Advertisement

An Application of Data Mining Techniques for Flood Forecasting: Application in Rivers Daya and Bhargavi, India

  • Binay Kumar Panigrahi
  • Soumya Das
  • Tushar Kumar Nath
  • Manas Ranjan Senapati
Original Contribution

Abstract

In the present study, with a view to speculate the water flow of two rivers in eastern India namely river Daya and river Bhargavi, the focus was on developing Cascaded Functional Link Artificial Neural Network (C-FLANN) model. Parameters of C-FLANN architecture were updated using Harmony Search (HS) and Differential Evolution (DE). As the numbers of samples are very low, there is a risk of over fitting. To avoid this Map reduce based ANOVA technique is used to select important features. These features were used and provided to the architecture which is used to predict the water flow in both the rivers, one day, one week and two weeks ahead. The results of both the techniques were compared with Radial Basis Functional Neural Network (RBFNN) and Multilayer Perceptron (MLP), two widely used artificial neural network for prediction. From the result it was confirmed that C-FLANN trained through HS gives better prediction result than being trained through DE or RBFNN or MLP and can be used for predicting water flow in different rivers.

Keywords

Artificial Neural Network (ANN) Harmony Search Differential Evolution Cascaded FLANN Radial Basis Functional Neural Network (RBFNN) Map reduce Multi-layer Perceptron 

