پديد آورندگان :
پوررﻏﻼم آﻣﯿﺠﯽ، ﻣﺴﻌﻮد دانشگاه تهران - پرديس كشاورزي و منابع طبيعي - دانشكده مهندسي و فناوري كشاورزي - گروه مهندسي آبياري و آباداني , اﺣﻤﺪاﻟﯽ، ﺧﺎﻟﺪ دانشگاه تهران - دانشكده منابع طبيعي - گروه احياء مناطق خشك و كوهستاني , ﻟﯿﺎﻗﺖ، ﻋﺒﺪاﻟﻤﺠﯿﺪ دانشگاه تهران - گروه مهندسي آبياري و آباداني
كليدواژه :
آزﻣﻮن ﮔﺎﻣﺎ , اﻟﮕﻮرﯾﺘﻢ ژﻧﺘﯿﮏ , ﺗﭙﻪﻧﻮردي , ﺗﻌﺒﯿﻪ ﮐﺎﻣﻞ , آﺑﯿﺎري ﺗﺤﺖﻓﺸﺎر
چكيده فارسي :
ﺗﺨﺼﯿﺺ ﺑﻮدﺟﻪ ﺑﻪ ﺳﺎﻣﺎﻧﻪﻫﺎي آﺑﯿﺎري ﺗﺤﺖﻓﺸﺎر در ﮐﻨﺎر ﺑﺮرﺳﯽ ﮐﺎراﯾﯽ آنﻫﺎ، ﯾﮑﯽ از ﻣﻮﺿﻮﻋﺎﺗﯽ اﺳﺖ ﮐﻪ در ﺳﺎلﻫﺎي اﺧﯿﺮ ﻣﻮرد ﺗﻮﺟﻪ ﻣﺘﺨﺼﺼﺎن ﺻﻨﻌﺖ آب و ﻣﺴﺌﻮﻻن اﻣﺮ ﻣﯽ ﺑﺎﺷﺪ. ﺑﺮاي اﯾﻦ ﻣﻨﻈﻮر، ﺷﻨﺎﺳﺎﯾﯽ ﻋﻮاﻣﻞ و اﺟﺰاي ﻣﺆﺛﺮ ﺑﺮ ﻫﺰﯾﻨﻪ ﻫﺎي ﯾﮏ ﺳﺎﻣﺎﻧﻪ آﺑﯿﺎري، ﻣﻮﺿﻮع ﺑﺴـﯿﺎر ﻣﻬﻤـﯽ اﺳـﺖ ﮐـﻪ ﮐﻤﺘﺮ ﻣﻮرد ﺗﻮﺟﻪ ﻗﺮار ﮔﺮﻓﺘﻪ اﺳﺖ. از ﻃﺮﻓﯽ ﯾﮑﯽ از ﻣﺸﮑﻼت اﺳﺎﺳﯽ ﻣﺪﻟﺴﺎزي دادهﻣﺒﻨﺎ، ﺗﻌﯿﯿﻦ ﻣﺆﺛﺮﺗﺮﯾﻦ ﻣﺘﻐﯿﺮﻫﺎي ورودي در ﺗﺨﻤﯿﻦ ﯾﮏ ﺧﺮوﺟﯽ ﻣﻌﯿﻦ اﺳﺖ. آزﻣﻮن ﮔﺎﻣﺎ ﯾﮑﯽ از ﻣﻬﻢ ﺗﺮﯾﻦ اﺑﺰارﻫﺎﯾﯽ اﺳﺖ ﮐﻪ ﻣﯽ ﺗﻮاﻧﺪ ﺑﺮاي ﺗﺤﻠﯿﻞ ﺣﺴﺎﺳﯿﺖ و اﻧﺘﺨﺎب ﻣﻬﻢ ﺗﺮﯾﻦ وﯾﮋﮔﯽ از ﺑـﯿﻦ ﺗﻌـﺪ اد زﯾـﺎدي از وﯾﮋﮔـﯽ ﻫـﺎي ﺗﺄﺛﯿﺮﮔﺬار ﺑﺮ ﺧﺮوﺟﯽ اﺳﺘﻔﺎده ﺷﻮد. ﺑﻨﺎﺑﺮاﯾﻦ اﯾﻦ ﭘﮋوﻫﺶ ﺑﺎ ﻫﺪف ﺗﻌﯿﯿﻦ ﻣﺆﺛﺮﺗﺮﯾﻦ وﯾﮋﮔﯽ ﻫﺎ ﺑﺮ ﻫﺰﯾﻨﻪ ﺳﺎﻣﺎﻧﻪﻫﺎي آﺑﯿﺎري ﻗﻄـﺮه اي در ﭼﻬـﺎر ﺑﺨـﺶ ﺷـﺎﻣﻞ ﻫﺰﯾﻨﻪ اﯾﺴﺘﮕﺎه ﭘﻤﭙﺎژ و ﺳﯿﺴﺘﻢ ﮐﻨﺘﺮل ﻣﺮﮐﺰي )TCP(، ﻫﺰﯾﻨﻪ ﻟﻮازم داﺧﻞ ﻣﺰرﻋﻪ )TCF(، ﻫﺰﯾﻨﻪ ﻧﺼﺐ و اﺟـﺮاي داﺧـﻞ ﻣﺰرﻋـﻪ و اﯾﺴـﺘﮕﺎه ﭘﻤﭙـﺎژ )TCI( و ﻫﺰﯾﻨﻪ ﮐﻞ )TCT( اﻧﺠﺎم ﺷﺪ. اﺑﺘﺪا داده ﻫﺎ و اﻃﻼﻋﺎت 100 ﭘﺮوژه آﺑﯿﺎري ﻗﻄﺮهاي اﺟﺮاﺷﺪه در ﻧﻘﺎط ﻣﺨﺘﻠﻒ ﮐﺸﻮر، ﺟﻤـﻊ آوري ﮔﺮدﯾـﺪ و ﺑﺎﻧـﮏ اﻃﻼﻋـﺎﺗﯽ ﺷﺎﻣﻞ 39 ﻣﺘﻐﯿﺮ ﻣﻬﻢ و ﺗﺄﺛﯿﺮﮔﺬار در ﻫﺰﯾﻨﻪ ﺑﺨﺶﻫﺎي ذﮐﺮﺷﺪه، ﺗﻬﯿﻪ ﺷﺪ. ﺑﺮاي ﺗﺤﻠﯿﻞ ﺣﺴﺎﺳﯿﺖ و اﻧﺘﺨﺎب ﻣﺘﻐﯿﺮﻫﺎي ورودي اﺛﺮﮔﺬار ﺑﺮ ﻫﺰﯾﻨﻪ ﺑﺨﺶﻫـﺎي ﻣﺨﺘﻠﻒ ﺳﺎﻣﺎﻧﻪﻫﺎي آﺑﯿﺎري ﻗﻄﺮه اي از ﻧﺮم اﻓﺰار winGamma اﺳﺘﻔﺎده ﺷﺪ. ﺑﺮ اﺳﺎس ﺗﺤﻠﯿﻞ ﺣﺴﺎﺳﯿﺖ اﻧﺠﺎم ﺷﺪه، ﺑﻬﺘﺮﯾﻦ ﻣﻌﯿﺎرﻫﺎي ارزﯾﺎﺑﯽ در ﺑﺨﺶ TCP
