to the United Nations Convention on biological diversity. Ministry of Forestry, Myanmar. http://www.cbd.int/doc/world/mm/mm-nr-04-en.pdf. Accessed 11 Jan 2014
Nayak PC, Satyajirao YR, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manag 20:77–90. doi:10.1007/s11269-006-4007-z
Pandey GR, Nguyen V-T-V (1999) A comparative study of regression based methods in regional flood frequency analysis. J Hydrol 225:92–101
Razavi T, Coulibaly P (2013) Classification of Ontario watersheds based on physical attributes and streamflow series. J Hydrol 493:81–94
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, PDP research group (eds) Parallel distributed processing, vol 1. MIT Press, Cambridge, pp 318–362
Sahay RR, Srivastava A (2014) Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network. Water Resour Manag 28:301–317. doi:10.1007/s11269-013-0446-5
Sattari NT, Apaydin H, Ozturk F (2012) Flow estimations for the Sohu stream using artificial neural networks. Environ Earth Sci 66(7):2031–2045
Shaban M, Urban B, El Saadi A, Faisal M (2010) Detection and mapping of water pollution variation in the Nile Delta using multivariate clustering and GIS techniques. J Environ Manag 91:1785–1793
Smith K, Ward R (1998) Floods. Physical process and human impacts. Wiley, England
Thomas DM, Benson MA (1970) Generalization of streamflow characteristics from drainage-basin characteristics. United States Geological Survey, Water-Supply Paper 1975
United States Department of Agriculture (USDA) (1986) Urban Hydrology for small watershed. Technical Release 55
World Meteorological Organization (2008) Guide to Hydrological Practices. Volume 1 Hydrology- From Measurements to Hydrological Information. WMO-No. 168. 6th ed. Geneva
Acknowledgments
The study was funded by the Deutscher Akademischer Austausch Dienst (DAAD) (German Academic Exchange Service). The Department of Meteorology and Hydrology, the Survey Department and the Land Use Department in Myanmar are gratefully acknowledged for providing data.
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Latt, Z.Z., Wittenberg, H. & Urban, B. Clustering Hydrological Homogeneous Regions and Neural Network Based Index Flood Estimation for Ungauged Catchments: an Example of the Chindwin River in Myanmar. Water Resour Manage 29, 913–928 (2015). https://doi.org/10.1007/s11269-014-0851-4
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DOI: https://doi.org/10.1007/s11269-014-0851-4