توسعه روش هیبریدی موجک-الگوریتم Kstar برای پیش‌بینی بارش‌های ماهانه (مطالعه موردی: ایستگاه سینوپتیک اهواز)

نوع مقاله : مقاله پژوهشی

نویسندگان

استادیار گروه هیدرولوژی و منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران

چکیده

پیش­بینی متغیرهای هیدرولوژیک و به ویژه بارش نقش بسیار مهمی در مدیریت و برنامه­ریزی منابع آبی داشته و از این­رو توسعه روش­هایی که بتواند تخمین دقیقی از آن را به دست دهد همواره مورد توجه محققان بوده است. در این پژوهش از داده­های بارش ایستگاه سینوپتیک اهواز در دوره آماری 2018-1961 برای توسعه مدل­های هیبریدی موجک Kstar (WKstar) و برنامه­ریزی بیان ژن (WGEP) استفاده شد. عملکرد مدل­های به کار رفته با شاخص­های آماری ضریب همبستگی (CC)، نش- ساتکلیف (NS)، کلینگ گوپتا (KGE) و ضریب ویلموت (WI) مورد بررسی قرار گرفت. در ابتدا مدل­های منفرد Kstar و GEP با ورودی­های بارش تاخیر یافته تا چهار ماه قبل و شماره ماه­ها اجرا شدند. نتایج نشان داد که هر دو مدل با تاخیر زمانی یک ماه (الگوی M1) به بیشترین دقت رسیده اما عملکرد آنها بسیار ضعیف و غیرقابل قبول بود. با توجه به اینکه هر دو مدل با الگوی M1 بهترین عملکرد را داشته­اند از این رو بارش­های یک ماه قبل با استفاده از پنج تابع موجک مختلف به زیرسری­های تقریب و جزئیات تجزیه شده و مجدداً به مدل­ها معرفی شدند. نتایج نشان داد که عملکرد مدل­های هیبریدی موجک نسبت به حالت منفرد بسیار بهبود یافته به طوری که شاخص NS از 139/0 به 607/0 افزایش یافت. همچنین بهترین عملکرد مدل­های هیبریدی WKstar و WGEP با ورودی­های تابع موجک دابچیز چهار و سطح تجزیه دو به دست آمده و از نظر آماری اختلاف معنی­داری بین دو مدل هیبریدی توسعه یافته وجود نداشت، اما با استفاده از نمودار ویولونی مشخص گردید که مدل WKstar برای پیش­بینی بارش­های ایستگاه سینوپتیک اهواز مناسب­تر می­باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Development of Wavelet-Kstar Algorithm Hybrid Model for the Monthly Precipitation Prediction (Case Study: Synoptic Station of Ahvaz)

نویسندگان [English]

  • Farshad Ahmadi
  • Mohammad Amin Maddah
Assistant Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
چکیده [English]

Predicting hydrological parameters, especially rainfall, has played a very important role in water resources management and planning. Therefore, the development of methods giving accurate estimates has always been of interest to researchers. In this study, precipitation data from the Ahvaz synoptic station in the period of 2018-1961 were used to develop Kstar and Gene Expression Programming wavelet hybrid models (WKstar and WGEP). The performance of the applied models was evaluated using statistical indices, including the correlation coefficient (CC), Nash-Sutcliffe (NS), Kling–Gupta (KGE) and the Willmott Index (WI). Initially, the Kstar and GEP individual models were implemented, with a delay in precipitation input up to four months ago and month numbers. Results showed that both models achieved maximum accuracy with a time delay of one month (M1 senario), but their performance was very low and unacceptable. Regarding that both models with the M1 pattern have the best performance, so the M1 inputs decomposed by five different wavelet functions to detail and approximat subsets and were represented to the models. The results showed that the performance of wavelet hybrid models was significantly improved, so that the NS index increased from 0.139 to 0.607. In addition, the best performance of WKstar and WGEP hybrid models was obtained with the inputs of the Daubechies wavelet of order four and the decomposition level two. Also, there was no significant difference between the two developed hybrid models statistically, but using the violin plot it was found that the WKstar model is more suitable for predicting precipitation at the Ahvaz synoptic station.

