Runoff Projection under Climate Change Conditions with Data-Mining Methods
Publication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 8
Abstract
This work proposes data-mining algorithms for runoff projection under climate change conditions. Specifically, genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) data-mining tools are applied for runoff projection and their predictive skills are compared by means of several standard indicators of models’ performance. The approach herein implemented predicts future regional precipitation and temperature with the Hadley Centre Coupled Atmosphere-Ocean General Circulation Model version 3 (HadCM3) atmosphere-ocean general circulation model (AOGCM) followed by runoff prediction with GP, ANN, and SVM in the Aidoghmoush Basin, Iran. This paper’s results demonstrate that SVM outperforms GP and ANN by 7 and 5%, respectively.
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References
Aboutalebi, M., Bozorg-Haddad, O., and Loáiciga, H. A. (2015). “Optimal monthly reservoir operation rules for hydropower generation derived with SVR-NSGA II.” J. Water Resour. Plann. Manage., 04015029.
Aboutalebi, M., Bozorg-Haddad, O., and Loáiciga, H. A. (2016). “Simulation of methyl tertiary butyl ether concentration in river-reservoir systems using support vector regression.” J. Irrig. Drain. Eng., 04016015.
Ahmad, S., and Simonovic, S. (2001). “Developing runoff hydrograph using artificial neural networks.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
Anderson, M. L., Chen, Z., Kavvas, M., and Feldman, A. (2002). “Coupling HEC-HMS with atmospheric models for prediction of watershed runoff.” J. Hydrol. Eng., 312–318.
Andrade, M., Choi, C., Mondaca, M., Lansey, K., and Kang, D. (2013). “Enhancing artificial neural networks applied to the optimal design of water distribution systems.” World Environmental and Water Resources Congress, ASCE, Reston, VA.
Asefa, T., Kemblowski, M. W., Urroz, G., McKee, M., and Khalil, A. (2004). “Support vectors-based groundwater head observation networks design.” J. Water Resour. Res., 40(11), W11509.
Ashofteh, P. S., Bozorg-Haddad, O., Akbari-Alashti, H., and Mariño, M. A. (2014). “Determination of irrigation allocation policy under climate change by genetic programming.” J. Irrig. Drain. Eng., 04014059.
Ashofteh, P. S., Bozorg-Haddad, O., and Mariño, M. A. (2013). “Scenario assessment of streamflow simulation and its transition probability in future periods under climate change.” J. Water Res. Manage., 27(1), 255–274.
Bayram, S., and Al-Jibouri, S. (2016). “Efficacy of estimation methods in forecasting building projects’ costs.” J. Constr. Eng. Manage., 05016012.
Behzad, M., Asghari, K., Eazi, M., and Palhang, M. (2009). “Generalization performance of support vector machines and neural networks in runoff modeling.” Expert Syst. Appl., 36(4), 7624–7629.
Citakoglu, H. (2015). “Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation.” J. Comput. Electron. Agric., 118, 28–37.
Cramer, N. L. (1985). “A representation for the adaptive generation of simple sequential programs.” Proc., Int. Conf. on Genetic Algorithms and the Applications, John J. Grefenstette, ed., Carneige-Mellon Univ., Pittsburgh, 183–187.
Dye, P. J., and Croke, B. F. W. (2003). “Evaluation of streamflow predictions by the IHACRES rainfall-runoff model in two South African catchments.” J. Modell. Hydrol. Syst., 18(8–9), 705–712.
Fallah-Mehdipour, E., Bozorg-Haddad, O., and Mariño, M. A. (2013). “Extraction of optimal operation rules in an aquifer-dam system: Genetic programming approach.” J. Irrig. Drain. Eng., 872–879.
Fallah-Mehdipour, E., Bozorg-Haddad, O., and Mariño, M. A. (2014). “Genetic programming in groundwater modeling.” J. Hydrol. Eng., 04014031.
Fernandes, L., and Haie, N. (2001). “Neural networks in water resources management.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
Guyon, L., Weston, J., Barnhill, S., and Vapnik, V. (2002). “Gene selection for cancer classification using support vector machine.” J. Mach. Learn., 46(1), 389–422.
