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A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network

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Abstract

Artificial neural networks (ANNs) are being used increasingly to forecast rainfall. In this study, several meta-heuristic algorithms are applied to train an ANN in order to improve the accuracy of rainfall forecasting. Centripetal accelerated particle swarm optimization (CAPSO), a gravitational search algorithm and an imperialist competitive algorithm train a multilayer perceptron (MLP) network as a feed-forward ANN for rainfall forecasting in Johor State, Malaysia. They are employed to forecast the average monthly rainfall in the next 5 and 10 years using the two modes of original (without data preprocessing) and data preprocessing with singular spectrum analysis. Additionally, for each month, the average monthly rainfall during the last 5 years is computed and a month with less rainfall than the average is classified as 0 (light rainfall month), otherwise as 1 (heavy rainfall month). The attributes used in the classification can be applied to forecast the monthly rainfall. The proposed methods integrate the accuracy and structure of ANN simultaneously. The result showed that the hybrid learning of MLP with the CAPSO algorithm provided higher rainfall forecasting accuracy, lower error and higher classification accuracy. One of the main advantages of CAPSO compared with the other algorithms to train MLP includes the following: The algorithm has no need to tune any algorithmic parameter and it shows good performance on unseen data (testing data).

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References

  1. Olson D, Junker N, Korty B (1995) Evaluation of 33 years of quantitative precipitation forecasting at the NMC. Weather Forecast 10:498–511

    Article  Google Scholar 

  2. Olsson J et al (2004) Neural networks for rainfall forecasting by atmospheric downscaling. J Hydrol Eng 9(1):1–12

    Article  Google Scholar 

  3. Georgakakos K, Bras R (1984) A hydrologically useful station precipitation model: 2. Case studies. Water Resour Res 20:1597–1610

    Article  Google Scholar 

  4. Georgakakos K, Bras R (1984) A hydrologically useful station precipitation model 1. Formulation. Water Resour Res 20:1585–1596

    Article  Google Scholar 

  5. Partal T, Kişi Ö (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212

    Article  Google Scholar 

  6. Kisi O, Cimen M (2012) Engineering applications of artificial intelligence precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intell 25:783–792

    Article  Google Scholar 

  7. Halff AH, Halff HM, Azmoodeh M (1993) Predicting runoff from rainfall using neural networks. Eng Hydrol. ASCE, pp 760–765

  8. Zhu M-L, Fujita M, Hashimoto N (1994) Application of neural networks to runoff prediction. In: Hipel KW et al (eds) Stochastic and statistical methods in hydrology and environmental engineering. Springer, Dordrecht, pp 205–216

    Chapter  Google Scholar 

  9. Fang X, Kuo Y-H (2013) Improving ensemble-based quantitative precipitation forecasts for topography-enhanced typhoon heavy rainfall over Taiwan with a modified probability-matching technique. Mon Weather Rev 141:3908–3932

    Article  Google Scholar 

  10. Flood I, Kartam N (1994) Neural networks in civil engineering. II: systems and application. J Comput Civ Eng 8:149–162

    Article  Google Scholar 

  11. Luk K, Ball J, Sharma A (2001) An application of artificial neural networks for rainfall forecasting. Math Comput Model 33:683–693

    Article  MATH  Google Scholar 

  12. Valverde Ramírez MC, de Campos Velho HF, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the São Paulo region. J Hydrol 301:146–162

    Article  Google Scholar 

  13. Azadi S, Sepaskhah A (2012) Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks. Theor Appl Climatol 109:175–189

    Article  Google Scholar 

  14. Mekanika F et al (2013) Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503:11–21

    Article  Google Scholar 

  15. Nastos PT et al (2013) Rain intensity forecast using artificial neural networks in Athens, Greece. Atmos Res 119:153–160

    Article  Google Scholar 

  16. Wu J, Long J, Liu M (2015) Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148:136–142

    Article  Google Scholar 

  17. Tokar A, Johnson P (1999) Rainfall–runoff modeling using artificial neural networks. J Hydrol Eng 4:232–239

    Article  Google Scholar 

  18. Rajurkar M, Kothyari U, Chaube U (2004) Modeling of the daily rainfall–runoff relationship with artificial neural network. J Hydrol 285:96–113

    Article  Google Scholar 

  19. Rezaeian Zadeh M et al (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manage 24:2673–2688

    Article  Google Scholar 

  20. Rezaeian-Zadeh M, Tabari H, Abghari H (2012) Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J Geosci 6:2529–2537

    Article  Google Scholar 

  21. Rezaeian-Zadeh M, Tabari H (2012) MLP-based drought forecasting in different climatic regions. Theor Appl Climatol 109:407–414

    Article  Google Scholar 

  22. Rezaeianzadeh M et al (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput Appl 25:25–37

    Article  Google Scholar 

  23. Bowden G, Dandy G, Maier H (2005) Input determination for neural network models in water resources applications. Part 1—background and methodology. J Hydrol 301:75–92

    Article  Google Scholar 

  24. Singh K et al (2009) Artificial neural network modeling of the river water quality—a case study. Ecol Model 220:888–895

    Article  Google Scholar 

  25. Kumar M, Raghuwanshi N (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128:224–233

    Article  Google Scholar 

  26. Aghajanloo M-B, Sabziparvar A-A, Hosseinzadeh Talaee P (2012) Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran. Neural Comput Appl 23:1387–1393

    Article  Google Scholar 

  27. Tabari H, Hosseinzadeh Talaee P (2013) Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Comput Appl 23:341–348

    Article  Google Scholar 

  28. Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240

    Article  Google Scholar 

  29. Lallahem S et al (2005) On the use of neural networks to evaluate groundwater levels in fractured media. J Hydrol 307:92–111

