پپيش بيني و ارزيابي ارتباط دبي رودخانه در ايستگاه هاي هيدرومتريك متوالي با استفاده از روش هاي تركيبي GPR-EEMD (مطالعه موردي: رودخانه هوستونيك)

Prediction of River Discharge and Assessment its Relationship at Consecutive Hydrometric Stations Using GPR-EEMD Combined Techniques (Case Study: Housatonic River

ﭘﯿﺶﺑﯿﻨﯽ دﻗﯿﻖ دﺑﯽ در رودﺧﺎﻧﻪﻫﺎ، از ﻣﻬﻢﺗﺮﯾﻦ ﻣﺆﻟّﻔﻪﻫﺎي ﻓﺮآﯾﻨﺪﻫﺎي ﻫﯿﺪروﻟﻮژﯾﮑﯽ و ﻫﯿﺪروﻟﯿﮑﯽ در ﻣﺪﯾﺮﯾﺖ ﻣﻨﺎﺑﻊ آب، ﺑﻪ وﯾﮋه در اﺗّﺨﺎذ ﺗﺪاﺑﯿﺮ ﻣﻨﺎﺳﺐ در ﻣﻮاﻗﻊ ﺧﺸﮑﺴﺎﻟﯽ و ﺑﺮوز ﺳﯿﻼب اﺳﺖ. در اﯾﻦ ﺗﺤﻘﯿﻖ از ﺗﺎﺑﻊ ﻣﻮﺟﮏ و ﺗﺠﺰﯾﮥ ﻣﺪ ﺗﺠﺮﺑﯽ ﯾﮑﭙﺎرﭼﻪ ﮐﻪ از اﺑﺰارﻫﺎي ﻣﺤﺎﺳﺒﺎت ﻧﺮم ﻣﺤﺴﻮب ﻣﯽ ﺷﻮﻧﺪ، ﺟﻬﺖ اﺳﺘﺨﺮاج وﯾﮋﮔﯽﻫﺎي ﺳﺮي زﻣﺎﻧﯽ اﺳﺘﻔﺎده ﮔﺮدﯾﺪه و ﮐﺎراﯾﯽ ﻣﺪلﻫﺎي ﻣﻮﺟﮏ- ﮔﻮﺳﯿﻦ )DWT- GPR( و ﺗﺠﺰﯾﮥ ﻣﺪ ﺗﺠﺮﺑﯽ ﯾﮑﭙﺎرﭼﻪ- ﮔﻮﺳﯿﻦ )EEMD- GPR( ﺑﺮاي ﭘﯿﺶﺑﯿﻨﯽ دﺑﯽ ﺑﯿﻦ ﺳﻪ اﯾﺴﺘﮕﺎه ﻣﺘﻮاﻟﯽ رودﺧﺎﻧﻪ ﻫﻮﺳﺘﻮﻧﯿﮏ، واﻗﻊ در آﻣﺮﯾﮑﺎ ﻣﻮرد ﺑﺮرﺳﯽ ﻗﺮار ﮔﺮﻓﺘﻪ اﺳﺖ. ﺑﺮاي اﯾﻦ ﻣﻨﻈﻮر در ﮔﺎم اول، ﻣﻘﺪار دﺑﯽ اﯾﺴﺘﮕﺎه ﭘﺎﯾﯿﻦدﺳﺖ، ﺗﻮﺳﻂ اﯾﺴﺘﮕﺎهﻫﺎي ﺑﺎﻻدﺳﺖ ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪل رﮔﺮﺳﯿﻮن ﻓﺮاﯾﻨﺪ ﮔﺎوﺳﯽ ﭘﯿﺶﺑﯿﻨﯽ ﺷﺪه اﺳﺖ. ﺳﭙﺲ ﺳﺮيﻫﺎي زﻣﺎﻧﯽ دﺑﯽ و اﺷﻞ ﺗﻮﺳﻂ ﺗﺒﺪﯾﻞ ﻣﻮﺟﮏ و ﺗﺠﺰﯾﮥ ﻣﺪ ﺗﺠﺮﺑﯽ ﯾﮑﭙﺎرﭼﻪ ﺑﻪ زﯾﺮﺳﺮيﻫﺎﯾﯽ ﺗﺠﺰﯾﻪ ﮔﺸﺘﻪ و اﯾﻦ زﯾﺮﺳﺮيﻫﺎ ﺟﻬﺖ ﺷﺒﯿﻪﺳﺎزي راﺑﻄﻪ دﺑﯽ- اﺷﻞ وارد ﻣﺪل رﮔﺮﺳﯿﻮن ﻓﺮاﯾﻨﺪ ﮔﺎوﺳﯽ ﺷﺪﻧﺪ. ﻫﻤﭽﻨﯿﻦ ﺗﺄﺛﯿﺮ ﻫﺮ ﯾﮏ از زﯾﺮﺳﺮيﻫﺎي روش ﺗﺠﺰﯾﮥ ﻣﺪ ﺗﺠﺮﺑﯽ ﯾﮑﭙﺎرﭼﻪ )Residual and IMFs( در ﻧﺘﺎﯾﺞ ﭘﯿﺶﺑﯿﻨﯽ، ﺑﺮرﺳﯽ ﮔﺮدﯾﺪ. ﻣﺸﺎﻫﺪه ﮔﺮدﯾﺪ ﮐﻪ ﻧﺎﮐﺎرآﻣﺪﺗﺮﯾﻦ زﯾﺮﺳﺮي در ﺗﺠﺰﯾﮥ ﻣﺪ ﺗﺠﺮﺑﯽ ﯾﮑﭙﺎرﺟﻪ، زﯾﺮﺳﺮي ﺑﺎﻗﯿﻤﺎﻧﺪه )Residual( ﻣﯽﺑﺎﺷﺪ. ﻧﺘﺎﯾﭻ ﺣﺎﮐﯽ از آن اﺳﺖ ﮐﻪ روشﻫﺎي ﺗﺮﮐﯿﺒﯽ ﻣﻮﺟﮏ )DWT- GPR( و ﺗﺠﺰﯾﮥ ﻣﺪ ﺗﺠﺮﺑﯽ ﯾﮑﭙﺎرﭼﻪ )EEMD- GPR( ﺗﺎ ﺣﺪود زﯾﺎدي ﺑﺎﻋﺚ ﺑﻬﺒﻮد ﻧﺘﺎﯾﺞ ﮔﺮدﯾﺪﻧﺪ. ﺑﻪ ﻋﻨﻮان ﻧﻤﻮﻧﻪ، ﺑﺮاي ﻣﺮﺣﻠﻪ آزﻣﻮن ﻣﺪل ﺑﺮﺗﺮ ﭘﯿﺶﺑﯿﻨﯽ دﺑﯽ اﯾﺴﺘﮕﺎه دوم، ﻣﺪل ﺗﻠﻔﯿﻘﯽ ﺗﺠﺰﯾﻪ ﻣﺪ ﺗﺠﺮﺑﯽ ﯾﮑﭙﺎرﭼﻪ- ﮔﻮﺳﯿﻦ 0/74=DC را ﺑﻪ 0/80=DC و ﻣﺪل ﺗﻠﻔﯿﻘﯽ ﻣﻮﺟﮏ- ﮔﻮﺳﯿﻦ 0/74=DC را ﺑﻪ 0/83=DC ارﺗﻘﺎء داد.

