پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 314 |
| تعداد مقالات | 3,321 |
| تعداد مشاهده مقاله | 3,546,595 |
| تعداد دریافت فایل اصل مقاله | 2,591,245 |
Investigating the Effect of Wavelet Decomposition on the Performance of the Optimized Support Vector Regression in Precipitation Simulation | ||
| Water Harvesting Research | ||
| دوره 6، شماره 1 - شماره پیاپی 9، خرداد 2023، صفحه 145-156 اصل مقاله (1.13 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2024.7040.1120 | ||
| نویسندگان | ||
| Majid Rastgary1؛ Emad Mahjoobi* 2؛ Samad Emamgholizadeh3 | ||
| 1PhD student in Water Resources Management and Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| 2Assistant Professor, Department of Water and Environmental Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| 3Professor, Department of Water and Environmental Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| چکیده | ||
| Considering the climatic changes and the increase of extreme values in recent years, in this study, the effect of time series decomposition based on wavelet transform in improving the performance of the optimized support vector regression model in the simulation of annual precipitation in Dashband and Tapik stations has been discussed and investigated in the Lake Urmia Basin in the period of 1971-2020. In this study, the Ant colony algorithm was used to optimize the parameters of the support vector regression model. Daubechies 4 wavelet with three decomposition levels 1, 2 and 3 was used to decomposition the time series of precipitation in the studied stations. The SVR model takes in annual precipitation data as input, while the decomposition-based models take in decomposed precipitation values. The results of investigation the error rate and efficiency of the 4 investigated models include optimized SVR, W1-SVR (optimized SVR based on level 1 decomposition), W2-SVR (optimized SVR based on level 2 decomposition) and W3-SVR (optimized SVR based on level 3 analysis) showed that the error rate of all 4 mentioned models is acceptable and the observed values are in the 95% confidence interval. The error rate of 5.20 and 6.68 mm in the simulation of precipitation in Dashband and Tapik stations using the optimized SVR model by time series decomposition based on wavelet theory in level 1 decomposition in the mentioned stations, 31 and 35 percent improvement has been found. The level 2 decomposition of the time series of precipitation obtained the lowest error among the different levels of decomposition, which was 3.42 and 3.26 mm in Dashband and Tapik stations, respectively. Considering the increase in simulation complexity with the involvement of wavelet theory, the error rate improvement and model performance are acceptable. The hybrid W-SVR model in this study provides reliable results for precipitation simulation. Analyzing the annual precipitation series makes it possible to develop the dimensions of the optimized SVR model. | ||
| کلیدواژهها | ||
| Ant Colony Algorithm؛ Daubechies؛ Decomposition؛ Wavelet Transform | ||
| مراجع | ||
|
Ahmadi, F., & Maddah, M. A. (2021). Development of Wavelet-Kstar Algorithm Hybrid Model for the Monthly Precipitation Prediction (Case Study: Synoptic Station of Ahvaz). Iranian Journal of Soil and Water Research, 52(2), 409-420. Asadi, S., Shahrabi, J., Abbaszadeh, P., & Tabanmehr, S. (2013). A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing, 121, 470-480. Cannas, B., Fanni, A., See, L., & Sias, G. (2006). Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Physics and Chemistry of the Earth, Parts A/B/C, 31(18), 1164-1171. Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Ant System: An autocatalytic optimizing process. Tech. Report 91-016, Politecnico di Milano, Italy. Eslami, P., Nasirian, A., Akbarpour, A., & Nazeri Tahroudi, M. (2022). Groundwater estimation of Ghayen plain with regression-based and hybrid time series models. Paddy and Water Environment, 20(3), 429-440. Jamei, M., Ali, M., Malik, A., Karbasi, M., Rai, P., & Yaseen, Z. M. (2023). Development of a TVF-EMD-based multi-decomposition technique integrated with Encoder-Decoder-Bidirectional-LSTM for monthly rainfall forecasting. Journal of Hydrology, 617, 129105. Karami, F., & Dariane, A. B. (2017). Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model. International Journal of River Basin Management, 15(1), 1-8. Kim, S., Kisi, O., Seo, Y., Singh, V. P., & Lee, C. J. (2016). Assessment of rainfall aggregation and disaggregation using data-driven models and wavelet decomposition. Hydrology Research, 48(1), 99-116. Kousali, M., Salarijazi, M., & Ghorbani, K. (2022). Estimation of non-stationary behavior in annual and seasonal surface freshwater volume discharged into the Gorgan Bay, Iran. Natural Resources Research, 31(2), 835-847. Mendoza, D. E., Samaniego, E. P., Mora, D. E., Espinoza, M. J., & Campozano, L. V. (2019). Finding teleconnections from decomposed rainfall signals using dynamic harmonic regressions: a Tropical Andean case study. Climate Dynamics, 52, 4643-4670. Nazeri Tahroudi, M., & Mirabbasi Najafabadi, R. (2023). Investigating the effect of precipitation series decomposition on the simulation of electrical conductivity of river flow (case study: Eskandari sub-basin). Journal of Drought and Climate change Research, 1(1), 33-48. doi: 10.22077/jdcr.2023.5950.1005 Nazeri Tahroudi, M., & Mirabbasi, R. (2023a). Development of decomposition-based model using Copula-GARCH approach to simulate instantaneous peak discharge. Applied Water Science, 13(9), 182. Nazeri Tahroudi, M., & Mirabbasi, R. (2023b). Frequency decomposition associated with machine learning algorithms and copula modeling for river flow prediction. Stochastic Environmental Research and Risk Assessment, 1-22. Nazeri Tahroudi, M., Amirabadyzadeh, M., & Zeynali, M. J. (2018). Evaluation of the Accuracy of Artificial Intelligence and Regression Models in Simulation the Daily Temperature. Journal of Meteorology and Atmospheric Science, 1(1), 65-76. Nazeri-Tahroudi, M., & Ramezani, Y. (2020). Estimation of dew point temperature in different climates of Iran using support vector regression. IDŐJÁRÁS/Quarterly Journal of the Hungarian Meteorological Service, 124(4), 521-539. Nekoeeyan, M. A., Radmanesh, F., & Ahmadi, F. (2022). Prediction of Monthly Streamflow Using Shannon Entropy and Wavelet Theory Approaches (Case study: Maroon River). Water and Irrigation Management, 12(1), 15-31. doi: 10.22059/jwim.2022.335702.949. [In Persian]. Venugopal, V., & Foufoula-Georgiou, E. (1996). Energy decomposition of rainfall in the time-frequency-scale domain using wavelet packets. Journal of Hydrology, 187(1-2), 3-27. Wei, S., Yang, H., Song, J., Abbaspour, K., & Xu, Z. (2013). A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58(2), 374-389. Zakhrouf, M., Bouchelkia, H., Stamboul, M., Kim, S., & Heddam, S. (2018). Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria). Physical Geography, 39(6), 506-522. | ||
|
آمار تعداد مشاهده مقاله: 122 تعداد دریافت فایل اصل مقاله: 240 |
||
نماد الکترونیک
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب