Soft computing tools in rainfall-runoff modeling

ISH Journal of Hydraulic Engineering

Volumn 15, Issue , 2009, Pages 84-96

  Jothiprakash, V ^a   Magar, R ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Adaptive Neuro Fuzzy Inference System; Artificial Neural Network; Chaos Theory; Genetic Programming; Rainfall Runoff process; Soft Computing Technique; Support Vector Machine

Indexed keywords

EID: 84555210587     PISSN: 09715010     EISSN: 21643040     Source Type: Journal    
DOI: 10.1080/09715010.2009.10514970     Document Type: Article

References (60)

1. Abbott, M. B., 1997. Research in to the next generation of computational hydraulic modeling. Proceedings of the 27th Congress of the International Association for Hydraulic Research, 2:859–864.
2. ASCE. 2000b. Artificial neural networks in hydrology II:hydrologic applications. Journal of Hydrologic Engineering, ASCE, 5 (No. 2):124–137.
3. ASCE. 2000a. Artificial Neural Networks in Hydrology I:Preliminary Concepts. Journal of Hydrologic Engineering, ASCE, 5 (No. 2):115–123.
4. Babbovic, V., 1996. Emergence, Evolution, Intelligence:Hydro informatics, Balkema, Publishers Rotterdam.
5. Babbovic, V., and Abbot, M. B., 1997. The Evolution of Equations from Hydraulic Data. Part- II:Applications, Journal of Hydraulic Research, 35:411–430.
6. Beven, K. J., 2001. Rainfall-Runoff Modeling, The Primer, Wiley, Chichester
7. Bhattacharya, B., and Solmatine, D. P., 2003. Neural Networks and M5 Model Trees in Modeling Water Level-Discharge Relationship for an Indian River, Proc.Europen Symposium on Artificial neural Networks 407–412.
8. Bray, M., and Han, D., 2004. Identification of Support Vector Machines for Runoff Modeling. Journal of Hydroinformatics, 6:265–280.
9. Carriere, P., Mohagheghs, S., and Gaskari, R., 1996. Performance of a Virtual Runoff Hydrograph System. Journal of Water Resource Planning and Management, ASCE, 122 (No. 6):421–427.
10. Chang, F. J., and Chen, Y. C., 2001. A Counterpropogation Fuzzy-Neural Network Modeling Approaches to Real Time Stream Flow Prediction. Journal of Hydrology, 245:153–164.
11. Chen, J., and Adams, B., 2006. Integration Neural Network with Conceptual Models in Rainfall Runoff Modeling. Journal of Hydrology, 318:232–249.
12. Cheng, C. T., Ou, C. P., and Chau, K. W., 2002. Combining a fuzzy optimal models with a genetic algorithms solve multi objective rainfall-runoff model calibration. Journal of Hydrology, 26 (No. 3):72–86.
13. Chow, Ven Te., Maidment, David, Mays, R., and Larry, W., 1988. Applied Hydrology, McGraw International Editions, New York.
14. Dibike, Y. B., Velickov, S., Solomatine, D. P., and Abbott, M. B., 2001. Model Induction with Support Vector Machines:Introduction and Applications. Journal of Computing in Civil Engineering, ASCE, 15 (No. 3):208–216.
15. Goldberg, D. E., 1989. Genetic algorithms for search, in:Optimization and Machine Learning. Reading, Mass:Addison-Wesley Publishing Co.
16. Gunn, S., 1998. Support Vector Machines for Classification and Regression. ISIS Technical Report.
17. Haykin, S., 1994. Neural Networks:A Comprehensive Foundation. Mac-Millan, New York.
18. Hundecha, Y., Bardossy, A., and Theisen, H. W., 2001. Development of a Fuzzy Logic Based Rainfall-Runoff Model. Hydrological Sciences Journal, 46 (No. 3):363–377.
19. Jain, A., and Indurthy, P. K.V., 2003. Comparative Analysis of Event Based Rainfall- Runoff Modeling Techniques-Deterministic. Statistical and Artificial Neural Networks, Journal of Hydrology, 8:93–98.