Hancock, K., Chung, C., and Mills, W. (2004). “Climate change and its effects on California water resources.” Proc., Water and Environmental Resources Management, ASCE, Reston, VA.
Hong, S., Jung, S., Kim, B., and Kim, H. (2013). “The impact assessment of climate change on the long-term runoff on the Han river in south Korea based on RCP climate change scenarios.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
Huang, W. (2001). “Neural network method in real-time forecasting of Apalachicola river flow.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
Ingol-Blanco, E., and McKinney, D. (2009). “Hydrologic modeling for assessing climate change impacts on the water resources of the Rio Conchos basin.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
Kalin, L., and Isik, S. (2010). “Prediction of water quality parameters using an artificial neural network model.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
Kashif Gill, M., Asefa, T., Kaheil, Y., and Mckee, M. (2007). “Effects of missing data on performance of learning algorithms for hydrologic predictions: Implications to an imputation technique.” J. Water Resour. Res., 43(7), W07416.
Khalil, A., McKee, M., Kemblowski, M., and Asefa, T. (2005). “Basin scale water management and forecasting using artificial neural networks.” J. Am. Water Res. Assoc., 41(1), 195–208.
Khan, M., and Coulibaly, P. (2006). “Application of support vector machine in lake water level prediction.” J. Hydrol. Eng., 199–205.
Maity, R., and Kumar, D. (2006). “Artificial neural network approach for streamflow forecasting in India using ENSO and EQUINOO.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
Makkeasorn, A., Chang, N. B., and Zhou, X. (2008). “Short-term streamflow forecasting with global climate change implications—A comparative study between genetic programming and neural network models.” J. Hydrol., 352(3–4), 336–354.
Marquardt, D. W. (1963). “An algorithm for least-squares estimation of nonlinear parameters.” J. Soc. Ind. Appl. Mathe., 11(2), 431–441.
MATLAB [Computer software]. MathWorks, Natick, MA.
Mimikou, M., Baltas, E., and Varanou, E. (2001). “Climate change impacts on water resources: Quantity and quality aspects.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
Nazif, S., Karamouz, M., and Zahmatkesh, Z. (2012). “Climate change impacts on runoff evaluation: A case study.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
Romero-Gomez, P., Austin, R. G., and Choi, C. Y. (2007). “Prediction of contaminants in water distribution systems using artificial neural networks.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
She, N., and Basketfield, D. (2005). “Long range forecast of streamflow using support vector machine.” Proc., World Environmental and Water Resources Congress, ASCE, Reston, VA.
Sidhu, R. S., Vatta, K., and Lall, U. (2011). “Climate change impact and management strategies for sustainable water-energy-agriculture outcomes in Punjab.” Indian J. Agric. Econ., 66(3), 328–339.
Su, J., Wang, X., Liang, Y., and Chen, B. (2014). “GA-based support vector machine model for prediction of monthly reservoir storage.” J. Hydrol. Eng., 1430–1437.
Tanagra 1.4 [Computer software]. Ricco Rakotomalala, France.
Tripathi, S., Srinivas, V. V., and Nanjundiah, R. S. (2006). “Downscaling of precipitation for climate change scenarios: A support vector machine approach.” J. Hydrol., 330(3–4), 621–640.
Vapnik, V. (1995). The nature of statistical learning theory, Springer, New York.
Wagesho, N., Jain, M., and Goel, N. (2013). “Effect of climate change on runoff generation: Application to Rift Valley Lakes basin of Ethiopia.” J. Hydrol. Eng., 1048–1063.
Whigham, P. A., and Crapper, P. E. (2001). “Modelling rainfall-runoff using genetic programming.” Math. Comput. Modell., 33(6–7), 707–721.
Xie, H., and Eheart, J. (2003). “Assessing vulnerability of water resources to climate change in Midwest.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
Zhang, J., et al. (2013). “Using hydrologic simulation to explore the impacts of climate change on runoff in the Huaihe River Basin of China.” J. Hydrol. Eng., 1393–1399.
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©2017 American Society of Civil Engineers.
History
Received: Oct 21, 2016
Accepted: Feb 27, 2017
Published online: May 20, 2017
Published in print: Aug 1, 2017
Discussion open until: Oct 20, 2017
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