کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6413554 1629950 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evolutionary neural networks for monthly pan evaporation modeling
ترجمه فارسی عنوان
شبکه های عصبی تکاملی برای مدل سازی تبخیر ماهانه
کلمات کلیدی
تبخیر، مدل سازی، تکامل دیفرانسیل، شبکه های عصبی، عصب فازی، روش استفن استوارت،
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی


- Evolutionary neural networks (ENN) is used for modeling pan evaporation.
- Data from two stations of Turkey are used in the study.
- ENN models are compared with fuzzy genetic, neuro-fuzzy and ANN methods.
- ENN models perform better than the other models.
- Pan evaporation can be successfully estimated by the ENN method.

SummaryEstimating pan evaporation is very important for monitoring, survey and management of water resources. This study proposes the application evolutionary neural networks (ENN) for modeling monthly pan evaporations. Solar radiation, air temperature, relative humidity, wind speed and pan evaporation data from two stations, Antalya and Mersin, in Mediterranean Region of Turkey are used in the study. In the first part of the study, ENN models are compared with those of the fuzzy genetic (FG), neuro-fuzzy (ANFIS), artificial neural networks (ANN) and Stephens-Stewart (SS) methods in estimating pan evaporations of Antalya and Mersin stations, separately. Comparison results indicate that the ENN models generally perform better than the FG, ANFIS, ANN and SS models. In the second part of the study, models are compared with each other in estimating Mersin's pan evaporations using input data of both stations. Results reveal that the ENN models performed better than the FG, ANFIS and ANN models. It was concluded that monthly pan evaporations can be successfully estimated by the ENN method. The performance of the ENN model with full weather data as inputs presents 0.749 and 0.759 mm of mean absolute error for the Antalya and Mersin stations, respectively.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 498, 19 August 2013, Pages 36-45
نویسندگان
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