كليدواژه :
الگوريتم تكاملي , بهينه¬سازي , رسوب¬دهي , شبيه¬سازي آبخيز , منحني سنجه
چكيده فارسي :
تعیین مقدار رسوب حوضه و توزیع مكانی آن با اندازه¬گیری¬های میدانی در عمل با چالش جدی مواجه است. از این رو، مدل¬های مختلف، به¬منظور شبیه¬سازی فرسایش خاك و برآورد رسوب استفاده می¬شود. مدل SWAT یكی از مدل¬های هیدرولوژیكی نیمه¬فیزیكی و نیمه¬توزیعی است كه در سال‌های اخیر كاربرد گسترده¬ای داشته است؛ اما شبیه¬سازی رسوب این مدل نسبت به دبی جریان با خطای زیادی همراه می¬باشد كه بخشی از آن به دلیل استفاده از روش¬های تجربی هم‌چون منحنی سنجه رسوب برای برآورد رسوب می¬باشد. از این¬رو، در این تحقیق برای شبیه¬سازی رواناب و رسوب در حوزه سد فریمان از قابلیت¬های الگوریتم ژنتیك به¬منظور بهینه¬سازی رابطه دبی– رسوب استفاده گردید. بدین منظور مسئله بهینه¬سازی الگوریتم ژنتیك به¬صورت یك فضای جستجو از مقادیر پیوسته ضرایب رابطه دبی- رسوب، در نرم¬افزار MATLAB برنامه¬نویسی شد و ضرایب بهینه تعیین گردید. نتایج تحقیق، عملكرد بهتر الگوریتم ژنتیك در برآورد رسوب با ضریب نش- ساتكلیف 46/0، ضریب تعیین 72/0 و ریشه میانگین مربعات خطا 9/70 نسبت به منحنی¬سنجه را نشان می¬دهد. از این رو داده¬های حاصل از این روش برای آنالیز حساسیت، واسنجی و اعتبارسنجی مدل SWAT استفاده گردید. در ارزیابی مدل، ضریب نش¬ساتكلیف دبی ماهانه برای دوره 7 ساله واسنجی و دوره 3 ساله اعتبارسنجی به ترتیب 75/0 و 85/0 و برای رسوب ماهانه به ترتیب 73/0 و 76/0 به¬دست آمد. نتایج تحقیق بیانگر كارایی خوب مدل SWAT در شبیه¬سازی رواناب و رسوب می¬باشد، همچنین استفاده از الگوریتم ژنتیك به منظور بهینه¬سازی رابطه دبی– رسوب نقش مهمی در تعیین مقادیر رسوب و دقت شبیه¬سازی مدل داشته است.
چكيده لاتين :
Introduction: Determining the amount of watershed sedimentation and its spatial distribution by using field measurements in practice faces a serious challenge. In recent decades, hydrological models have been widely used by hydrologists and water resource managers as a tool for analysing water resource management systems. The SWAT model is one of the semi-physical and semi-distributed hydrological models that have been widely used in recent years. Despite the wide use of the SWAT, simulation of the sediment has been associated with a large error in comparison to flow. These errors may come from using empirical methods such as the sediment rating curve for estimating sediment based on measured data. Therefore, in this research, the capabilities of the genetic algorithm (GA) were used to optimize the relationship between discharge and sediment and further optimal equation used for calibration and validation of the model.
Materials and Methods: The studied area is Fariman dam watershed with an area of 278.8 km2 which is located at latitude of 35 ˚ 33 ʹ to 35˚ 41 ʹ and longitude of 59 ˚ 34 ʹ to 59 ˚ 44 ʹ in Razavi Khorasan province. In this study, SWAT model was used to simulate runoff and sediment yield of Fariman dam watershed. In order to run the model, meteorological and hydrometric data including daily rainfall and maximum and minimum temperatures and sediment yield and discharge data, soil and land use maps of the watershed were achieved from relevant resources. The capabilities of the genetic algorithm were used to optimize the discharge -sediment relationship and were compared with sediment rating curve. For this purpose, optimization problem was defined for the genetic algorithm in MATLAB software as a search space of continuous values of the discharge –sediment coefficients. After that, sediment yield was extracted based on discharge data and calculated monthly sediment for SWAT calibration and validation. Sensitivity analysis, calibration and validation of the model were performed using the SUFI-2 algorithm using SWAT-CUP software. For this purpose using high sensitive parameters, the model was calibrated and validated for the period of 1991 to 2000.
Results and Discussion: Optimal coefficients extracted by GA indicate a better performance of the genetic algorithm in estimating the sediment yield. The comparative results of the sediment estimation models, revealed better performance of the genetic algorithm with RMSE = 70.9, NSE =0.46 and R2= 0.72 than the sediment rating curve. According to senetivity analysis of SWAT model, twelve parameters for stream flow and seven parameters for sediment yield were found to be sensitive. The most sensitive parameters for stream flow were SCS runoff curve number (CN2), effective hydraulic conductivity in tributary channel (CH_K1) and base flow alpha factor for bank storage (ALPHA_BNK) and the most sensitive parameters for sediment yield were peak rate adjustment factor for sediment routing, USLE equation soil erodibility factor (USLE_K), sediment concentration in lateral flow and groundwater flow (LAT_SED) and exponent parameter for calculating sediment reentrained in channel sediment routing (SPEXP). The SWAT calibration and validation results showed that the Nash-Sutcliffe efficiency index for monthly sediment and discharge for calibration period was 0.75 and 0.73, respectively and in the validation period was 0.85 and 0.76, respectively. Calibration and validation of the SWAT model was done with genetic algorithm model as an optimal method for deriving sediment data from measured daily discharge. The Nash-Sutcliffe efficiency coefficient for monthly discharge was 0.75 and 0.85 in the calibration and validation periods. Nash-Sutcliffe efficiency coefficients for monthly sediment yield were 0.86 and 0.81 for the same periods. SWAT evaluation results indicate that the model simulation is acceptable for predicting sediment yield and river flow. The performance of SWAT model in predicting of sediment in low flow is poor, which can be due to the effect of the parameters and model simplifications in the simulation of the sediment load.
Conclusions: In this research, simulation of runoff and sediment flow for Fariman dam watershed was performed using SWAT model. For this purpose, the capabilities of the genetic algorithm were used to optimize the relationship between discharge and sediment yields; then the results were used to calibrate and validate the SWAT model. The results indicate that genetics algorithm can be used for optimizing coefficient of sediment discharge equation and the result is better than sediment rating curve. Simulation of watershed hydrology using SWAT shows that the capability of the model in prediction of sediment yield and water flow is good. Using genetic algorithm to optimize the relationship between discharge and sediment has an important role in extracting daily sediment yield and simulation accuracy of the model. Also, the use of evolutionary algorithms can have a significant role in extracting the discharge -sediment relations, which usually is accompanied with a large error in experimental models such as a sediment rating curve.
