کالیبراسیون ﻣﺪل توزیعی ﻫﻴﺪروﻟﻮژﻳﻜﻰ wetspa ﺑﺎ اﺳﺘﻔﺎده ازالگوریتم‌های ﺑﻬﻴﻨﻪﺳﺎزى ﭼﻨﺪﻫﺪﻓﻪ عنکبوت بیوه سیاه و nsga-ii

استفاده از ﻣﺪلﻫﺎى ﺑﺎرش-رواﻧﺎب ﻣﻔﻬﻮﻣﻰ به عنوان یکی اﺑﺰارﻫﺎى ﺳﺎده و در ﻋﻴﻦ ﺣﺎل ﻛﺎرآﻣﺪ در ﻣﺪلﺳﺎزىﻫﺎى ﻫﻴﺪروﻟﻮژﻳﻜﻰ کاربرد فراوان دارند. اﻳﻦ ﻣﺪلﻫﺎ ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ اﻃﻼﻋﺎت ورودى از ﻗﺒﻴﻞ ﺑﺎرش، ﺗﺒﺨﻴﺮ-ﺗﻌﺮق و دﻣﺎى اﻧﺪازهﮔﻴﺮى ﺷﺪه و اﻃﻼﻋﺎت ﺗﻮﭘﻮﮔﺮاﻓﻰ ﺣﻮﺿﻪ، رژﻳﻢ ﺟﺮﻳﺎن را ﺑﺎ اﺳﺘﻔﺎده از رواﺑﻂ رﻳﺎﺿﻰ ﺷﺒﻴﻪﺳﺎزى ﻣﻰﻛﻨﻨﺪ. در پژوهش حاضر، ﻗﺎﺑﻠﻴﺖ اﻟﮕﻮرﻳﺘﻢهای ﺑﻬﻴﻨﻪﺳﺎزى عنکبوت بیوه سیاه(bwo) و nsga-ii در واﺳﻨﺠﻰ ﻣﺪل توزیعی ﻫﻴﺪروﻟﻮژﻳﻜﻰ wetspa ﺑﻪ ﻣﻨﻈﻮر ﺷﺒﻴﻪﺳﺎزى ﺑﺎرش رواﻧﺎب ﺣﻮﺿﻪ گرگانرود مورد ارزیابی قرار گرفتهاست. اﻟﮕﻮرﻳﺘﻢهای ﺑﻬﻴﻨﻪﺳﺎزى ﻓﻮق ﺑﻪ ﺻﻮرت ﭼﻨﺪﻫﺪﻓﻪ ﺑﺮاى واﺳﻨﺠﻰ ١١ ﭘﺎراﻣﺘﺮ ﺳﺮاﺳﺮى ﻣﺪل wetspa اﺳﺘﻔﺎده ﺷﺪند. ﺗﻮاﺑﻊ ﻫﺪف در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪه در اﻳﻦ پژوهش ﺷﺎﻣﻞ دو ﺷﺎﺧﺺ ﻧﺶ-ﺳﺎﺗﻜﻠﻴﻒ و ﻧﺶ-ﺳﺎﺗﻜﻠﻴﻒﻟﮕﺎرﻳﺘﻤﻰ بوده ﺗﺎ ﺑﻪوﺳﻴﻠﻪ آﻧﻬﺎ ﻋﻤﻠﻜﺮد ﻣﺪل در ﭘﻴﺶﺑﻴﻨﻰ دﺑﻰﻫﺎى ﺣﺪاﻛﺜﺮى و ﺣﺪاﻗﻠﻰ ﺑﻬﺒﻮد ﻳﺎﺑﺪ. ﭘﺲ از واﺳﻨﺠﻰ و ﺻﺤﺖ‌ﺳﻨﺠﻰ ﻣﺪل، از آن ﺑﺮاى ﺷﺒﻴﻪﺳﺎزى ﺳﻴﻼب در ﻳﻚدوره ﻳﻚ ﺳﺎﻟﻪ در ﺣﻮﺿﻪ ﻣﺬﻛﻮر اﺳﺘﻔﺎده ﮔﺮدﻳﺪ و ﻗﺎﺑﻠﻴﺖ ﻣﺪل ارزﻳﺎﺑﻰ ﺷﺪ ﻧﺘﺎﻳﺞ ﻧﺸﺎن داد ﻛﻪ اﻟﮕﻮرﻳﺘﻢهای ﺑﻬﻴﻨﻪﺳﺎزى bwo و nsga-iiﺑﺎ ﺿﺮﻳﺐ همبستگی 0.81 و 0.69 بهترتیب ﻋﻤﻠﻜﺮد خوب و قابل قبولی را در واﺳﻨﺠﻰ ﻣﺪل داﺷﺘﻪاﻧﺪ. بنابراین عملکرد اﻟﮕﻮرﻳﺘﻢ ﺑﻬﻴﻨﻪﺳﺎزىbwo بسیار بهتر از nsga-ii ارزیابی شد. ﻫﻤﭽﻨﻴﻦ، آﻧﺎﻟﻴﺰ ﺣﺴﺎﺳﻴﺖ ﭘﺎراﻣﺘﺮﻫﺎى ﻣﻮﺛﺮ ﻧﺸﺎن داد ﻛﻪ ﺿﺮﻳﺐ رواﻧﺎب ﺳﻄﺤﻰ، ﺣﺴﺎسﺗﺮﻳﻦ ﭘﺎراﻣﺘﺮ ﺳﺮاﺳﺮى ﻣﺪل wetspa ﺑﻮده اﺳﺖ.

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

Conceptual rainfallrunoff (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 Nondominated Sorting Genetic Algorithm II (NSGAII) and Black Widow Optimization (BWO) for multiobjective 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 1year flood simulation in the basin and the results were analyzed. Results showed that the evolutionary NSGAII 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 NSGAII 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.