کاربرد سه مدل هوشمند در برآورد بار معلق حوضه‌های آبخیز (مطالعه موردی: حوضه آبخیز دره‌رود، استان اردبیل)

تخمین بار معلق در طیف وسیعی از مسائل از قبیل طراحی مخازن سدها، برآورد میزان فرسایش و رسوب گذاری اطراف پایه های پل و مدیریت حوضه های آبخیز به کار گرفته می شود. در این پژوهش به منظور تخمین بار معلق حوضه دره رود، مقادیر دبی و بار معلق در 16 ایستگاه هیدرومتری طی دوره مشترک آماری 15 ساله (94-1380) جمع آوری گردید. پنج الگوی مختلف بر اساس میزان تاثیرگذاری متغیرهای دبی و خصوصیات فیزیوگرافی زیرحوضه ها شامل مساحت، شیب، ضریب شکل و شماره منحنی بر بار معلق حوضه تعریف شد. ضمناً با در نظر گرفتن پارامترهای مساحت و شیب، زیرحوضه ها به دو گروه اول و دوم تقسیم بندی شدند. عملکرد مدل های شبکه عصبی مصنوعی (ann)، سامانه استنتاجی فازی عصبی تطبیقی (anfis) و برنامه ریزی بیان ژن (gep) در پیش بینی بار معلق مورد بررسی قرار گرفت. نتایج نشان داد تخمین بار معلق با به کارگیری الگوی ترکیبی شامل کلیه خصوصیات فیزیوگرافی و دبی با بیشترین دقت همراه بود. در بین مدل های هوشمند بهترین عملکرد متعلق به مدل gep بود. در گروه اول، این مدل بیشترین ضریب تعیین (0.68=r2)، کمترین مقدار ریشه میانگین مربعات خطا ( ton/day7.69=rmse) و بیشترین ضریب نش-ساتکلیف (0.55=ns) را در مقایسه با سایر مدل ها به خود اختصاص داد. در خصوص گروه دوم نیز مدل gep با دارا بودن مقادیر r2، rmse و ns به ترتیب برابر با 0.72، 975.26 و 0.43 برتری محسوسی داشت. با استفاده از مدل gep برای گروه های اول و دوم مدل های منطقه ای رسوب استخراج شد. طبق نتایج، طی سال های 941380 سالانه به طور میانگین 6.33 میلیون تن رسوبات معلق توسط شبکه آبراهه ها در کل حوضه دره رود جابه جا شده و به طور متوسط سهم هر کیلومتر مربع حوضه، حدود 1000 تن بوده است.

Application of Three Intelligent Models in Estimation of Watersheds Suspended Load (Case Study: Dareh-Roud Watershed, Ardabil Province)

Introduction: Suspended load estimation is utilized to study and investigate many problems of water engineering sciences such as dam reservoir design, transportation of sediments and pollution in the rivers, creation of stable channels, estimation of erosion and sedimentation around bridge piers, and watershed management. The purpose of this study was to estimate the suspended load in the DarehRoud watershed in Ardabil province using the rivers discharge values and the physiographic characteristics of the subbasins. Moreover, annual suspended load and sediment specific discharge were calculated for the whole of the watershed. Materials and Methods: In this study, the DarehRoud watershed in Ardebil province was considered as the study area. The flow discharge and suspended load data were collected from 16 hydrometric stations with a statistical period of 15 years from 20012015. The physiographic characteristics of subbasins, including area (A), slope (S), shape factor (Sf), and curve number (CN), were achieved using ArcGIS and WMS. Five different input combinations were defined based on the effect of flow discharge variables and physiographic properties on the suspended load. Also, considering the area and slope parameters, the subbasins were divided into two groups (i.e., the first and second groups). The performance of dataintelligent models, including Artificial Neural Networks (ANN), Adaptive NeuralFuzzy Interference System (ANFIS), and Gene Expression Programming (GEP) models were investigated in the predict of the suspended load in the study area. Several statistical indicators, including determination coefficient (R2), root mean square error (RMSE), and Nash Sutcliffe efficiency (NS), were utilized to evaluate the model’s efficiency. Results and Discussion: According to the results, estimation of suspended load without using the physiographic characteristics resulted in a high error, and in contrast, the suspended load estimation was most accurate by using a combined scenario involving all physiographic aspects and flow discharge. The scatterplots indicated that in the first group, the points were concentrated around the 1:1 axis for the values of less than 20 (ton/day). However, for the greater amounts, the scattering of issues around the onetoone line was not appropriate, which means that the models were in the condition of underestimation. Similar conditions were observed for the second group, the excellent dispersion was seen for the values of less than 1000 (ton/day), and in general, the models had underestimation conditions. However, in both groups, the dispersion of the GEP model was somewhat better than the other models. Based on the values of R2 and NS, ANN and ANFIS models had the acceptable and satisfactory accuracy for the first group. The GEP model was more reliable and efficient in estimating the suspended load of the first group. On the other hand, the efficiency of ANN and ANFIS was not acceptable for the second group. Comparison of the results of different models using the best input combination indicated that the GEP model with the highest determination coefficient (R2 = 0.68), the lowest root mean square error (RMSE = 7.69 ton/day). The NS equal to 0.55 in the validation step has shown better performance than the other models in estimating the suspended load for the first group. Similarly, for the second group, the GEP model with the highest determination coefficient (R2 = 0.72), the lowest root means square error (RMSE = 975.26 ton/day). The NS equal to 0.43 in the validation step has shown better performance than other models in estimating the suspended load. Conclusion: In the present study, the efficiency of different intelligent models was investigated in the suspended load estimation of Darehroud watershed. In this regard, an extended period (i.e., during 15 years) of measured data, including flow discharge and sediment at the hydrometric stations located on the mentioned watershed, were used. In order to simulate the suspended load, five different input combinations were considered. For all models, the accuracy of suspended load estimation was improved by combining the physiographic characteristics and discharge values. Due to the higher accuracy of the GEP model, regional sediment models were achieved for the first and second groups, separately. Also, annual suspended load and sediment specific discharge were calculated for all subbasins. According to the results, most of the suspended load of the DarehRoud watershed is produced and transported in its old rivers (i.e., DarehRoud and QarahSu). Based on the results of this research, in the DarehRoud watershed, 6.33 million tons of suspended sediments were transported during 20012015.