نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشگاه بوعلی همدان
2 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان
3 گروه آمار، دانشکده علوم پایه، دانشگاه پیام نور، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Accurately estimating the suspended sediment load of a river is crucial for the study and development of water resource systems. However, direct measurement of sediment load is expensive and time-consuming. Therefore, developing alternative models, such as gauge curves or simulation models, is recommended. This study employed two methods to predict the river's suspended sediment load. The first method used a combination of a support vector machine (SVM) model and a refrigeration algorithm (SA), while the second method used a gauge curve and a modified FAO sediment measurement method. To validate the modeling results, water flow and suspended sediment load data from Yelfan River between 2006-2014 were used. Normalizing the input data to the SVM model improved the standard error index (SE) by 9.93% in the modeling test section. Additionally, modifying the input pattern from single input-single output (SISO) to multiple input-single output (MISO) improved this index by 1.03%. After optimizing the calibration curve coefficients and the FAO correction method using the general reduced gradient (GRG), the SE index improved by 10.13% and 73.19%, respectively. Among the reviewed methods, the SVM model based on the normalized multi-input-single-output model was found to be the best method for predicting the suspended sediment load of the river.
کلیدواژهها [English]
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