Simulation of Long-Term Rainfall in Babolsar City by Using Optimized Wavelet-Extreme Learning Machine

Subject Areas : Farm water management with the aim of improving irrigation management indicators

hamed  karimi ¹ (Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran)
mohammad ali  izadbakhsh ² (Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran)
behrouz  yaghoubi ³ (Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran)
saeid  shabanlou ⁴ (Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran)

Received: 2020-04-29 Accepted : 2021-07-03 Published : 2021-07-23

Keywords: Rainfall, Simulation, Babolsar, Wavelet Transform, Extreme learning machine,

Abstract :

In this study, the long-term rainfall in Babolsar city was simulated by means of an optimized AI model. To do this, the extreme learning machine (ELM) and the wavelet transform (WT) were combined. It should be stated that the monthly rainfall values from 1951 to 2019 were applied, meaning that 70% of the observed values were employed to training the AI models and 30% of rest were utilized to testing these models. Firstly, the activation functions of the ELM models were evaluated; as a result, the sigmoid was chosen as the best activation function. Moreover, the lags of time series were introduced using the autocorrelation function (ACF) that four ELM models were defined through those identified lags. By performing a sensitivity analysis, the superior ELM model was introduced. The values of correlation coefficient (R), variance accounted for (VAF), and scatter index (SI) for the ELM model were respectively computed to be 0.524, 27.064, and 0.819. Furthermore, different mother wavelets were examined and the “dmey” was opted as the best mother wavelet. The wavelet transform enhanced the accuracy of the simulations significantly. For instance, the VAF index for the hybrid WELM model equaled to 86.461. It is noteworthy that the hybrid model was evaluated for different decomposition levels (DL) and then the best one was detected. Also, the (t-1) and (t-12) lags were identified as the most effective input lags.

References:

باباعلی، ح. و دهقانی، ر. 1391 . مقایسه مدل های هوشمند در تخمین بارش ماهانۀ حوضۀ کاکارضا. اکولوژی، 1(4): 1-11.

دهقانی، ن.، وفاخواه، م. و بهره مند، ع. 1392 . مدل سازی بارش رواناب با استفاده از شبکه عصبی مصنوعی و شبکه فازی - عصبی تطبیقی در حوزه آبخیز کسیلیان. پژوهشنامه مدیریت حوزه آبخیز، 7 (13): 128-137.

Chang, T. K., Talei, A., Quek, C. and Pauwels, V. R. 2018. Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure. Journal of hydrology, 564: 1179-1193.
Dabral, P. P. anf Murry, M. Z. 2017. Modelling and forecasting of rainfall time series using SARIMA. Environmental Processes, 4(2): 399-419.
Hardwinarto, S. and Aipassa, M. 2015. Rainfall monthly prediction based on artificial neural network: A case study in Tenggarong Station, East Kalimantan-Indonesia. Procedia Computer Science, 59: 142-151.
Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K. 2006. Extreme learning machine: theory and applications. Neurocomputing 70:489–501.
Mallat, S. 1989. Multiresolution approximations and wavelet orthonormal bases of L2(R). Trans. Am. Math. Soc. 315 (1):69–87.
Mehr, A. D., Nourani, V., Khosrowshahi, V. K. and Ghorbani, M. A. 2019. A hybrid support vector regression–firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology, 16(1): 335-346.
Nagahamulla, H. R., Ratnayake, U. R. and Ratnaweera, A. 2012. An ensemble of artificial neural networks in rainfall forecasting. In International Conference on Advances in ICT for Emerging Regions (ICTer2012) (pp. 176-181). IEEE.
Nasseri, M., Asghari, K. and 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.
Nourani, V., Hosseini Baghanam, A., Adamowski, J. and Kisi, O. 2014. Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology 514: 358–377.
Nourani, V., Kaynejad, M.A. and Malekani, L. 2009. Application of neuro- fuzzy inference system in rainfall-runoff modeling. Journal of Civil of Engineering & Environment, 39(4): 75-81.
Savic, D. A., Walters, G. A. and Davidson, J. W. 1999. A genetic programming approach to rainfall-runoff modelling. Water Resources Management, 13(3), 219-231.
Wong, K. W., Wong, P. M., Gedeon, T. D. and Fung, C. C. 2003. Rainfall prediction model using soft computing technique. Soft Computing, 7(6): 434-438.