20. Jang, J. S. R., 1992. Self-Learning Fuzzy Controllers Based On Temporal Back Propagation. IEEE, Trans. Neural Networks, 3 (No. 5):714–723.
21. Jayawardena, A. W., Muttil, N., and Lee, J. H. W., 2006. Comparative Analysis of Data-Driven and GIS-Based Conceptual Rainfall-Runoff Model. Journal of Hydrologic Engineering, ASCE, 11 (No. 1):1–11.
22. Khan, S. A., and See, L. M., 2006. Rainfall-Runoff Modelling using Data Driven Statistical Methods., International Conference on Advances in Space Technology, ISBN:1-4244-0515-7 16–20.
23. Khu, T. S., Liong, S. Y., Babovic, V., Madsen, H., and Muttil, N., 2001. Genetic Programming and its Application in Real Time Runoff Forecasting. Journal of American Water Works Association, 37 (No. 2):439–451.
24. Koza, J. R., 1992. Genetic Programming of Computers by Means of Natural Selection Cambridge:MIT Press.
25. Lee, G. Y., and Suzuki, A., 1995. Genetic Programming Approach for Time Series Analysis and Prediction. Journal of Graduate School and Faculty of Engineering, University of Tokyo, 43 (No. 2):201–220.
26. Liong, S. Y., and Sivapragasam, C., 2002. Flood Stage Forecasting with Support Vector Machine. Journal of American Water Resources Association, 38 (No. 1):173–186.
27. Mohan, S., and Jothiprakash, V., 2002. Development of an Interactive Software for Precipitation Analysis. Journal of Indian Water Resources Society, 22 (No. 2):37–46.
28. Nayak, P. C., Sudheer, K. P., Rangan, D. M., and Ramasastri, K. S., 2004. A Neuro-Fuzzy Computing Technique for Modeling Hydrological Time Series. Journal of Hydrology, 291:52–66.
29. Oakley, N., and Howard, E., 1994. The Application of Genetic Programming to the Investigation of Short, Noisy, Chaotic Data Series. In:Evolutionary Programming. Lecture Notes in Computer Sciences, T. C. Fogarty (Editor),:320–332. Springer-Verlag
30. Openshaw, S., and Openshaw, C., 1997. Artificial Intelligence in Geograpahy. John Willy and Sons Ltd, Chichester.
31. Osuna, E., Freund, R., and Girosi, F., 1997. An Improved Training Algorithm for Support Vector Machines. In Proc. of the IEEE Workshop on Neural Networks for Signal Processing VII, New York 276–285.
32. Rajurkar, M. P., Kothyari, U. C., and Chaube, U. C., 2004. Modeling of the daily Rainfall-Runoff relationship with Artificial Neural Network. Journal of Hydrology, 285:96–113. No. (1–4)
33. Sajjad, A., Slobodan, P., and Simonovicb. 2005. An Artificial Neural Network Model for Generating Hydrograph from Hydro-Meteorological Parameters. Journal of Hydrology, 315:236–251.
34. Savic, D. A., Walters, G. A., and Davidson, J. W., 1999. A Genetic Programming Approach to Rainfall-Runoff Modeling. Water Resources Management, 13:219–231.
35. See, L., and Openshaw, S., 1999. Applying Soft Computing Approaches to River Level Forecasting. Hydrological Sciences Journal, 44:763–779. No, 5
36. Sen, Z., 1998. Fuzzy Algorithm for Estimation of Solar Irradiation from Sunshine Duration. Solar Energy, 63 (No. 1):39–49.
37. Shamseldin, A. Y., O'Connor, K. M., and Liang, G. C., 1997. Methods for Combining the Outputs of Different Rainfall-Runoff Models. Journal of Hydrology, 197:203–229.
38. Singh, V. P., 1988. Hydrologic Systems, Vol 1:rainfall-runoff modeling Eaglewood Cliffs, NJ:Prentice Hall.
39. Sivakumar, B., Liong, S. Y, and Liaw, C. Y., 1998. Evidence of chaotic behavior in Singapore rainfall. Journal of American Water Resources Association, 34 (No. 2):301–310.
