پديد آورندگان :
ﺳﯿﺪي، ﻧﻌﯿﻤﻪ داﻧﺸﮕﺎه ﻋﻠﻮم ﮐﺸﺎورزي و ﻣﻨﺎﺑﻊ ﻃﺒﯿﻌﯽ ﺳﺎري - داﻧﺸﮑﺪه ﻣﻬﻨﺪﺳﯽ زراﻋﯽ - ﮔﺮوه ﻣﻬﻨﺪﺳﯽ آب، ﺳﺎري، اﯾﺮان , ﻓﻀﻞاوﻟﯽ، راﻣﯿﻦ داﻧﺸﮕﺎه ﻋﻠﻮم ﮐﺸﺎورزي و ﻣﻨﺎﺑﻊ ﻃﺒﯿﻌﯽ ﺳﺎري - داﻧﺸﮑﺪه ﻣﻬﻨﺪﺳﯽ زراﻋﯽ - ﮔﺮوه ﻣﻬﻨﺪﺳﯽ آب، ﺳﺎري، اﯾﺮان , ﻣﺴﻌﻮدﯾﺎن، ﻣﺤﺴﻦ داﻧﺸﮕﺎه ﻋﻠﻮم ﮐﺸﺎورزي و ﻣﻨﺎﺑﻊ ﻃﺒﯿﻌﯽ ﺳﺎري - داﻧﺸﮑﺪه ﻣﻬﻨﺪﺳﯽ زراﻋﯽ - ﮔﺮوه ﻣﻬﻨﺪﺳﯽ آب، ﺳﺎري، اﯾﺮان , ﮐﯿﺎء، ﻋﯿﺴﯽ ﺳﺎزﻣﺎن ﺗﺤﻘﯿﻘﺎت، آﻣﻮزش و ﺗﺮوﯾﺞ ﮐﺸﺎورزي - ﻣﺮﮐﺰ ﺗﺤﻘﯿﻘﺎت و آﻣﻮزش ﮐﺸﺎورزي و ﻣﻨﺎﺑﻊ ﻃﺒﯿﻌﯽ ﻣﺎزﻧﺪران - ﺑﺨﺶ ﺗﺤﻘﯿﻘﺎت ﺣﻔﺎﻇﺖ ﺧﺎك و آﺑﺨﯿﺰداري، ﺳﺎري، اﯾﺮان
كليدواژه :
ﺗﺒﺨﯿﺮ روزاﻧﻪ , ﺗﺒﺨﯿﺮ ﻣﺎﻫﺎﻧﻪ , ﺗﺸﺖ ﺗﺒﺨﯿﺮ , ﺳﻠﯿﻤﺎنﺗﻨﮕﻪ , ﺳﺎﺧﺘﺎر ﻣﺪل
چكيده فارسي :
ﺗﺒﺨﯿﺮ ﯾﮑﯽ از ﻣﺆﻟﻔﻪﻫﺎي اﺻﻠﯽ ﭼﺮﺧﻪي آب در ﻃﺒﯿﻌﺖ اﺳﺖ ﮐﻪ ﻧﻘﺸﯽ اﺳﺎﺳﯽ در ﻣﻄﺎﻟﻌﺎت ﮐﺸﺎورزي، ﻫﯿﺪروﻟﻮژي، ﻫﻮاﺷﻨﺎﺳﯽ، ﺑﻬﺮهﺑﺮداري از ﻣﺨﺎزن، ﻃﺮاﺣﯽ ﺳﯿﺴﺘﻢﻫﺎي آﺑﯿﺎري و زﻫﮑﺸﯽ، زﻣﺎنﺑﻨﺪي آﺑﯿﺎري و ﻣﺪﯾﺮﯾﺖ ﻣﻨﺎﺑﻊ آب اﯾﻔﺎ ﻣﯽﮐﻨﺪ. در اﯾﻦ ﭘﮋوﻫﺶ ﺗﺮﮐﯿﺐﻫﺎي ﻣﺘﻨﻮﻋﯽ از ﻫﺸﺖ ﻣﺘﻐﯿﺮ ﻫﻮاﺷﻨﺎﺳﯽ ﺑﻪﻋﻨﻮان ورودي ﻣﺪل ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ ﺑﺮاي ﭼﻬﺎر اﯾﺴﺘﮕﺎه ﻫﻮاﺷﻨﺎﺳﯽ اﻃﺮاف ﺳﺪ ﺷﻬﯿﺪ رﺟﺎﯾﯽ ﺷﻬﺮﺳﺘﺎن ﺳﺎري ﻃﯽ ﯾﮏ دوره 10ﺳﺎﻟﻪ ﻣﻮرد ﺑﺮرﺳﯽ ﻗﺮار ﮔﺮﻓﺖ. ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ از ﺷﺎﺧﺺﻫﺎي آﻣﺎري ﻣﺪلﻫﺎ، دﯾﺎﮔﺮام ﭘﺮاﮐﻨﺶ و ﻣﯿﺰان ﺗﺒﺨﯿﺮ روزاﻧﻪ ﺑﺮآورد ﺷﺪه و ﻣﺸﺎﻫﺪاﺗﯽ ﻧﺸﺎن داد ﮐﻪ در ﻣﺠﻤﻮع روش ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﺗﻮاﻧﺴﺘﻪ اﺳﺖ ﺗﺒﺨﯿﺮ روزاﻧﻪ در ﭼﻬﺎر اﯾﺴﺘﮕﺎه ﻣﻮرد ﻣﻄﺎﻟﻌﻪ را ﺑﺎ دﻗﺖ ﺧﻮﺑﯽ ﺑﺮآورد ﮐﻨﺪ. ﺑﺎ اﯾﻦﺣﺎل ﺑﻬﺘﺮﯾﻦ ﺳﺎﺧﺘﺎر ﻣﺪلﻫﺎي ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﺑﺮاي ﭼﻬﺎر اﯾﺴﺘﮕﺎه ﺳﻠﯿﻤﺎن ﺗﻨﮕﻪ، ﻣﺤﻮﻃﻪ اداره ﺳﺎري، ﻓﺮﯾﻢ ﺻﺤﺮا و ﺗﻠﻤﺎدره ﺑﺎ ﻫﻔﺖ ﻣﺘﻐﯿﺮ ورودي، ﯾﮏ ﻻﯾﻪ ﭘﻨﻬﺎن و ﺑﻪ ﺗﺮﺗﯿﺐ 10 ،8 ،12 و 12 ﻧﺮون ﺑﺮﺣﺴﺐ ﻣﻌﯿﺎرﻫﺎي MSE و R2 اﻧﺘﺨﺎب ﺷﺪﻧﺪ. ﺿﺮﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ در اﯾﺴﺘﮕﺎهﻫﺎي ﺳﻠﯿﻤﺎنﺗﻨﮕﻪ، ﻣﺤﻮﻃﻪ اداره ﺳﺎري، ﻓﺮﯾﻢ ﺻﺤﺮا و ﺗﻠﻤﺎدره ﺑﻪﺗﺮﺗﯿﺐ ﺑﺮاﺑﺮ ﺑﺎ 0/92 ،0/91 ،0/88 و 0/89 ﺑﺮاي دادهﻫﺎي روزاﻧﻪ ﺑﻪدﺳﺖ آﻣﺪ. ﻫﻤﭽﻨﯿﻦ ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ از ﺷﺒﯿﻪ ﺳﺎزي ﺗﺒﺨﯿﺮ ﻣﺎﻫﺎﻧﻪ ﻧﺸﺎن داد ﮐﻪ روش ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﻣﺼﻨﻮﻋﯽ ﺗﻮاﻧﺴﺘﻪ اﺳﺖ ﺑﺎ دﻗﺖ ﺧﻮﺑﯽ ﺗﺒﺨﯿﺮ ﻣﺎﻫﺎﻧﻪ را ﺑﺎ ﺿﺮﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ ﺑﻪﺗﺮﺗﯿﺐ 0/99 ،0/98 ،0/98 و 0/99 ﺑﺎ ﺳﻄﺢ اﻋﺘﻤﺎد 95 درﺻﺪ ﺑﻪﺗﺮﺗﯿﺐ ﺑﺮاي اﯾﺴﺘﮕﺎهﻫﺎي ﺳﻠﯿﻤﺎنﺗﻨﮕﻪ، ﻣﺤﻮﻃﻪ اداره ﺳﺎري، ﻓﺮﯾﻢ ﺻﺤﺮا و ﺗﻠﻤﺎدره ﺑﺮآورد ﻧﻤﺎﯾﺪ.