ﺑﻪ دﺳﺖ آﻣﺪ و ﻣﻘﺪار ﻋﺪدي آﻣﺎره ﮔﺎﻣﺎ، ﺧﻄﺎي ﻣﻄﻠﻖ ﻗﺎﺑﻞ اﻧﺘﻈﺎر )Expected Absolute Error(، آﻣﺎره ﮔﺮادﯾﺎن )Gradient(، ﺧﻄﺎي اﺳﺘﺎﻧﺪارد آﻣﺎره ﮔﺎﻣﺎ
2
)Standard Error of 𝛤(، ﺿﺮﯾﺐ ﺗﺒﯿﯿﻦ )R ( و ﺷﺎﺧﺺ V-Ratio ﺑﻪ ﺗﺮﺗﯿﺐ ﺑﺮاﺑﺮ ﺑﺎ 0/87 ،0/024 ،0/008 ،0/219 ،0/048 و 0/192 ﺛﺒﺖ ﺷﺪ ﮐـﻪ اﯾـﻦ
ﺑﯿﺎﻧﮕﺮ ﻫﻤﺒﺴﺘﮕﯽ ﺑﺎﻻي ﻣﺘﻐﯿﺮﻫﺎي ﻣﻮرد ﻣﻄﺎﻟﻌﻪ ﺑﺎ ﻫﺰﯾﻨﻪ ﺑﺨﺶ ﻣﺬﮐﻮر اﺳﺖ. ﺑﺮاي ﯾﺎﻓﺘﻦ ﺗﺮﮐﯿﺐ ﺑﻬﯿﻨﻪ از داده ﻫﺎ ﺑـﺮاي ﻣـﺪل ﺳـﺎزي ﻫﺰﯾﻨـﻪ ، از ﺳـﻪ روش اﻟﮕﻮرﯾﺘﻢ ژﻧﺘﯿﮏ )(GA، ﺗﭙﻪﻧﻮردي (HC) و ﺗﻌﺒﯿﻪ ﮐﺎﻣﻞ (FE) اﺳﺘﻔﺎده ﺷﺪ. ﻧﺘﺎﯾﺞ اﯾﻦ ﺑﺨﺶ ﻧﺸﺎن داد ﮐﻪ ﺗﻌﺪاد ﻣﺘﻐﯿﺮﻫﺎي ﻣﻮرد ﻧﯿﺎز و ﺗﺮﮐﯿﺐ ﺑﻬﯿﻨﻪ ورودي ﮐﻪ در روش ﻫﺎي GA و HC ﺑﻪ ﺗﺮﺗﯿﺐ ﺣﺪود 40 درﺻﺪ و 90 درﺻﺪ از ﻣﺘﻐﯿﺮﻫﺎ )ﺑﻪ ﺗﺮﺗﯿﺐ 16 و 35 ﻣﺘﻐﯿﺮ( در اﻣﺮ ﻣﺪل ﺳﺎزي دﺧﯿﻞ ﺑﻮدﻧﺪ، در روش FE ﺑﻪ 20 درﺻﺪ رﺳﯿﺪه و ﻓﻘﻂ ﻫﺸﺖ ﻣﺘﻐﯿﺮ ﺑﺮاي ﻣﺪل ﺳﺎزي ﻫﺰﯾﻨﻪ اﻧﺘﺨﺎب ﺷﺪه اﺳﺖ ﮐﻪ ﻧﺘﺎﯾﺞ اﯾﻦ روش ﺑﻪ ﻋﻨﻮان ﻣﺪل ﺑﺮﺗﺮ اﻧﺘﺨﺎب ﮔﺮدﯾـﺪ. ﻫﻤﭽﻨـﯿﻦ ﻧﺘﯿﺠـﻪ ﻣﺪل ﻫﯿﺒﺮﯾﺪ ﻧﺸﺎن داد زﻣﺎﻧﯽ ﮐﻪ از ﭘﻨﺞ ﻣﺘﻐﯿﺮ QT (l/s) )ﻣﻘﺪار ﮐﻞ دﺑﯽ آب ﻗﺎﺑﻞ دﺳﺘﺮس(، )SR (m )ﻓﺎﺻﻠﻪ ردﯾﻒ ﮔﯿﺎﻫﺎن(، QE (l/s) )دﺑﯽ ﮔﺴﯿﻠﻨﺪه(، T )(h )ﺗﻌﺪاد ﺳﺎﻋﺎت ﮐﺎري در ﺷﺒﺎﻧﻪ روز( و )NIT (n )ﺗﻌﺪاد ﻧﻮﺑﺖ ﻫﺎي آﺑﯿﺎري( ﺑﻪ ﻋﻨﻮان ﺗﺮﮐﯿﺐ ﺑﻬﯿﻨﻪ ورودي ﺑﺮاي ﻣﺪل ﺳـﺎزي ﻫﺰﯾﻨـﻪ ﺳـﺎﻣﺎﻧﻪ ﻫـﺎي آﺑﯿـﺎري ﻗﻄﺮه اي اﺳﺘﻔﺎده ﺷﻮد، ﺳﺎده ﺗﺮﯾﻦ و ﺑﻬﯿﻨﻪﺗﺮﯾﻦ ﻣﺪل ﺑﻪ دﺳﺖ ﻣﯽآﯾﺪ. ﺑﻨﺎﺑﺮاﯾﻦ ﻧﺘﺎﯾﺞ اﯾﻦ ﻣﻄﺎﻟﻌﻪ ﻣﯽ ﺗﻮاﻧﺪ در ﺷﻨﺎﺳﺎﯾﯽ ﻣﺘﻐﯿﺮﻫـﺎي ﺗﺄﺛﯿﺮﮔـﺬار در ﻫﺰﯾﻨـﻪ ﻫـﺎي ﺳﺎﻣﺎﻧﻪﻫﺎي آﺑﯿﺎري ﺗﺤﺖ ﻓﺸﺎر و ﻧﻬﺎﯾﺘﺎً ﻣﺪل ﺳﺎزي اﻗﺘﺼﺎدي اﯾﻦ ﺳﺎﻣﺎﻧﻪ ﻫﺎ اﺳﺘﻔﺎده ﮔﺮدد.
چكيده لاتين :
, this study aims to determine the most effective features on the cost of drip irrigation systems in four parts, including the Cost of pumping station and central control system (TCP), Cost of on-farm equipment (TCF), Cost of installation and operation