Investigation of Effective Parameters in Modeling Monthly Precipitation using Intelligent Integrated Models Based on Time Series Decomposition

Document Type : Original Article

Authors

1 Department of Geography, Ahar Branch, Islamic Azad University - Ahar-Iran

2 Department of Civil Engineerin, Ahar Branch, Islamic Azad University - Ahar - Iran

Abstract

Rainfall forecasting is important in many different aspects of watershed management, such as flood and drought warning systems. Spatiotemporal variations of rainfall cause its prediction to be difficult. In this study, the monthly rainfall of Urmia and Mako stations were assessed using the intelligent kernel-based methods using Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Transform (DWT). For this aim, different models were developed based on teleconnection patterns and climatic elements including rainfall, humidity, and temperature of previous months, and the impact of these parameters on accuracy of the modeling process was investigated. The obtained results showed the high efficiency of the integrated methods used in modeling process. It was observed that in the monthly precipitation modeling, the simultaneous use of teleconnection patterns and climatic elements related to previous months improves the accuracy of the models by up to 35%. Comparison of the wavelet transform and ensemble empirical mode decomposition showed that time series decomposition based on wavelet transform led to more accurate outcomes. The results of sensitivity analysis showed that the precipitation parameter with three months lag was the most effective parameter in monthly precipitation modeling.

Keywords

References

ﺑﺨﺘﯿﺎری، ب.، ﺷﻬﺮﮐﯽ، ن. و اﺣﻤﺪی، م. م. 1392. ﺑﺮآورد اﺣﺘﻤﺎﻻت ﺑﺎرش روزاﻧﻪ ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪل زﻧﺠﯿﺮه ﻣﺎرﮐﻒ در اﻗﻠﯿﻢ ﻫﺎی ﻣﺨﺘﻠﻒ اﯾﺮان. ﺗﺤﻘﯿﻘﺎت ﻣﻨﺎﺑﻊ آب اﯾﺮان. 2 (10): 55-44.
ﺳﺘﺎری، م.ت.، رﺿﺎزاده ﺟﻮدی، ع. و ﻧﻬﺮﯾﻦ ، ﻓﺮﻧﺎز. 1393. ﭘﯿﺶ ﺑﯿﻨﯽ ﻣﻘﺎدﯾﺮ ﺑﺎرش ﻣﺎﻫﺎﻧﻪ ﺑﺎ اﺳﺘﻔﺎده از ﺷﺒﮑﻪ ﻫﺎی ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ و ﻣﺪل درﺧﺘﯽ M5 ﻣﻄﺎﻟﻌﻪ ﻣﻮردی: اﯾﺴﺘﮕﺎه اﻫﺮ. ﭘﮋوﻫﺶ ﻫﺎی ﺟﻐﺮاﻓﯿﺎی ﻃﺒﯿﻌﯽ.  46 (2): 260-247.
ﻓﻼﺣﯽ، م. ر.، ورواﻧﯽ، ه. وﮔﻠﯿﺎن، س. 1390. ﭘﯿﺶ ﺑﯿﻨﯽ ﺑﺎرش ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪل رﮔﺮﺳﯿﻮن درﺧﺘﯽ ﺑﻪ ﻣﻨﻈﻮر ﮐﻨﺘﺮل ﺳﯿﻞ. ﭘﻨﺠﻤﯿﻦ ﮐﻨﻔﺮاﻧﺲ ﺳﺮاﺳﺮی آﺑﺨﯿﺰداری و ﻣﺪﯾﺮﯾﺖ ﻣﻨﺎﺑﻊ آب و ﺧﺎک ﮐﺸﻮر.
Adamowski, K., Prokoph, A. and Adamowski, J. 2009. Development of a new method of wavelet aided trend detection and estimation. Hydrology Process. 23(18): 2686-2696.
Amirat, Y., Benbouzidb, M., Wang, T., Bacha, K. and Feld, G. 2018. EEMD-based notch filter for induction machine bearing faults detection. Applied Acoustics. 133: 202-209.
ASCE. 2000. Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Networks in hydrology. I: Preliminary concepts. Hydrological Engineering, ASCE. 5(2): 115-123.
Chong, K. L., Lai, S. H., Yao, Y., Ahmed, A. N., Jaafar, W. Z. and El-Shafie, A. 2020. Performance enhancement model for rainfall forecasting utilizing integrated wavelet-convolutional neural network. Water Resources Management. 34(8): 2371-2387.
Chou, C. M. 2011. Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Stochastic Environmental Research and Risk Assessment. 6: 1401-1408.
Deo, R. C., Samui, P. and Kim, D. 2016. Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stochastic Environmental Research and Risk Assessment. 30: 1769-1786.
Kisi, O., Cimen, M. 2012. Precipitation forecasting by using wavelet-support vector machine conjunction model. Engineering Application Artificial Intelligence. 25: 783-792.
Kumar, S., Tripathy, D., Nayak, S., Mohaparta, S. 2013. Prediction of rainfall in India using artificial neural network models. International Journal of intelligent system and applications. 12: 1-22.
Lau, K. M. and Weng, H. Y. 1995. Climate signal detection using wavelet transform, How to make time series sing. Bull Am Meteorol Soc. 76: 2391-2402.
Marzano, F. S., Fionda, E. and Ciotti, P. 2006. Neural-network approach to ground- based passive microwave estimation of precipitation intensity and extinction. Hydrology. 328: 121-131.
Nayak, D., Mahapatra, A. and Mishra, P. 2013. A survey on rainfall prediction using artificial neural network. International journal of computer applications. 72(16): 32-40.
 NOAA. 2009. Earth System Research laboratory, https://www.esrl.noaa.gov/psd/data/climateindices/list/.
Roushangar, K. and Ghasempour, R. 2017. Estimation of bedload discharge in sewer pipes with different boundary conditions using an evolutionary algorithm. International Journal of Sediment Research. 32(4): 564-574.
Samantaray, S., Tripathy, O., Sahoo, A. and Ghose, D. K. 2020. Rainfall forecasting through ANN and SVM in Bolangir Watershed, India. In smart intelligent computing and applications (pp. 767-774). Springer, Singapore.
Siviapragasam, C. and Liong, S. 2001. Rainfall and runoff forcasting with SSA-SVM approach. Hydroinformation. 3: 141-152.
Soltani, A. S., Saberi, A. and Gheisouri, M. 2017. Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province. Researches in Geographical Sciences. 17(44): 87-105.
Wu, Z. and Huang, N. F. 2004. A study of the characteristics of white noise using the empirical mode decomposition method. Proc RS Lond 460A: 1597-1611.
Iranian Journal of Irrigation & Drainage
Volume 16, Issue 4 - Serial Number 94
September and October 2022
Pages 789-803

Files

Share

How to cite

Statistics