Abstract
Accurate and timely precipitation prediction is very important to people’s daily activities and production plans. However, the impact factors of meteorological precipitation are numerous and complex, making it difficult to predict, and the prediction effect by traditional methods is difficult to meet the public expectations. This work proposes to use Multicellular Gene Expression Programming (MC_GEP) algorithm for modeling the historical precipitation data series decomposed by Empirical Mode Decomposition (EMD). Then we design a novel Multicellular Gene Expression Programming based method coupled with Empirical Mode Decomposition, named as EMGEP2RP, for precipitation modeling and prediction. Using RMSE and MAE as evaluation indicators, simulation experiments were conducted on three different types of real precipitation data sets in different regions. The comparing results show that the EMGEP2RP algorithm significantly outperforms not only the existing Gene Expression Programming (GEP) algorithm, but also the Back Propagation and Support Vector Machine algorithms which are widely used in meteorological modeling and predictions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Peng, Y., Wang, Q., Yuan, C., et al.: Review of research on data mining in application of meteorological forecasting. J. Arid Meteorol. 33(1), 19–27 (2015)
Geetha, G., Selvaraj, R.S.: Prediction of monthly rainfall in chennal using back propagation neural network modeal. Int. J. Eng. Sci. Technol. 3(1), 211–213 (2011)
Kisi, O., Cimen, M.: Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng. Appl. Artif. Intell. 25(4), 783–792 (2012)
Chen, C., Feng, H., Chen, J.: Application of Sichuan heavy rainfall ensemble prediction probability products based on Bayesian method. Meteorol. Monthly 36(5), 32–39 (2010)
Mishra, N., Soni, H.K., Sharma, S., et al.: Development and analysis of artificial neural network models for rainfall prediction by using time-series data. Int. J. Intell. Syst. Appl. 10(1), 16–23 (2018)
Du, J., Liu, Y., Yu, Y., et al.: A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms. Algorithms 10(2), 57 (2017)
Zainudin, S., Jasim, D.S., Bakar, A.A.: Comparative analysis of data mining techniques for Malaysian rainfall prediction. Int. J. Adv. Sci. Eng. Inf. Technol. 6(6), 1148 (2016)
Shamshirband, S., Petkovićet, G., et al.: Soft-computing methodologies for precipitation estimation: a case study. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 8(3), 1353–1358 (2015)
Geetha, A., Nasira, G.M.: Data mining for meteorological applications: decision trees for modeling rainfall prediction. In: IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–4. IEEE (2015)
Abbot, J., Marohasy, J.: Application of artificial neural networks to forecasting monthly rainfall one year in advance for locations within the Murray Darling basin, Australia. Int. J. Sus. Dev. Plann. 12(8), 1282–1298 (2017)
Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, 2nd edn. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-32849-1
Peng, Y., Yuan, C., Chen, J., Wu, X., Wang, R.: Multicellular gene expression programming algorithm for function optimization. Control Theory Appl. 27(11), 1585–1589 (2010)
Wang, X., Bi, G.-h., Tang, J.-R.: Composite forecasting model of sunspot time sequences based on EMD. Comput. Eng. 37(24), 176–179 (2011)
Peng, Y.Z., Yuan, C.A., Qin, X., et al.: An improved gene expression programming approach for symbolic regression problems. Neurocomputing 137(15), 293–301 (2014)
Zhong, J., Ong, Y.S., Cai, W.: Self-learning gene expression programming. IEEE Trans. Evol. Comput. 20(1), 65–80 (2016)
Emamgolizadeh, S., Bateni, S.M., Shahsavani, D., et al.: Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS). J. Hydrol. 529, 1590–1600 (2015)
Roushangar, K., Akhgar, S., Salmasi, F.: Estimating discharge coefficient of stepped spillways under nappe and skimming flow regime using data driven approaches. Flow Meas. Instrum. 59, 79–87 (2018)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China Grant #61562008, #41575051, and the Natural Science Foundation of Guangxi Grant #2017GXNSFAA198228 and #2014GXNSFDA118037, and the grant of “Bagui Scholars” Program of Guangxi Zhuang Autonomous Region of China. Yuzhong Peng is the corresponding author.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Li, H., Peng, Y., Deng, C., Pan, Y., Gong, D., Zhang, H. (2018). Multicellular Gene Expression Programming-Based Hybrid Model for Precipitation Prediction Coupled with EMD. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-95930-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95929-0
Online ISBN: 978-3-319-95930-6
eBook Packages: Computer ScienceComputer Science (R0)