on-farm and pumping station (TCI) and Total cost (TCT). First, data of 100 drip irrigation projects implemented in different parts of the country were collected and it was prepared a database containing 39 important and influential variables in the cost of the mentioned parts. Based on the sensitivity analysis, the best evaluation criteria were obtained in TCP and the numerical amount of gamma statistic, Expected Absolute Error, Gradient statistic, Standard Error of Γ, coefficient of determination (R2), and V-Ratio index were recorded as 0.048, 0.219, 0.008, 0.024, 0.87 and 0.192, respectively, which indicate the high correlation between the experimental variables and the cost of the corresponded sector. To find the optimal combination of data for cost modeling, we used Genetic Algorithm (GA), Hill Climbing (HC), and Full Embedding (FE). The results showed that the number of required variables and the optimal input combination, which covered 40 and 90% of the variables (16 and 35 variables, respectively) in GA and HC method reached 20% in the FE method and only eight variables were selected for cost modeling and also the results of this method were selected as the superior model. Moreover, the result of the hybrid model revealed the simplest and most optimal model was obtained when QT (l/s) (total amount of available water flow), SR (m) (plant row spacing), QE (l/s) (emitter flow), T (h) (number of working hours per day) and NIT (n) (number of irrigation shifts) were used as the optimal input combination to modeling the cost of drip irrigation systems.
