کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7210901 | 1469241 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Computational approaches for annual maximum river flow series
ترجمه فارسی عنوان
رویکردهای محاسباتی برای سری جریانهای حداکثر رودخانه سالانه
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی (عمومی)
چکیده انگلیسی
Studies of annual peak discharge and its temporal variations are widely used in the planning and decision making process of water resources management. Very recently, soft computing techniques are gaining ground for time series analysis of hydrological events such as rainfall and runoff. In this study Artificial Neural Network (ANN) has been used in combination with wavelet to model the annual maximum flow discharge of rivers. The results of ANN-Wavelet (WANN) model indicate overall low coherence (R2Â =Â 0.39) better than ANN (R2Â =Â 0.31) in isolation. In the present analysis, the authors also conceded a probabilistic distributional analysis of river flow time series which has greater potential to better reflect peak flow dynamics. The results highlight that the overall performance of probability distribution models is superior to WANN model. Instead of that WANN is better than probabilistic models to find the global maxima of the series.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ain Shams Engineering Journal - Volume 8, Issue 1, March 2017, Pages 51-58
Journal: Ain Shams Engineering Journal - Volume 8, Issue 1, March 2017, Pages 51-58
نویسندگان
Harinarayan Tiwari, Subash Pd. Rai, Nayan Sharma, Dheeraj Kumar,