چكيده لاتين :
Evaporation is one of the main components of the water cycle in nature,
which plays a key role in agricultural studies, hydrology, meteorology,
reservoir operation, irrigation and drainage systems design, irrigation
scheduling and water resources management. In this study, eight types of
meteorological parameters as inputs for estimating evaporation from the
pan by artificial neural network for four meteorological stations around
Shahid Rajaei Dam were investigated. Meteorological data were collected
for ten years from 4 stations around Shahid Rajaei Dam. The results of
statistical criteria of the models, distribution diagram and daily
evaporation rate were estimated and observations showed that the neural
network method was able to estimate the daily evaporation in the four
stations with good accuracy. However, the best structure of neural
network models for stations of Soleiman Tangeh, Sari office, Farim Sahra
and Telamadreh with seven input variables, one hidden layer and 12, 8,
10 and 12 neurons, respectively, were selected according to MSE and R2
criteria. MSE and R2 criteria were selected. The correlation coefficients
for daily data in Soleiman tangeh, Sari office, Farim Sahra and Telmadreh
stations were extracted 0.88, 0.91, 0.92 and 0.89, respectively. Also, the
results of monthly evaporation simulation showed that the artificial neural
network method was able to calculate the monthly evaporation with
correlation coefficients of 0.98, 0.98, 0.99 and 0.99 with 95% confidence
level for Soleiman Tangeh, Sari office, Farim Sahra and Telamadreh
stations, respectively.
