Skip to main content
Log in

Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

This study proposes a two-stage intelligence-based pumping control (TWOPC) model for real-time pumping operation to solve the complex problem of estimating the desired pump flow and determining the optimal combination of pumps deployed in a flood event. In Stage I of the model, the desired pump flow was forecasted using the multilayer perceptron (MLP) with hydrological information including rainfall and basin runoffs, forebay water levels, and pump flows. In Stage II, the optimal pump combination was forecasted using tree-derived rules obtained from C4.5, classification and regression tree (CART), and chi-squared automatic interaction detection (CHAID) classifiers. The East Chung-Kong pumping station in New Taipei City was used as the study area. The pumping facilities included both submersible and upright axial pumps. The optimal input–output patterns, derived from a deterministic pumping operation optimization model, were used to train and validate the proposed TWOPC model. Data for this study were collected from three storms and four typhoons that affected an urban drainage basin. A total of 1,765 records were available. The results indicated that the case with a lag time of 5 min provided the most desirable pump flows in Stage I, and the C4.5 tree-based classifier performed well in Stage II. In addition, Typhoons Sinlaku (2) (2008/9/15) and Jangmi (2008/9/29) were selected for simulating the TWOPC model. The results demonstrated that the TWOPC model provided a more favorable performance than the traditional experienced method did. Overall, the proposed two-stage prediction model successfully addressed the problems of both determining the desired pump flow and optimal pump combination.

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

Similar content being viewed by others

References

  • Accadia C, Mariani S, Casaioli M, Lavagnini A, Speranza A (2003) Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Weather Forecas 18:918–932

    Article  Google Scholar 

  • Aggarwal SK, Goel A, Singh VP (2012) Stage and discharge forecasting by SVM and ANN techniques. Water Resour Manag 26(13):3705–3724

    Article  Google Scholar 

  • Andrade MG, Fragoso MD, Carneiro AAFM (2001) A stochastic approach to the flood control problem. Appl Math Model 499–511

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

    Article  Google Scholar 

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

    Google Scholar 

  • Chang FJ, Chang KY, Chang LC (2008) Counterpropagation fuzzy-neural network for city flood control system. J Hydrol 358:24–34

    Article  Google Scholar 

  • Chiang YM, Chang LC, Tsai MJ, Wang YF, Chang FJ (2011) Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks. Hydrol Earth Syst Sci 15:185–196

    Article  Google Scholar 

  • Coulibaly P (2010) Reservoir computing approach to Great Lakes water level forecasting. J Hydrol 381:76–88

    Article  Google Scholar 

  • Fallah-Mehdipour E, Haddad OB, Mariño MA (2012) Real-time operation of reservoir system by genetic programming. Water Resour Manag 26(14):4091–4103

    Article  Google Scholar 

  • Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533–549

    Article  Google Scholar 

  • Glover F, Laguna M (1997) Tabu search. Kluwer, Boston

    Book  Google Scholar 

  • Ho SH, Jee SE, Lee JE, Park JS (2004) Analysis on risk factors for cervical cancer using induction technique. Expert Syst Appl 27(1):97–105

    Article  Google Scholar 

  • Hsu NS, Wei CC (2007) A multipurpose reservoir real-time operation model for flood control during typhoon invasion. J Hydrol 336(3–4):282–293

    Article  Google Scholar 

  • Hsu MH, Chen SH, Chang TJ (2000) Inundation simulation for urban drainage basin with storm sewer system. J Hydrol 234(1–2):21–37

    Article  Google Scholar 

  • Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Appl Stat 29(2):119–127

    Article  Google Scholar 

  • Kişi O (2008) The potential of different ANN techniques in evapotranspiration modeling. Hydrol Process 22:2449–2460

    Article  Google Scholar 

  • Kleppin L, Pesch R, Schroder W (2008) CHAID Models on boundary conditions of metal accumulation in mosses collected in Germany in 1990, 1995 and 2000. Atmos Environ 42:5220–5231

    Article  Google Scholar 

  • Kougias IP, Theodossiou NP (2013) Multiobjective pump scheduling optimization using Harmony Search Algorithm (HSA) and polyphonic HAS. Water Resour Manag 27(5):1249–1261