Accurate forecasting of river flow is one of the most important factors in surface water resources management, especially during flood and drought periods. In this research, the wavelet function and the ensemble empirical mode decomposition (EEMD), which are considered as soft computing tools, were used to derive the time series features, and the efficiency of the wavelet- Gaussian and the ensemble empirical mode decomposition-Gaussian models for predicting the discharge between the three consecutive stations located in the Housatonic river have been investigated. For this purpose, in the first step, the discharge of downstream stations is predicted by upstream stations using the Gaussian process regression model. Then, the discharge-stage time series was broken up by wavelet transform and ensemble empirical mode decomposition into cages, and these subclasses were introduced into the Gaussian process regression modeling to simulate the discharge-stage relationship. Also, the effect of each of the sub-series of ensemble empirical mode decomposition model (Residual and IMFs) was studied to improve predictive outcomes. It was observed that the most inefficient subseries in the ensemble empirical mode decomposition model is the residual subseries. The results indicate that wavelet compound techniques (DWT-GPR) and ensemble empirical mode decomposition (EEMD-GPR) have improved the results to a certain extent. As an example, for the test stage, the best prediction model of the second station, the combined model of ensemble empirical mode decomposition-Gaussian upgraded determination coefficient (DC) from 0.74 to 0.80 and the combined model of wavelet-Gaussian upgraded DC from 0.74 to 0.83.