دوره 10، شماره 20 - ( پاییز و زمستان 1398 )
جلد 10 شماره 20 صفحات 12-1 |
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Nozari H, Tavakoli F. (2019). Evaluation of the Efficiency of Linear and Nonlinear Models in Predicting Monthly Rainfall (Case Study: Hamedan Province). jwmr. 10(20), 1-12. doi:10.29252/jwmr.10.20.1
URL: http://jwmr.sanru.ac.ir/article-1-935-fa.html
URL: http://jwmr.sanru.ac.ir/article-1-935-fa.html
نوذری حامد، توکلی فاطمه. ارزیابی کارایی مدل‌های مختلف خطی و غیرخطی در پیش‌بینی بارندگی ماهانه در تغییرات اقلیم استان همدان پژوهشنامه مديريت حوزه آبخيز 1398; 10 (20) :12-1 10.29252/jwmr.10.20.1
گروه علوم و مهندسی آب
چکیده: (2865 مشاهده)
در این پژوهش به منظور پیش‌بینی مقادیر ماهانه بارش از مدل‌های ماشین بردار پشتیبان (SVM)، ماشین بردار پشتیبان تلفیق شده با تبدیل موجک (W-SVM)،ARMAX و ARIMA استفاده گردید. لذا از سری زمانی ماهانه ایستگاه‌های باران‌سنجی واقع در استان همدان طی یک دوره 25 ساله (1370-1394) استفاده شد. این دوره 25 ساله به 17 سال برای آموزش، 4 سال برای واسنجی و ۴ سال برای صحت‌سنجی مدل تقسیم شد. مقایسه آماری نتایج به کمک شاخص‌های ضریب همبستگی (r)، جذر میانگین مربعات خطا (RMSE) و خطای استاندارد (SE) صورت گرفت. نتایج نشان داد که به ترتیب مدل‌های ARIMA، ماشین بردار پشتیبان، ARMAX و ماشین بردار پشتیبان تلفیق شده با تبدیل موجک در رتبه‌های اول تا چهارم قرار دارند. همچنین مدل ماشین بردار پشتیبان دارای پارامترهای قابل تنظیم کمتری نسبت به مدل‌های دیگر می باشد. لذا این مدل با سهولت بیشتر و در زمان کمتری قادر به پیش‌بینی بارش بوده و از این نظر نسبت به سایر روش‌ها ارجحیت دارد.
نوع مطالعه: پژوهشي |
موضوع مقاله:
هواشناسی
دریافت: 1397/1/24 | ویرایش نهایی: 1399/3/27 | پذیرش: 1397/10/15 | انتشار: 1398/10/24
دریافت: 1397/1/24 | ویرایش نهایی: 1399/3/27 | پذیرش: 1397/10/15 | انتشار: 1398/10/24
فهرست منابع
1. 1. Abebe, A.J., D.P. Solomatine and R.G.W. Venneker. 2000. Application of adaptive fuzzy rule-based models for recontruction of missing precipitation events. Hydrological Sciences Journal, 45(3): 425-436. [DOI:10.1080/02626660009492339]
2. Bari, S.H., M.T. Rahman, M.M. Hussain and S. Ray. 2015. Forecasting Monthly Precipitation in Sylhet City Using ARIMA Model. Civil and Environmental Research, 7(1): 69-77.
3. Behzad, M., K. Asghari and E.A. Coppola. 2010. Comparative Study of SVMs and ANNs in Aquifer Water Level Prediction, Journal of Computing in Civil Engineering, 24(5): 408-413. [DOI:10.1061/(ASCE)CP.1943-5487.0000043]
4. Cannas, B., A. Fanni and G. Sias. 2005. River flow forecasting using neural networks and wavelet analysis. Geophysical Research Abstracts, Vol. 7, SRef-ID: 1607-7962/gra/EGU05-A-08651.
5. Cannas, B., A. Fanni, L. See and G. Sias. 2006. Data preprocessing for river flow forecasting using neural networks, Wavelet transforms and data partitioning. Physics and Chemistry of the Earth 31(18): 1164-1171. [DOI:10.1016/j.pce.2006.03.020]
6. Chowdhury, A. and A. Biswas. 2016. Development of a Monthly Rainfall Prediction Model Using Arima Techniques in Krishnanagar Sub-Division, Nadia District, West Bengal. International Journal of Engineering Studies and Technical Approach, 2(2): 18-26.
7. Coulibaly, F. and N.D. Evora. 2007. Comparison of neural network methods for infilling missing daily weather records. Journal of Hydrology, 341: 27-41. [DOI:10.1016/j.jhydrol.2007.04.020]
8. Cristianini, N. and J. Shawe-Taylor. 2000. An Introduction to Support Vector Machines Cambridge University Press, New York, USA.
9. Dabral, P.P., T. Saring and D. Jhajharia. 2016. Time Series Models of Monthly Rainfall and Temperature to Detect Climate Change for Jorhat (Assam), India. Global NEST Journal, 18: 1-14 [DOI:10.30955/gnj.001740]
10. Dehghani, R., M.A. Ghorbani, M. TeshnehLab, A. Rikhtehgar gheasi and E. Asadi. 2015. Comparison and evaluation of bayesian neural network, gene expression programming, support vector machine and multiple linear regression in river discharge estimation (case study: Sufi Chay Basin). Iranian of Irrigation & Water Engineering, 5(20): 66-85 (In Persian).
11. Graham, A. and E.P. Mishra. 2017. Time series analysis model to forecast rainfall for Allahabad region. Journal of Pharmacognosy and Phytochemistry, 6(5): 1418-1421.