References

  1. 1.
    A. Brath, P. Burlando, R. Rosso, Sensitivity analysis of real time flood forecasting to on-line rainfall predictions, in Selected Papers from the Workshop on Natural Disasters in European-Mediterranean Countries, Perugia, Italy, pp. 469–488, 1988Google Scholar
  2. 2.
    M.N. French, W.F. Krajewski, R.R. Cuykendall, Rainfall forecasting in space and time using a neural network. J. Hydrol. 137(1–4), 1–31 (1992).  https://doi.org/10.1016/0022-1694(92)90046-X CrossRefGoogle Scholar
  3. 3.
    M. Shaka, S.O. Dulo, S. Wycliffe, K.K. Joseph, I. Timothy, K. James, K. Paul, O. Patrick, G. Simon, K. Victor, O. Roseln, T. Deksios, Flood and drought forecasting and early warning program (for the Nile Basin). University of Nairobi, Digital Repository, 2005Google Scholar
  4. 4.
    S.H. Elsafi, Artificial neural networks (ANNs) for flood forecasting at Dongola Station in the river Nile, Sudan. Alex. Eng. J. 53(3), 655–662 (2014).  https://doi.org/10.1016/j.aej.2014.06.010 CrossRefGoogle Scholar
  5. 5.
    D.L. Fread, Flood routing: a synopsis of past, present, and future capability, in Rainfall-Runoff Relationship. Proceedings of the International Symposium on Rainfall-Runoff Modelling, ed. by V.P. Singh. Mississippi State University, USA, pp. 521–542, 1981Google Scholar
  6. 6.
    D.N. Kumar, F. Baliarsingh, K.S. Raju, Extended Muskingum method for flood routing. J. Hydro-Environ. Res. 5(2), 127–135 (2011).  https://doi.org/10.1016/j.jher.2010.08.003 CrossRefGoogle Scholar
  7. 7.
    C.T. Cheng, K.W. Chau, Fuzzy iteration methodology for reservoir flood control operation. J. Am. Water Resour. Assoc. 37(5), 1381–1388 (2001).  https://doi.org/10.1111/j.1752-1688.2001.tb03646.x CrossRefGoogle Scholar
  8. 8.
    C.T. Cheng, K.W. Chau, Three-person multi-objective conflict decision in reservoir flood control. Eur. J. Oper. Res. 142(3), 625–631 (2002).  https://doi.org/10.1016/S0377-2217(01)00319-8 CrossRefzbMATHGoogle Scholar
  9. 9.
    K.W. Chau, C.L. Wu, Y.S. Li, Comparison of several flood forecasting models in Yangtze river. J. Hydrol. Eng. ASCE 10(6), 485–491 (2005).  https://doi.org/10.1061/(ASCE)1084-0699(2005)10:6(485) MathSciNetCrossRefGoogle Scholar
  10. 10.
    C.T. Cheng, C.P. Ou, K.W. Chau, Combining a fuzzy optimal model with a genetic algorithm to solve multi objective rainfall-runoff model calibration. J. Hydrol. 268(1–4), 72–86 (2002).  https://doi.org/10.1016/S0022-1694(02)00122-1 CrossRefGoogle Scholar
  11. 11.
    C.T. Cheng, W.C. Wang, D.M. Xu, K.W. Chau, Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resour. Manage 22(7), 895–909 (2008).  https://doi.org/10.1007/s11269-007-9200-1 CrossRefGoogle Scholar
  12. 12.
    V.T. Chow, D.R. Maidment, L.W. Mays, Applied Hydrology (McGraw-Hill Book Co., Singapore, 1988)Google Scholar
  13. 13.
    V.P. Singh, R.C. McCann, Some notes on Muskingum method of flood routing. J. Hydrol. 48(3–4), 343–361 (1980).  https://doi.org/10.1016/0022-1694(80)90125-0 CrossRefGoogle Scholar
  14. 14.
    M.A. Gill, Flood routing by Muskingum method. J. Hydrol. 36(3–4), 353–363 (1978).  https://doi.org/10.1016/0022-1694(78)90153-1 CrossRefGoogle Scholar
  15. 15.
    D. Stephenson, Direct optimization of Muskingum routing coefficients. J. Hydrol. 41(1–2), 161–165 (1979).  https://doi.org/10.1016/0022-1694(79)90115-X CrossRefGoogle Scholar
  16. 16.
    T. O’Donnell, A direct three-parameter Muskingum procedure incorporating lateral inflow. J. Hydraul. Eng. ASCE 30(4), 479–496 (1985).  https://doi.org/10.1080/02626668509491013 Google Scholar
  17. 17.
    Y.-K. Tung, River flood routing by nonlinear Muskingum method. J. Hydraul. Eng. ASCE 111(12), 1447–1460 (1985).  https://doi.org/10.1061/(ASCE)0733-9429(1985)111:12(1447) CrossRefGoogle Scholar
  18. 18.
    S. Mohan, Parameter estimation of non-linear Muskingum models using genetic algorithm. J. Hydraul. Eng. ASCE 123(2), 137–142 (1997).  https://doi.org/10.1061/(ASCE)0733-9429(1997)123:2(137) CrossRefGoogle Scholar
  19. 19.
    P. Choudhury, Multiple inflows Muskingum routing model. J. Hydrol. Eng. ASCE 12(5), 473–481 (2007).  https://doi.org/10.1061/(ASCE)1084-0699(2007)12:5(473) CrossRefGoogle Scholar
  20. 20.
    A. Das, Parameter estimation for Muskingum models. J. Irrig. Drain. Eng. ASCE 130(2), 140–147 (2004).  https://doi.org/10.1061/(asce)0733-9437(2004)130:2(140) CrossRefGoogle Scholar
  21. 21.
    A. Das, Chance-constrained optimization-based parameter estimation for Muskingum models. J. Irrig. Drain. Eng. ASCE 133(2), 487–494 (2007).  https://doi.org/10.1061/(ASCE)0733-9437(2004)130:2(140) CrossRefGoogle Scholar
  22. 22.
    R. Majhi, G. Panda, G. Sahoo, Efficient prediction of exchange rates with low complexity artificial neural network models. Expert Syst. Appl. 36(1), 181–189 (2009).  https://doi.org/10.1016/j.eswa.2007.09.005 CrossRefGoogle Scholar
  23. 23.
    P.K. Dash, M. Nayak, M.R. Senapati, I.W.C. Lee, Mining for similarities in time series data using wavelet-based feature vectors and neural networks. Eng. Appl. Artif. Intell. 20(2), 185–201 (2007).  https://doi.org/10.1016/j.engappai.2006.06.018 CrossRefGoogle Scholar
  24. 24.
    Z.W. Geem, J.H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search. Simul. SAGE J. 76(2), 60–68 (2001).  https://doi.org/10.1177/003754970107600201 CrossRefGoogle Scholar
  25. 25.
    X. Wang, X.-Z. Gao, K. Zenger, The Overview of Harmony Search, Springer Briefs in Applied Sciences and Technology (Springer, Cham, 2015)Google Scholar
  26. 26.
    S. Das, S. Mishra, S. Prasad, M.R. Senapati, A harmony search-based artificial neural network for stock market prediction. Int. J. Bus. Forecast. Market. Intell. 2(1), 19–36 (2015)Google Scholar
  27. 27.
    W. Chukiat, Training a single multiplicative neuron with a harmony search algorithm for prediction of S&P500 Index—an extensive performance evaluation, in 4th International Conference on Knowledge and Smart Technology (KST), 7–8 July, pp. 1–5, 2012.  https://doi.org/10.1109/kst.2012.6287731
  28. 28.
    M. Chengying, Harmony search-based test data generation for branch coverage in software structural testing. Neural Comput. Appl. 25(1), 199–216 (2014).  https://doi.org/10.1007/s00521-013-1474-z CrossRefGoogle Scholar
  29. 29.
    C. Worasucheep, A new self-adaptive differential evolution: its application in forecasting the stock exchange of Thailand, in IEEE Congress on Evolutionary Computation (CEC 2007). 25–28 September, pp. 1918–1925, 2007.  https://doi.org/10.1109/cec.2007.4424708
  30. 30.
    A. Qing, Basics of differential evolution, in Differential Evolution in Electromagnetics. Evolutionary Learning and Optimization, vol. 4, ed. by A. Qing, C.K. Lee (Springer, Berlin, 2010)CrossRefGoogle Scholar
  31. 31.
    H. Nizar, J. Bassem, S. Patrick, A fuzzy logic control using a differential evolution algorithm aimed at modelling the financial market dynamics. Inf. Sci. 181(1), 79–91 (2011).  https://doi.org/10.1016/j.ins.2010.09.010 CrossRefGoogle Scholar
  32. 32.
    P. Mohapatra, S. Das, A. Bhoi, T.K. Patra, Mining foreign exchange rates using bio-inspired neural nets. Int. J. Innov. Technol. Explor. Eng. 3(1), 56–62 (2013)Google Scholar

Copyright information

© The Institution of Engineers (India) 2018

Authors and Affiliations

  • Binay Kumar Panigrahi
    • 1
  • Soumya Das
    • 2
  • Tushar Kumar Nath
    • 3
  • Manas Ranjan Senapati
    • 4
  1. 1.Department of Civil EngineeringGandhi Institute of Education and Technology BaniatangiKhordaIndia
  2. 2.Department of Computer Science and EngineeringGovernment College of EngineeringKalahandiIndia
  3. 3.Department of Civil EngineeringIndira Gandhi Institute of Technology (IGIT)SarangaIndia
  4. 4.Department of Information TechnologyVeer Surendra Sai University of TechnologyBurlaIndia

Personalised recommendations