Skip to main content
Log in

Discretized and Continuous Target Fields for the Reservoir Release Rules During Floods

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

This paper employed two classical, popular decision-tree algorithms (C5.0 and CART), and traditional Regression to deal with reservoir operations regarding decision of the releases from a reservoir system during floods. The experiment site was in Shihmen Reservoir, located in northern Taiwan. In a typical single-peak typhoon, the rules derived include two operational stages, the stage before peakflow (Stage I) and the stage after peakflow (Stage II). This study collected 50 typhoons (1987–2009). Four cases are designed, that are discretized class labels (target fields) are run by C5.0 and CART (i.e., Cases 1 and 2, respectively), while numeric class labels are run by CART and Regression (i.e., Cases 3 and 4, respectively). The criteria of root mean square error (RMSE), coefficient of efficiency (CE), and relative error of peak discharge (EQp) were used to evaluate the forecasts. Results showed that the decision trees are skillful in the prediction of reservoir releases in the studied site. Furthermore, it was found that CART regression trees with numeric targets are more appropriate and precise than C5.0 classification trees and Regression for the prediction of releases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Apté C, Weiss S (1997) Data mining with decision trees and decision rules. Futur Gener Comput Syst 13:197–210

    Article  Google Scholar 

  • Bessler FT, Savic DA, Walters GA (2003) Water reservoir control with data mining. J Water Resour Plann Manag 129:26–34

    Article  Google Scholar 

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont

    Google Scholar 

  • Brodley CE, Utgoff PE (1995) Multivariate decision trees. Mach Learn 19:45–77

    Google Scholar 

  • Buntine W (1993) Learning classification trees. In: Hand DJ (ed) Artificial intelligence frontiers in statistics. Chapman & Hall, London, pp 182–201

    Google Scholar 

  • Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretizations of continuous features. In: Proceedings of the 12th International Conference on Machine Learning. New York, Morgan Kaufmann, pp 194–202

  • Hsu N-S, Wei C-C (2007) A multipurpose reservoir real-time operation model for flood control during typhoon invasion. J Hydrol 336:282–293

    Article  Google Scholar 

  • Hunt EB, Marin J, Stone PJ (1966) Experiments in induction. Academic, New York

    Google Scholar 

  • Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29:119–127

    Article  Google Scholar 

  • Kearns M, Mansour Y (1999) On the boosting ability of top-down decision tree learning algorithms. J Comput Syst Sci 58(1):109–128

    Article  Google Scholar 

  • Labadie JW (2004) Optimal operation of multireservoir systems: state-of-the-art review. J Water Resour Plann Manag 130(2):93–111

    Article  Google Scholar 

  • Lindo Systems Inc (2001) Lingo 7.0 user’s guide. Lindo Systems, Chicago

    Google Scholar 

  • Li X, Guo S, Liu P, Chen G (2010) Dynamic control of flood limited water level for reservoir operation by considering inflow uncertainty. J Hydrol 391:124–132

    Article  Google Scholar 

  • Liu P, Guo S, Xu X, Chen J (2011a) Derivation of aggregation-based joint operating rule curves for cascade hydropower reservoirs. Water Resour Manag 25(13):3177–3200

    Article  Google Scholar 

  • Liu X, Guo S, Liu P, Chen L, Li X (2011b) Deriving optimal refill rules for multi-purpose reservoir operation. Water Resour Manag 25(2):431–448

    Article  Google Scholar 

  • Lund JR, Ferreira I (1996) Operating rule optimization for Missouri river reservoir system. J Water Resour Plann Manag 122(4):287–295

    Article  Google Scholar 

  • Mahjoobi J, Etemad-Shahidi A (2008) An alternative approach for the prediction of significant wave heights based on classification and regression trees. Appl Ocean Res 30:172–177

    Article  Google Scholar 

  • Mahjoobi J, Etemad-Shahidi A, Kazeminezhad MH (2008) Hindcasting of wave parameters using different soft computing methods. Appl Ocean Res 30:28–36

