A Preliminary Study on a Hybrid Wavelet Neural Network Model for Forecasting Monthly Rainfall
Shiliang Zhang 1  
More details
Hide details
School of Information and Electrical Engineering, Ningde Normal University, Jiaocheng District, Ningde, P.R. CHINA
United Network Communications Co., Ltd., Fuzhou, P.R. CHINA
Online publish date: 2018-02-07
Publish date: 2018-02-07
EURASIA J. Math., Sci Tech. Ed 2018;14(5):1747–1757
In this paper, a hybrid wavelet neural network (HWNN) model is developed for effectively forecasting rainfall with the data of antecedent monthly rainfalls, the ant colony optimization algorithm (ACO) is combined with particle swarm optimization algorithm (PSO) to improve performance of artificial neural network (ANN) model. ACO is adopted to initialize the network connection the weights of and thresholds of WNN and PSO is used to update the parameters of ACO, HWNN can avoid falling into a local optimal solution and improve its convergence rate and obtain more accurate results. In simulations based on monthly rainfall data from the city of Ningde in the southeastern China. The forecasting performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error, root mean square error and average absolute percentage error. The results show that the HWNN model improves the monthly rainfall forecasting accuracy over Ningde in comparison to the reference models. The performance comparison shows that the proposed approach performs appreciably better than the compared approaches. Through the experimental results, the proposed approach has shown excellent prediction performance.
1. Alizadeh, M. J., & Kavianpour, M. R. (2015). Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Mar Pollut Bull., 98, 171-8.
2. Alizdeh, M. J., Joneyd, P. M., Motahhari, M., Ejlali, F., & Kiani, H. (2015). A wavelet-ANFIS model to estimate sedimentation in dam reservoir. Int. J. Comput. Appl., 114(9), 19-25.
3. Avci, E. (2009). Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm–support vector machines: HGASVM. Expert Systems with Applications, 36(2), 1391–1402.
4. Bigiarini, M., Clerc, M., & Rojas, R. (2013). Standard particle swarm optimization 2011 at CEC-2013: a baseline for future PSO improvements. In IEEE Congress on Evolutionary Computation, 2337–2344.
5. Boeringer, D. W., & Werner, D. H. (2004). Particle swarm optimization versus genetic algorithms for phased array synthesis. IEEE Transactions on Antennas and Propagation, 52(3), 771–779.
6. Chouikhi, N., Ammar, B., Rokbani, N., & Alimi, A. M. (2017). PSO-based analysis of Echo State Network parameters for time series forecasting. Applied Soft Computing, 55, 211-225.
7. Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H., & Ramli, M. F. (2012). Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar. Pollut. Bull., 64, 2409–2420.
8. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. IEEE International Conference on Neural Networks, Australia, 1942–1948.
9. Kisi, O. (2008). River flow forecasting and estimation using different artificial neural network techniques. Hydrology research, 39, 27-40.
10. Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., & Dai, K. (2012). An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Systems with Applications, 39(1), 424–430.
11. Nourani, V., Alami, M. T., & Aminfar, M. H. (2009). A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng. Appl. Artif. Intell. 22, 466–472.
12. Nourani, V., Kisi, Ö., & Komasi, M., (2011). Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process. J. Hydrol., 402, 41–59.
13. Okkan, U. (2012). Wavelet neural network model for reservoir inflow prediction. Scientia Iranica, 19, 1445–1455.
14. Rajaee, T., Nourani, V., Zounemat-Kermani, M., & Kisi, O. (2010). River suspended sediment load prediction: application ofANN and wavelet conjunction model. J. Hydrol. Eng., 16, 613–627.
15. Shen, Q., Shi, W. M., Kong, W., & Ye, B. X. (2007). A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification. Talanta, 71(4), 1679–1683.
16. Shi, X., & Zhou, J. (2012). Prediction residential house’s damage effect near open pit against blasting vibration based on svm with grid searching method/genetic algorithm. Advanced Science Letters, 11(1) 238–243.
17. Shi, Y. H., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization, 1999 CEC 99. In Proceedings of the 1999 Congress on Evolutionary Computation, Indianapolis, USA, 1945–1950.
18. Subasi, A. (2012). Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Computers in Biology and Medicine, 43(5), 576–586.
19. Tryland, I., Myrmel, M., Østensvik, Ø., Wennberg, A. C., & Robertson, L. J. (2014). Impact of rainfall on the hygienic quality of blue mussels and water in urban areas in the Inner Oslofjord, Norway. Mar Pollut Bull., 85(1).
20. Wu, C. H., Tzeng, G. H., & Lin, R. H. (2009). A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Systems with Applications, 36(3), 4725–4735.