كاليبراسيون ﻣﺪل توزيعي ﻫﻴﺪروﻟﻮژﻳﻜﻰ WetSpa ﺑﺎ اﺳﺘﻔﺎده ازالگوريتم‌هاي ﺑﻬﻴﻨﻪﺳﺎزى ﭼﻨﺪﻫﺪﻓﻪ عنكبوت بيوه سياه و NSGA-II

Calibration of WetSpa Distributed Hydrological Model using NSGA-II and Black Widow Multi-Objective Optimization Algorithms

اﺳﺘﻔﺎده از ﻣﺪلﻫﺎى ﺑﺎرش-رواﻧﺎب ﻣﻔﻬﻮﻣﻰ ﺑﻪﻋﻨﻮان ﯾﮑﯽ اﺑﺰارﻫﺎى ﺳﺎده و در ﻋﯿﻦ ﺣﺎل ﮐﺎرآﻣﺪ در ﻣﺪلﺳﺎزىﻫﺎى ﻫﯿﺪروﻟﻮژﯾﮑﻰ ﮐﺎرﺑﺮد ﻓﺮاوان دارﻧﺪ. اﯾﻦ ﻣﺪلﻫﺎ ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ اﻃﻼﻋﺎت ورودى از ﻗﺒﯿﻞ ﺑﺎرش، ﺗﺒﺨﯿﺮ- ﺗﻌﺮق و دﻣﺎى اﻧﺪازهﮔﯿﺮى ﺷﺪه و اﻃﻼﻋﺎت ﺗﻮﭘﻮﮔﺮاﻓﻰ ﺣﻮﺿﻪ، رژﯾﻢ ﺟﺮﯾﺎن را ﺑﺎ اﺳﺘﻔﺎده از رواﺑﻂ رﯾﺎﺿﻰ ﺷﺒﯿﻪﺳﺎزى ﻣﻰﮐﻨﻨﺪ. در ﭘﮋوﻫﺶ ﺣﺎﺿﺮ، ﻗﺎﺑﻠﯿﺖ اﻟﮕﻮرﯾﺘﻢﻫﺎي ﺑﻬﯿﻨﻪﺳﺎزى ﻋﻨﮑﺒﻮت ﺑﯿﻮه ﺳﯿﺎه BWO)( و NSGA-II در واﺳﻨﺠﻰ ﻣﺪل ﺗﻮزﯾﻌﯽ ﻫﯿﺪروﻟﻮژﯾﮑﻰ WetSpa ﺑﻪ ﻣﻨﻈﻮر ﺷﺒﯿﻪﺳﺎزى ﺑﺎرش- رواﻧﺎب ﺣﻮﺿﻪ ﮔﺮﮔﺎﻧﺮود ﻣﻮرد ارزﯾﺎﺑﯽ ﻗﺮار ﮔﺮﻓﺘﻪاﺳﺖ. اﻟﮕﻮرﯾﺘﻢﻫﺎي ﺑﻬﯿﻨﻪ ﺳﺎزى ﻓﻮق ﺑﻪ ﺻﻮرت ﭼﻨﺪﻫﺪﻓﻪ ﺑﺮاى واﺳﻨﺠﻰ 11 ﭘﺎراﻣﺘﺮ ﺳﺮاﺳﺮى ﻣﺪل WetSpa اﺳﺘﻔﺎده ﺷﺪﻧﺪ. ﺗﻮاﺑﻊ ﻫﺪف در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪه در اﯾﻦ ﭘﮋوﻫﺶ ﺷﺎﻣﻞ دو ﺷﺎﺧﺺ ﻧﺶ-ﺳﺎﺗﮑﻠﯿﻒ و ﻧﺶ-ﺳﺎﺗﮑﻠﯿﻒﻟﮕﺎرﯾﺘﻤﻰ ﺑﻮده ﺗﺎ ﺑﻪوﺳﯿﻠﻪ آﻧﻬﺎ ﻋﻤﻠﮑﺮد ﻣﺪل در ﭘﯿﺶﺑﯿﻨﻰ دﺑﻰﻫﺎى ﺣﺪاﮐﺜﺮى و ﺣﺪاﻗﻠﻰ ﺑﻬﺒﻮد ﯾﺎﺑﺪ. ﭘﺲ از واﺳﻨﺠﻰ و ﺻﺤﺖﺳﻨﺠﻰ ﻣﺪل، از آن ﺑﺮاى ﺷﺒﯿﻪﺳﺎزى ﺳﯿﻼب در ﯾﮏدوره ﯾﮏ ﺳﺎﻟﻪ در ﺣﻮﺿﻪ ﻣﺬﮐﻮر اﺳﺘﻔﺎده ﮔﺮدﯾﺪ و ﻗﺎﺑﻠﯿﺖ ﻣﺪل ارزﯾﺎﺑﻰ ﺷﺪ. ﻧﺘﺎﯾﺞ ﻧﺸﺎن داد ﮐﻪ اﻟﮕﻮرﯾﺘﻢﻫﺎي ﺑﻬﯿﻨﻪﺳﺎزى BWO و NSGA-II ﺑﺎ ﺿﺮﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ 0/81و 0/69 ﺑﻪﺗﺮﺗﯿﺐ ﻋﻤﻠﮑﺮد ﺧﻮب و ﻗﺎﺑﻞ ﻗﺒﻮﻟﯽ را در واﺳﻨﺠﻰ ﻣﺪل داﺷﺘﻪاﻧﺪ؛ ﺑﻨﺎﺑﺮاﯾﻦ ﻋﻤﻠﮑﺮد اﻟﮕﻮرﯾﺘﻢ ﺑﻬﯿﻨﻪﺳﺎزى BWO ﺑﺴﯿﺎر ﺑﻬﺘﺮ از NSGA-II ارزﯾﺎﺑﯽ ﺷﺪ. ﻫﻤﭽﻨﯿﻦ، آﻧﺎﻟﯿﺰ ﺣﺴﺎﺳﯿﺖ ﭘﺎراﻣﺘﺮﻫﺎى ﻣﻮﺛﺮ ﻧﺸﺎن داد ﮐﻪ ﺿﺮﯾﺐ رواﻧﺎب ﺳﻄﺤﻰ، ﺣﺴﺎسﺗﺮﯾﻦ ﭘﺎراﻣﺘﺮ ﺳﺮاﺳﺮى ﻣﺪل WetSpa ﺑﻮده اﺳﺖ.

Conceptual rainfall-runoff (RR) models are one of the simple and efficient tools in hydrological modeling. These models simulate the flow regime using mathematical equations using input data such as precipitation, evapotranspiration and measured temperature, and basin topographic information. Calibration of RR models, e.g. WetSpa which has been developed in Belgium, is a process in which parameter adjustment are made so as to match the dynamic behavior of the RR model to the observed behavior of the catchment. This research presents an application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Black Widow Optimization (BWO) for multi-objective calibration of WetSpa in Gorganroud river basin, Iran to optimize 11 global parameters of the WetSpa model. The objective functions are Nash–Sutcliffe and logarithmic Nash–Sutcliffe efficiencies in order to improve the model's performance. The WetSpa model then was applied for a period of 1-year flood simulation in the basin and the results were analyzed. Results showed that the evolutionary NSGA-II and BWO algorithms are capable of locating optimal parameter sets in the search space. The measured correlation coefficient in the calibration process was 0.69 and 0.81 for the NSGA-II and BWO algorithms, respectively. Moreover, a sensitivity analysis was conducted on the global parameters in which the surface runoff coefficient was the most sensitive parameter of the model.