40. Sivakumar, B., Liong, S. Y, Liaw, C. Y., and Phoon, K. K., 1999. Singapore Rainfall Behavior:Chaotic? Journal of Hydrologic Engineering. ASCE, 4 (No. 1):38–48.
41. Sivakumar, B., Berndtsson, R., Olsson, J., Jinno, K., and Kawamura, A., 2000. Dynamics of Monthly Rainfall-Runoff Process at the Gota Basin:A Search for Chaos. Hydrology Earth System, Science, 4 (No. 3):407–417.
42. Solomatine, D. P, and Xue, Y., 2004. M5 Model Tree and Neural Networks; Application to Flood Forecasting in Upper Reach of Huai River In China. Hydrological Sciences Journal, 9 (No. 6):491–501.
43. Solomatine, D. P., 2002. Computational Intelligence Techniques in Modeling Water System:Some Applications, Proceedings of the International Joint Conference on Neural Networks, IEEE 1853–1858.
44. Srinivasulu, S., and Jain, A., 2006. A Comparative Analysis of training Methods for Artificial Neural Network Rainfall Runoff Models. Applied Soft Computing, Elsevier, 6 (No. 3):295–306.
45. Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S., 2002. A Data Driven Algorithm for Constructing ANN Based Rainfall-Runoff Models. Hydrological Processes, 16 (No. 6):1325–1330.
46. Sugeno, M., and Yasukawa, T., 1993. A Fuzzy-Logic Based Approach to Qualitative Modeling. IEEE Transactions on Fuzzy systems, Vol., 1,:7–31.
47. Takagi, T., and Sugeno, M., 1985. Fuzzy Identification of Systems and its Application to modeling and control. IEEE Transactions System Man and Cybernetics, 15 (No. 1):116–132.
48. Thirumalaiah, K., and Deo, M. C., 2000. Hydrological Forecasting using Neural Networks. Journal of Hydrologic Engineering, 5 (No. 2):180–189.
49. Titamant, A., Vanclooster, M., Duckestein, L., and Persoons, E., 2000. Comparison of Fuzzy and Non Fuzzy Optimal Reservoir Operating Policies. Journal Water resources Planning and Management, ASCE, 128 (No. 6):390–398.
50. Tokar, A. S., and Johnson, P. A., 1999. Rainfall-Runoff Modeling Using Artificial Neural networks. Journal of Hydrologic Engineering, ASCE, 4 (No. 3):232–239.
51. Vapnik, V. N., 1995. The Nature of Statistical Learning Theory, Springer, New York.
52. Vapnik, V. N., 1998. Statistical Learning Theory. John Wiley & Sons, New York
53. Wang, X. K., Lu, W. Z., Cao, S. Y., and Fang, D., 2007. Using Time Delay Neural Network Combined with Genetic Algorithm to Predict Runoff Level of Linsham Watershed. Sinchuan, China, Journal of Hydrologic Engineering, ASCE, 12 (No. 2):231–236.
54. Whigham, P. A., and Crapper, P. F., 2001. Modelling Rainfall Runoff Using Genetic Programming. Mathematical and Computer Modelling, 33:707–721.
55. Yu, P. S., Chen, C. J., and Chen, S. J., 2000. Application of Gray and Fuzzy Method for Rainfall Forecasting. Journal of Hydrologic Engineering, ASCE, 5 (No. 40):339–345.
56. Zadeh, L. A., 1965. Fuzzy Sets. Information Control, 8 (No. 3):338–353.
57. Zadeh, L., and Kacprzyk, J., 1992. Fuzzy Logic for the Management of Uncertainty. Wiley, New York.
58. Zealand, C., Burn, D. H., and Simonovic, S. P., 1999. Short Term Stream Flow Forecasting using Artificial Neural Networks. Journal of hydrology, 214:32–48.
59. Zhang, B., and Govindraju, R. S., 2000. Prediction of Watershed Runoff Using Bayesian Concepts and Modular Neural Networks. Water Resources Research, 36 (No. 3):753–762.
60. Zhu, M. L., and Fujita, M., 1994. Comparison between Fuzzy Reasoning and Neural Network Method to Forecast Runoff Discharge. Journal of Hydro science and Hydraulic Engineering, 12 (No. 2):131–141.