کلیدواژه‌ها [English]

  • Decomposition Level
  • Mother wavelet
  • Willmott index
  • Violin plot
Abdourahamane, Z. S., Acar, R., & Serkan, Ş. (2019). Wavelet–copula‐based mutual information for rainfall forecasting applications. Hydrological Processes, 33(7), 1127-1142.
Ahmadi, F. (2020). Evaluation of Support Vector Machine and Adaptive Neuro-Fuzzy Inference System Performance in Prediction of Monthly River Flow (Case Study: Nazlu chai and Sezar Rivers). Iranian Journal of Soil and Water Research, 51(3), 673-686. (In Farsi)
Ahmadi, F., and Valinia, M. (2020). Prediction of Monthly River Flow Using Hybridization of Linear Time Series Models and Bayesian network (Case Study: Bakhtiari River). Water and Irrigation Management, 10(2), 233-245. (In Farsi)
Bushara, N. O. (2019). Weather forecasting using soft computing models: A comparative study. Journal of Applied Science, 2 (2): 1−22.
Costache, R., Pham, Q. B., Sharifi, E., Linh, N. T. T., Abba, S. I., Vojtek, M., ... & Khoi, D. N. (2020). Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sensing, 12(1), 106.
Ekmekcioğlu, Ö., Başakın, E. E., & Özger, M. (2020). Tree-based nonlinear ensemble technique to predict energy dissipation in stepped spillways. European Journal of Environmental and Civil Engineering, 1-19.
Estévez, J., Bellido-Jiménez, J. A., Liu, X., & García-Marín, A. P. (2020). Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment. Water, 12(7), 1909.
Ferreira, C. (2002). Genetic representation and genetic neutrality in gene expression programming. Advances in Complex Systems, 5(04), 389-408.
Freire, P. K. D. M. M., Santos, C. A. G., and da Silva, G. B. L. (2019). Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting. Applied Soft Computing. 80: 494-505.
Granata, F., Di Nunno, F., Gargano, R., & de Marinis, G. (2019). Equivalent discharge coefficient of side weirs in circular channel- a lazy machine learning approach. Water, 11(11), 2406.
Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection (Vol. 1). MIT press.
Mallat, S. G. 1998. A wavelet tour of signal processing, San Diego. Grossmann, A., & Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM journal on mathematical analysis, 15:4. 723-736.
Mehdizadeh, S., Ahmadi, F., Mehr, A. D., & Safari, M. J. S. (2020). Drought modeling using classic time series and hybrid wavelet-gene expression programming models. Journal of Hydrology, 125017.
Mehdizadeh, S., Behmanesh, J., & Khalili, K. (2018). New approaches for estimation of monthly rainfall based on GEP-ARCH and ANN-ARCH hybrid models. Water resources management, 32(2), 527-545.
Mehr, A. D. (2018). Month ahead rainfall forecasting using gene expression programming. American Journal of Earth and Environmental, 667, 63-70.
Mehr, A. D., Nourani, V., Khosrowshahi, V. K., & 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.
Mirabbasi, R., Kisi, O., Sanikhani, H., & Meshram, S. G. (2019). Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models. Neural Computing and Applications, 31(10), 6843-6862.
Mohammadi, B., Ahmadi, F., Mehdizadeh, S., Guan, Y., Pham, Q. B., Linh, N. T. T., & Tri, D. Q. (2020). Developing novel robust models to improve the accuracy of daily streamflow modeling. Water Resources Management, 34(10), 3387-3409.
Phillies, G. D. J., Gould, H., and Tobochnik, J. (1996). Wavelets: a new alternative to Fourier transforms. Comput. Phys. 10, 247–252.
Polikar, R. (1996). Fundamental concepts and an overview of the wavelet theory. Second Edition, Rowan University, College of Engineering Web Servers, Glassboro. NJ. 08028.
Polikar, R., & Mastorakis, N. (1999). The story of wavelets in Physics and Modern Topics in Mechanical and Electrical Engineering. World Scientific and Engineering Society Press, 192-197.
Santos, C. A., Freire, P. K., Silva, R. M. D., & Akrami, S. A. (2019). Hybrid wavelet neural network approach for daily inflow forecasting using tropical rainfall measuring mission data. Journal of Hydrologic Engineering, 24(2), 04018062.
Solgi, A., golabi, M. (2017). Performance Assessment of Gene Expression Programming Model Using Data Preprocessing Methods to Modeling River Flow. Journal of Water and Soil Conservation, 24(2), 185-201. (In Farsi)
Sun, Y., Niu, J., & Sivakumar, B. (2019). A comparative study of models for short-term streamflow forecasting with emphasis on wavelet-based approach. Stochastic Environmental Research and Risk Assessment, 33(10), 1875-1891.
Wang, W., & Ding, J. (2003). Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1(1), 67-71.
Willmott, C. J., Robeson, S. M., & Matsuura, K. (2012). A refined index of model performance. International Journal of Climatology, 32(13), 2088-2094.