Abstract
Metro Water District (MWD) is an agency that administers water distribution in a large geographic region. It targets for existing conditions with future projections of water resources for conservation, supply, and usage. Hence, it is required to show proper water resources management for MWD. Where the river basin profiles are projected to provide the water resources management with potential issues for MWD. Here, Upper Chattahoochee River (UCR) basin of the Metropolitan North Georgia Water Planning District (MNGWPD) selected for the study area. UCR is one of the largest river basins in the MNGWPD and it provides drinking and primary receiving water for nearly 3.5 million people of Atlanta Metro Region. In this study, Parallel Computing of Emulator based Spatial Optimization (PCESO) framework developed for spatial optimization of large complex watersheds. The proposed framework optimizes the hydrological model by parallel computing, emulator fit, sampling design, and spatial optimization. The results showed that 1) the computational time required for spatial optimization was significantly reduced by 50%, 2) goodness-of-fit reached its threshold limit in all stations inclusive in reservoir containing stations, 3) the water balance components and the optimized parameter values with sensitivity index provided the physical phenomena of the study area and showed the approximate hydrological processes in MWD. Further, this proposed work incorporates into future climate data can provide an accurate hydrological analysis with water allocation issues like water use, demand, conservation, and supply for MWD and it helps to identify the water-related disasters floods and droughts.
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References
Abbaspour KC, Rouholahnejad E, Vaghefi S, Srinivasan R, Yang H, Kløve B (2015) A continental-scale hydrology and water quality model for Europe : calibration and uncertainty of a high-resolution large-scale SWAT model. J Hydrol 524:733–752. https://doi.org/10.1016/j.jhydrol.2015.03.027
Black V (2017) Water resources management plan. Metropolitan North Georgia Water Planning District, Georgia, USA.
Budamala V, Mahindrakar AB (2019) Enhance the prediction of complex hydrological models by pseudo-simulators. Geocarto Int 0:1–17. https://doi.org/10.1080/10106049.2019.1629646
Cibin R, Chaubey I (2015) A computationally efficient approach for watershed scale spatial optimization. Environ Model Softw 66:1–11. https://doi.org/10.1016/j.envsoft.2014.12.014
Femeena PV, Sudheer KP, Cibin R, Chaubey I (2018) Spatial optimization of cropping pattern for sustainable food and biofuel production with minimal downstream pollution. J Environ Manag 212:198–209. https://doi.org/10.1016/j.jenvman.2018.01.060
Forrester AIJ, Keane AJ (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45:50–79. https://doi.org/10.1016/j.paerosci.2008.11.001
Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria : implications for improving hydrological modelling. J Hydrol 377:80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
Haftka RT, Villanueva D, Chaudhuri A (2016) Parallel surrogate-assisted global optimization with expensive functions – a survey. Struct Multidiscip Optim 54:3–13. https://doi.org/10.1007/s00158-016-1432-3
Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for Sytematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part-1 - a discussion of principles. J Hydrol 10:282–290
Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2009) Soil and Water Assessment Tool User’s Manual: Version 2005. Texas Water Resources Institute, TR-192, College Station, Texas.
Pecci F, Abraham E, Stoianov I (2019) Model reduction and outer approximation for optimising the placement of control valves in complex water networks. J Water Resour Plan Manag 145:1–13. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001055
Queipo NV, Haftka RT, Shyy W, Goel T, Vaidyanathan R, Kevin Tucker P (2005) Surrogate-based analysis and optimization. Prog Aerosp Sci 41:1–28. https://doi.org/10.1016/j.paerosci.2005.02.001
Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48:1–32. https://doi.org/10.1029/2011WR011527
Rehbach F, Zaefferer M, Stork J, Bartz-Beielstein T (2018) Comparison of parallel surrogate-assisted optimization approaches. In: GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference. pp 1348–1355. https://doi.org/10.1145/3205455.3205587
Rouholahnejad E, Abbaspour KC, Vejdani M, Srinivasan R, Schulin R, Lehmann A (2012) A parallelization framework for calibration of hydrological models. Environ Model Softw 31:28–36. https://doi.org/10.1016/j.envsoft.2011.12.001
Song J, Yang Y, Wu J et al (2018) Adaptive surrogate model based multiobjective optimization for coastal aquifer managemen. J Hydrol 561:98-111t
Viana FAC, Haftka RT, Steffen V (2009) Multiple surrogates: how cross-validation errors can help us to obtain the best predictor. Struct Multidiscip Optim 39:439–457. https://doi.org/10.1007/s00158-008-0338-0
Wang C, Duan Q, Gong W, Ye A, di Z, Miao C (2014) An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ Model Softw 60:167–179. https://doi.org/10.1016/j.envsoft.2014.05.026
Yen H, Wang X, Fontane DG, Harmel RD, Arabi M (2014) Environmental Modelling & Software A framework for propagation of uncertainty contributed by parameterization , input data , model structure , and calibration / validation data in watershed modeling. Environ Model Softw 54:211–221. https://doi.org/10.1016/j.envsoft.2014.01.004
Zhang X, Srinivasan R, Van Liew M (2009) Approximating swat model using artificial neural network and support vector machine. J Am Water Resour Assoc 45:460–474. https://doi.org/10.1111/j.1752-1688.2009.00302.x
Zhang J, Chowdhury S, Messac A (2012) An adaptive hybrid surrogate model. Struct Multidiscip Optim 46:223–238. https://doi.org/10.1007/s00158-012-0764-x
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Budamala, V., Mahindrakar, A.B. Approximation of Metro Water District Basin Using Parallel Computing of Emulator Based Spatial Optimization (PCESO). Water Resour Manage 34, 121–137 (2020). https://doi.org/10.1007/s11269-019-02424-3
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DOI: https://doi.org/10.1007/s11269-019-02424-3