    Article  Google Scholar 

  • Nagesh Kumar D, Baliarsingh F, Srinivasa Raju K (2010) Optimal reservoir operation for flood control using folded dynamic programming. Water Resour Manag 24(6):1045–1064

    Article  Google Scholar 

  • Needham JT, David WW Jr, Jay RL (2000) Linear programming for flood control in the Iowa and Des Moines Rivers. JJ Water Resour Plann Manag 126(3):118–127

    Article  Google Scholar 

  • Quinlan JR (1979) Discovering rules by induction from large collection of examples. In: Michie D (ed) Expert systems in the micro electronic age. Edinburg University Press, Edinburg

    Google Scholar 

  • Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo

    Google Scholar 

  • Quinlan JR (1998) C5.0: An Informal Tuorial. RuleQuest. www.rulequest.com/see5-unix.html.

  • Rani D, Moreira MM (2010) Simulation-optimization modeling: a survey and potential application in reservoir systems operation. Water Resour Manag 24(6):1107–1138

    Article  Google Scholar 

  • Rissanen J, Wax M (1998) Algorithm for constructing tree Structured classifier. US Patent No 4719571

  • Safavian SR, Landgrebe D (1998) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 22:660–674

    Google Scholar 

  • Sreerama KM (1998) Automatic construction of decision trees from data: a multidisciplinary survey. Data Min Knowl Disc 2:245–389

    Google Scholar 

  • Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Addison Wesley, Boston, pp 193–195

    Google Scholar 

  • Ticlavilca AM, McKee M (2011) Multivariate Bayesian regression approach to forecast releases from a system of multiple reservoirs. Water Resour Manag 25(2):523–543

    Article  Google Scholar 

  • Twala BETH, Jones MC, Hand DJ (2008) Good methods for coping with missing data in decision trees. Pattern Recognit Lett 29(7):950–956

    Article  Google Scholar 

  • Verplancke T, Van Looy S, Benoit D, Vansteelandt S, Depuydt P, De Turck F, Decruyenaere J (2008) Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Med Inform Decis Making 8(56):1–8

    Google Scholar 

  • Wasimi SA, Kitanidis PK (1983) Real-time forecasting and daily operation of a multireservoir system during floods by linear quadratic Gaussian control. Water Resour Res 19(6):1511–1522

    Article  Google Scholar 

  • Wei C-C, Hsu N-S (2008a) Multireservoir flood-control optimization with neural-based linear channel level routing under tidal effects. Water Resour Manag 22(11):1625–1647

    Article  Google Scholar 

  • Wei C-C, Hsu N-S (2008b) Multireservoir real-time operations for flood control using balanced water level index method. J Environ Manag 88(4):1624–1639

    Article  Google Scholar 

  • Wei C-C, Hsu N-S (2009) Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system. J Hydrol 365:213–224

    Article  Google Scholar 

  • Windsor JS (1973) Optimization model for the operation of flood control systems. Water Resour Res 9(5):1219–1226

    Article  Google Scholar 

  • WRA (2002) Guidelines of Shihmen Reservoir operations. Water Resources Agency, Taoyuan, in Chinese

    Google Scholar 

  • Wurbs RA (1993) Reservoir-system simulation and optimization models. J Water Resour Plann Manag 119(4):455–472

    Article  Google Scholar 

  • Xu ZX, Li JY (2002) Short-term inflow forecasting using an artificial neural network model. Hydrol Process 16(12):2423–2439

    Article  Google Scholar 

  • Yeh WW-G (1985) Reservoir management and operation models: a state-of-the-art review. Water Resour Res 21(12):1797–1818

    Article  Google Scholar 

  • Young GK Jr (1967) Finding reservoir operating rules. J Hydraul Div 93:297–321

    Google Scholar 

Download references

Acknowledgements

The support under Grant No. NSC100-2111-M-464-001 by the National Science Council, Taiwan is greatly appreciated. The writer is also grateful for the constructive comments of the referees.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chih-Chiang Wei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wei, CC. Discretized and Continuous Target Fields for the Reservoir Release Rules During Floods. Water Resour Manage 26, 3457–3477 (2012). https://doi.org/10.1007/s11269-012-0085-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-012-0085-2

Keywords

Navigation