    Article  Google Scholar 

  30. ASCE (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 124–137

  31. ASCE (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5:115–123

    Article  Google Scholar 

  32. Maier H, Dandy G (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124

    Article  Google Scholar 

  33. Flood I, Kartam N (1994) Neural networks in civil engineering. I: principles and understanding. J Comput Civ Eng 8:131–148

    Article  Google Scholar 

  34. Partal T, Cigizoglu H (2009) Prediction of daily precipitation using wavelet—neural networks. Hydrol Sci J 54:234–246

    Article  Google Scholar 

  35. Ramana R et al (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manage 27:3697–3711

    Article  Google Scholar 

  36. Nasseri M, Asghari K, Abedini M (2008) Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Syst Appl 35:1415–1421

    Article  Google Scholar 

  37. Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinform 12(4):458–473

    Article  Google Scholar 

  38. Zhao H, Jin L, Huang X (2010) A prediction of the monthly precipitation model based on PSO-ANN and its applications. In: Third international joint conference on computational science and optimization, IEEE

  39. Wu J, Wang L, Zhu B (2006) The meteorological prediction model study of neural ensemble based on PSO algorithms. In: The 6th world congress on intelligent control and automation. Dalian, China

  40. Wu J, Chen E (2009) A novel nonparametric regression ensemble for rainfall forecasting using particle swarm optimization technique coupled with artificial neural network. In: Yu W, He H, Zhang N (eds) Proceedings of the 6th international symposium on neural networks, ISNN 2009 Wuhan, 26–29 May 2009, Part III. Lecture notes in computer science, vol 5553. Springer, Berlin, pp 49–58

  41. Hong W-C (2008) Rainfall forecasting by technological machine learning models. Appl Math Comput 200:41–57

    MathSciNet  MATH  Google Scholar 

  42. Wu J, Liu M, Jin L (2010) A hybrid support vector regression approach for rainfall forecasting using particle swarm optimization and projection pursuit technology. Int J Comput Intell Appl 9(2):87–104

    Article  MATH  Google Scholar 

  43. Rumelhart DE, McClelland J (1986) Parallel distributed processing: explorations in the microstructure of cognition: foundations. MIT Press, Cambridge

    Google Scholar 

  44. Brent RP (1991) Fast training algorithms for multilayer neural nets. Neural Netw IEEE Trans 2(3):346–354

    Article  MathSciNet  Google Scholar 

  45. Gori M, Tesi A (1992) On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Mach Intell 14(1):76–86

    Article  Google Scholar 

  46. Beheshti Z, Shamsuddin SM (2014) CAPSO: centripetal accelerated particle swarm optimization. Inf Sci 258:54–79

    Article  MathSciNet  Google Scholar 

  47. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  48. Schutz B (2003) Gravity from the ground up: an introductory guide to gravity and general relativity. Cambridge University Press, Cambridge

    Book  Google Scholar 

  49. Halliday D, Resnick R (1993) Fundamentals of physics. Wiley, New York

    MATH  Google Scholar 

  50. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Evolutionary computation, 2007. CEC 2007. IEEE congress on, IEEE

  51. Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447

    Article  Google Scholar 

  52. Beheshti Z (2013) Centripetal accelerated particle swarm optimization and its applications in machine learning. Universiti Teknologi Malaysia

  53. Lisi F, Nicolis O, Sandri M (1995) Combining singular-spectrum analysis and neural networks for time series forecasting. Neural Process Lett 2(4):6–10

    Article  Google Scholar 

  54. Sivapragasam C, Liong S, Pasha M (2001) Rainfall and runoff forecasting with SSA-SVM approach. J Hydroinform 3:141–152

    Google Scholar 

  55. Baratta D et al (2003) Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting. Neural Netw 16(3):375–387

    Article  Google Scholar 

  56. Golyandina N, Nekrutkin V, Zhigljavsky AA (2001) Analysis of time series structure: SSA and related techniques. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  57. Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  58. Willmott CJ (1981) On the validation of models. Phys Geogr 2(2):184–194

    Google Scholar 

  59. Karunanithi N et al (1994) Neural networks for river flow prediction. J Comput Civ Eng 8(2):201–220

    Article  Google Scholar 

  60. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  MathSciNet  Google Scholar 

  61. Beheshti Z et al (2013) Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. Soft Comput 1–18

  62. Beheshti Z, Shamsudding SM (2013) A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl 5:1–35

    Google Scholar 

  63. Wu C, Chau K, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389:146–167

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank the Research Management Centre (RMC), Universiti Teknologi Malaysia (UTM), for the support in R&D. Also, they hereby would like to appreciate the postdoctoral program, Universiti Teknologi Malaysia (UTM), for the financial support and research activities. This work is partially supported by The Flagship Projects Q.J130000.2428.02G50 and Q.J130000.2428.02G38.

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Authors and Affiliations

  1. UTM Big Data Centre, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Zahra Beheshti & Siti Mariyam Shamsuddin

  2. Centre for Environmental Sustainability and Water Security (IPASA), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Morteza Firouzi & Zulkifli Yusop

  3. Department of Information System, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Masoumeh Zibarzani

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  1. Zahra Beheshti
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  2. Morteza Firouzi
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  3. Siti Mariyam Shamsuddin
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  4. Masoumeh Zibarzani
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  5. Zulkifli Yusop
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Correspondence to Zahra Beheshti.

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Beheshti, Z., Firouzi, M., Shamsuddin, S.M. et al. A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network. Neural Comput & Applic 27, 2551–2565 (2016). https://doi.org/10.1007/s00521-015-2024-7

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  • DOI: https://doi.org/10.1007/s00521-015-2024-7

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