Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts

  • Ali Danandeh MehrEmail author
  • Mir Jafar Sadegh Safari


It is well documented that standalone machine learning methods are not suitable for rainfall forecasting in long lead-time horizons. The task is more difficult in arid and semiarid regions. Addressing these issues, the present paper introduces a hybrid machine learning model, namely multiple genetic programming (MGP), that improves the predictive accuracy of the standalone genetic programming (GP) technique when used for 1-month ahead rainfall forecasting. The new model uses a multi-step evolutionary search algorithm in which high-performance rain-borne genes from a multigene GP solution are recombined through a classic GP engine. The model is demonstrated using rainfall measurements from two meteorology stations in Lake Urmia Basin, Iran. The efficiency of the MGP was cross-validated against the benchmark models, namely standard GP and autoregressive state-space. The results indicated that the MGP statistically outperforms the benchmarks at both rain gauge stations. It may reduce the absolute and relative errors by approximately up to 15% and 40%, respectively. This significant improvement over standalone GP together with the explicit structure of the MGP model endorse its application for 1-month ahead rainfall forecasting in practice.


Rainfall Stochastic modelling Genetic programming Hybrid models 



  1. Aksoy, H., & Dahamsheh, A. (2009). Artificial neural network models for forecasting monthly precipitation in Jordan. Stochastic Environmental Research and Risk Assessment, 23(7), 917–931.CrossRefGoogle Scholar
  2. Aksoy, H., & Dahamsheh, A. (2018). Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions. Journal of Hydrology, 562, 758–779.CrossRefGoogle Scholar
  3. Azad, A., Manoochehri, M., Kashi, H., Farzin, S., Karami, H., Nourani, V., & Shiri, J. (2019). Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling. Journal of Hydrology, 571, 214–224.CrossRefGoogle Scholar
  4. Bakhshaii, A., & Stull, R. (2009). Deterministic ensemble forecasts using gene-expression programming. Weather and Forecasting, 24(5), 1431–1451.CrossRefGoogle Scholar
  5. Cassel, D. K., Wendroth, O., & Nielsen, D. R. (2000). Assessing spatial variability in an agricultural experiment station field: opportunities arising from spatial dependence. Agronomy Journal, 92(4), 706–714.CrossRefGoogle Scholar
  6. Danandeh Mehr, A. (2018). An improved gene expression programming model for streamflow forecasting in intermittent streams. Journal of Hydrology, 563, 669–678.CrossRefGoogle Scholar
  7. Danandeh Mehr, A., & Kahya, E. (2017). A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction. Journal of Hydrology, 549, 603–615.CrossRefGoogle Scholar
  8. Danandeh Mehr, A., Nourani, V., Hrnjica, B., & Molajou, A. (2017). A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events. Journal of Hydrology, 555, 397–406.CrossRefGoogle Scholar
  9. Danandeh Mehr, A., Nourani, V., Karimi Khosrowshahi, V., & Ghorbani, M. A. (2018a). A hybrid support vector regression–firefly model for monthly rainfall forecasting. International journal of Environmental Science and Technology, 1-12, 643–667.Google Scholar
  10. Danandeh Mehr, A., Nourani, V., Kahya, E., Hrnjica, B., Sattar, A. M., & Yaseen, Z. M. (2018b). Genetic programming in water resources engineering: a state-of-the-art review. Journal of Hydrology, 566.Google Scholar
  11. Delleur, J. W., & Kavvas, M. L. (1978). Stochastic models for monthly rainfall forecasting and synthetic generation. Journal of Applied Meteorology, 17(10), 1528–1536.CrossRefGoogle Scholar
  12. Dufek, A. S., Augusto, D. A., Dias, P. L., & Barbosa, H. J. (2017). Application of evolutionary computation on ensemble forecast of quantitative precipitation. Computers & Geosciences, 106, 139–149.CrossRefGoogle Scholar
  13. Eray, O., Mert, C., & Kisi, O. (2018). Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation. Hydrology Research, 49(4), 1221–1233.CrossRefGoogle Scholar
  14. Fallah-Ghalhüry, G. A., Mousavi-Baygi, M., & Nokhandan, M. H. (2009). Annual rainfnail forecasting by using Mamdani fuzzy inference system. Research Journal of Environmental Sciences, 3(4), 400–413.CrossRefGoogle Scholar
  15. Farajzadeh, J., & Alizadeh, F. (2018). A hybrid linear–nonlinear approach to predict the monthly rainfall over the Urmia Lake watershed using wavelet-SARIMAX-LSSVM conjugated model. Journal of Hydroinformatics, 20(1), 246–262.CrossRefGoogle Scholar
  16. Feng, Q., Wen, X., & Li, J. (2015). Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions. Water Resources Management, 29(4), 1049–1065.CrossRefGoogle Scholar