12. Hajibigloo, M., A. Ghazalsoflo and H. Alimirzaee. 2013. Discussion and Forecast Monthly Average Rainfall Techniques Using SARIMA (Case study: Pluviometry Station Babaaman Bojnourd). Journal of Irrigation Science and Engineering, 36(3): 41-54 (In Persian).
13. Hamel, L. 2009. Knowledge Discovery with Support Vector Machines, Hoboken, N.J. John Wiley. [DOI:10.1002/9780470503065]
14. Hamidi, O., J. Poorolajal, M. Sadeghifar, H. Abbasi, Z. Maryanaji, H.R. Faridi and L. Tapak. 2014. A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theoretical and Applied Climatology, 119: 723-731 [DOI:10.1007/s00704-014-1141-z]
15. Isazadeh, M., H. Ahmadzadeh and M.A. Ghorbani. 2018. Assessment of Normalization of Monthly Runoff Probabilistic Distribution impact on SVM and ANN Models Performance in Monthly River Flows Simulation (A Case Study: ZarrinehRud River Basin). Journal of Watershed Management Research, 8(16): 22-33 (In Persian).
16. Isazadeh; M., H. Ahmadzadeh and M.A. Ghorbani. 2016. Assessment of kernel functions performance in river flow estimation using support vector machine. Journal of Water and Soil Conservation, 23(3): 69-89 (In Persian).
17. Karamouz, M. and S. Araghinejad. 2005. Advanced Hydrology. 2nd edn., Tehran Polytechnic Press, Tehran, Iran, 464.
18. Khosravi, M. and H. Shakiba. 2010. Precipitation forecasting using artificial neural networks in order to flood management. 4th International Congress of the Islamic Word Geographers (In Persian).
19. Kibunja, H.W., J.M. Kihoro, G.O. Orwa and W.O. Yodah. 2014. Forecasting Precipitation Using SARIMA Model: A Case Study of Mt. Kenya Region. Mathematical Theory and Modeling, 4(11): 50-58.
20. Kisi, O. 2009. Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J. of Hydrologic Engineering, 14(8): 773-782. [DOI:10.1061/(ASCE)HE.1943-5584.0000053]
21. Kisi, O. and M. Cimen. 2012. Precipitation forecasting by using wavelet-support vector machine conjunction model. Engineering Applications of Artificial Intelligence, 25(4): 783-792. [DOI:10.1016/j.engappai.2011.11.003]
22. Mahmud, I., S.H. Bari and M. Rahman. 2017. Monthly rainfall forecast of Bangladesh using autoregressive integrated moving average method. Environmental Engineering Research, 22(2): 162-168. [DOI:10.4491/eer.2016.075]
23. Manzour, D. and M. Yadi Pour. 2016. Studying the Iranian Electricity Market Price with an ARMAX-GARCH Mode Quarterly. Journal of Quantitative Economics, 13(1): 97-117.
24. Merry, R.J.E. 2005. Wavelet Theory and Applications. A literature study. Eindhoven University of Technology Department of Mechanical Engineering Control Systems Technology Group.
25. Mozafari, Gh.A., Sh. Shafiee and H.R. Hemati. 2016. Predicting monthly precipitation of Kermanshah synoptic station using the hybrid model of neural network and wavelet. Journal of Water and Soil Conservation (Journal of Agricultural Sciences and Natural Resources) 22(6): 135-152 (In Persian).
26. Najafi, A., S. Azizi Ghalati and M.H. Mokhtari. 2017. Assessment Kernel Support Vector Machines in Classification of Landuses (Case Study: Basin of Cheshmeh kileh-Chalkrod) .Journal of Watershed Management Research, 8(15): 92-101 (In Persian).
27. Omidi, R., F. Radmanesh and H. Zarei. 2014. River flow predicting using stochastic models. The First National Conference on Challenges on Water Resources and Agriculture, Esfahan (In Persian).
28. Papalaskaris, T., T. Panagiotidis and A. Pantrakis. 2016. Stochastic Monthly Rainfall Time Series Analysis, Modeling and Forecasting in Kavala City, Greece, North-Eastern Mediterranean Basin. Procedia Engineering, 162: 254-263. [DOI:10.1016/j.proeng.2016.11.054]
29. Rostami, M., A. Facheri Fard, M.A. Ghorbani, S. Darbandi and Y. Dinpajoh. 2012. River flow forecasting using wavelet analysis. Irrigation Sciences and Engineering (Jise) (Scientific Journal of Agriculture), 35(2): 73-81 (In Persian).
30. Salahi, B. and R. Maleki Meresht. 2016. Forecasting and Analysis of Monthly Rainfalls in Ardabil Province by Arima, Autoregrressive, and Winters Models. Journal of Water and Soil, 29(5): 1391-1450 (In Persian).
31. Shafaei, M., J. Adamowski, A. Fakheri-Fard, Y. Dinpashoh and K. Adamowski. 2016. A wavelet-SARIMA-ANN hybrid model for precipitation forecasting. Journal of Water and Land Development, 28(1): 27-36. [DOI:10.1515/jwld-2016-0003]
32. Shenify, M., A.S. Danesh, M. Gocić, R. Surya Taher, A.W. Abdul Wahab, A. Gani, S. Shamshirband and D. Petković. 2015. Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform. Water Resources Management, 30(2): 641-652. [DOI:10.1007/s11269-015-1182-9]
33. Toufani, P., A. Mosaedi and A. Fakheri Fard. 2011. Prediction of precipitation applying wavelet network model (case study: Zarringol station, Golestan province, Iran). Journal of Water and Soil (Agricultural Sciences and Technology), 25(5): 1217-1226 (In Persian).
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