    Article  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 

  • Mays LW, Tung YK (1992) Hydrosystems engineering and management. Water Resources, USA

    Google Scholar 

  • Melhem HG, Cheng Y, Kossler D, Scherschligt D (2003) Wrapper methods for inductive learning: example application to bridge decks. J Comput Civ Eng 17(1):46–57

    Article  Google Scholar 

  • Michael JA, Gordon SL (1997) Data mining technique: For marketing, sales and customer support. Wiley, New York

    Google Scholar 

  • Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manag 27(5):1301–1321

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ngo LL, Madsen H, Rosbjerg D (2007) Simulation and optimisation modelling approach for operation of the Hoa Binh reservoir, Vietnam. J Hydrol 336:269–281

    Article  Google Scholar 

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

    Google Scholar 

  • Rumelhart DE, McClelland JL, The PDP Research Group (1986) Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations. MIT Press, Cambridge

    Google Scholar 

  • Sajikumar N, Thandaveswara BS (1999) A non-linear rainfall-runoff model using an artificial neural network. J Hydrol 216:32–55

    Article  Google Scholar 

  • Schaefer JT (1990) The critical success index as an indicator of warning skill. Weather Forecas 5:570–575

    Article  Google Scholar 

  • Seckin N, Cobaner M, Yurtal R, Haktanir T (2013) Comparison of artificial neural network methods with L-moments for estimating flood flow at ungauged sites: the case of east Mediterranean River Basin, Turkey. Water Resour Manag 27(7):2103–2124

    Article  Google Scholar 

  • Shirmohammadi B, Vafakhah M, Moosavi V, Moghaddamnia A (2013) Application of several data-driven techniques for predicting groundwater level. Water Resour Manag 27(2):419–432

    Article  Google Scholar 

  • Shou KJ, Hong CY, Wu CC, Hsu HY, Fei LY, Lee JF, Wei CY (2011) Spatial and temporal analysis of landslides in Central Taiwan after 1999 Chi-Chi earthquake. Eng Geol 123(1–2):122–128

    Article  Google Scholar 

  • Srivastava PK, Han D, Ramirez MR, Islam T (2013) Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resour Manag 27(8):3127–3144

    Article  Google Scholar 

  • Ture M, Kurt I, Kurum AT, Ozdamar K (2005) Comparing classification techniques for predicting essential hypertension. Expert Syst Appls 29:583–588

    Article  Google Scholar 

  • Ture M, Tokatli F, Omurlu IK (2009) The comparisons of prognostic indexes using data mining techniques and Cox regression analysis in the breast cancer data. Expert Syst Appls 36:8247–8254

    Article  Google Scholar 

  • Valizadeh N, El-Shafie A (2013) Forecasting the level of reservoirs using multiple input fuzzification in ANFIS. Water Resour Manag 27(9):3319–3331

    Article  Google Scholar 

  • Wei CC (2012a) Discretized and continuous target fields for the reservoir release rules during floods. Water Resour Manag 26(12):3457–3477

    Article  Google Scholar 

  • Wei CC (2012b) RBF neural networks combined with principal component analysis applied to quantitative precipitation forecast for a reservoir watershed during typhoon periods. J Hydrometeorol 13(2):722–734

    Article  Google Scholar 

  • Wei CC, Hsu NS (2008) 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 CC, Hsu NS (2009) Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system. J Hydrol 365(3–4):213–224

    Article  Google Scholar 

  • Yagi S, Shiba S (1999) Application of genetic algorithms and fuzzy control to a combined sewer pumping station. Water Sci Technol 39(9):217–224

    Article  Google Scholar 

Download references

Acknowledgments

The support under Grant No. NSC102-2313-B-464-001 by the National Science Council of Taiwan is greatly appreciated. The writers are also grateful to the referees for their constructive comments.

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., Hsu, NS. & Huang, CL. Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins. Water Resour Manage 28, 425–444 (2014). https://doi.org/10.1007/s11269-013-0491-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-013-0491-0

Keywords

Navigation