  17. Hinchliffe, M., Hiden, H., McKay, B., Willis, M., Tham, M., & Barton, G. (1996). Modelling chemical process systems using a multi-gene. Late breaking papers at the genetic programming. pp 56–65.Google Scholar
  18. Hossain, I., Rasel, H. M., Imteaz, M. A., & Mekanik, F. (2018). Long-term seasonal rainfall forecasting: efficiency of linear modelling technique. Environmental Earth Sciences, 77(7), 280.CrossRefGoogle Scholar
  19. Hrnjica, B., & Danandeh Mehr, A. (2019). Optimized genetic programming applications: emerging research and opportunities (pp. 1–310). Hershey: IGI global.CrossRefGoogle Scholar
  20. Jia, X., Shao, M., Zhu, Y., & Luo, Y. (2017). Soil moisture decline due to afforestation across the Loess Plateau, China. Journal of Hydrology, 546, 113–122.CrossRefGoogle Scholar
  21. Karamouz, M., Razavi, S., & Araghinejad, S. (2008). Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study. Hydrological Processes: An International Journal, 22(2), 229–241.CrossRefGoogle Scholar
  22. Karimi, B., Safari, M., Danandeh Mehr, A., & Mohammadi, M. (2019). Monthly rainfall prediction using ARIMA and gene expression programming: a case study in Urmia, Iran. Online Journal of Engineering Sciences and Technologies, 2(3), 8–17.Google Scholar
  23. Kashid, S. S., & Maity, R. (2012). Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using genetic programming. Journal of Hydrology, 454, 26–41.CrossRefGoogle Scholar
  24. Kisi, O., & Cimen, M. (2012). Precipitation forecasting by using wavelet-support vector machine conjunction model. Engineering Applications of Artificial Intelligence, 25(4), 783–792.CrossRefGoogle Scholar
  25. Kisi, O., & Shiri, J. (2011). Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resources Management, 25(13), 3135–3152.CrossRefGoogle Scholar
  26. Koza, J. R., (1992). The genetic programming paradigm: genetically breeding populations of computer programs to solve problems. Dynamic, Genetic, and Chaotic Programming, (June), 203–321. A.Google Scholar
  27. Lin, G. F., & Chen, L. H. (2005). Application of an artificial neural network to typhoon rainfall forecasting. Hydrological Processes, 19(9), 1825–1837.CrossRefGoogle Scholar
  28. Lin, G. F., & Wu, M. C. (2009). A hybrid neural network model for typhoon-rainfall forecasting. Journal of Hydrology, 375(3–4), 450–458.CrossRefGoogle Scholar
  29. Luk, K. C., Ball, J. E., & Sharma, A. (2000). A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. Journal of Hydrology, 227(1–4), 56–65.CrossRefGoogle Scholar
  30. Mehr, A. D., Jabarnejad, M., & Nourani, V. (2019). Pareto-optimal MPSA-MGGP: a new gene-annealing model for monthly rainfall forecasting. Journal of Hydrology, 571, 406–415.CrossRefGoogle Scholar
  31. Mekanik, F., Imteaz, M. A., & Talei, A. (2016). Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals. Climate Dynamics, 46(9–10), 3097–3111.CrossRefGoogle Scholar
  32. Nasseri, M., Asghari, K., & Abedini, M. J. (2008). Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Systems with Applications, 35(3), 1415–1421.CrossRefGoogle Scholar
  33. Samantaray, S., Tripathy, O., Sahoo, A., & Ghose, D. K. (2020). Rainfall forecasting through ANN and SVM in Bolangir Watershed, India. In smart intelligent computing and applications (pp. 767–774). Singapore: Springer.Google Scholar
  34. Searson, D. (2015). GPTIPS 2: aan open-source software platform for symbolic data mining. In A. H. G. et al. (Ed.), Chapter 22 in handbook of genetic programming applications. New York, NY: Springer.CrossRefGoogle Scholar
  35. Sivapragasam, C., Liong, S., & Pasha, M. (2001). Rainfall and runoff forecasting with SSA-SVM approach. Journal of Hydroinformatics, 3(3), 141–152.CrossRefGoogle Scholar
  36. Toth, E., Brath, A., & Montanari, A. (2000). Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology, 239, 132–147.CrossRefGoogle Scholar
  37. Wu, C. L., Chau, K. W., & Fan, C. (2010). Prediction of rainfall time series using modular artificial neural networks coupled with data-pre-processing techniques. Journal of Hydrology, 389(1–2), 146–167.CrossRefGoogle Scholar
  38. Yevjevich, V. (1987). Stochastic models in hydrology. Stochastic Hydrology and Hydraulics, 1(1), 17–36.CrossRefGoogle Scholar
  39. Zhu, C., & Wu, J. (2013). Hybrid of genetic algorithm and simulated annealing for support vector regression optimization in rainfall forecasting. International Journal of Computational Intelligence and Applications, 12(02), 1350012.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringAntalya Bilim UniversityAntalyaTurkey
  2. 2.Department of Civil EngineeringYaşar UniversityIzmirTurkey

Personalised recommendations