Artificial neural network models of daily pan evaporation
Artificial neural network models of daily pan evaporation
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DOI：
10.1061/(asce)1084-0699(2006)11:1(65
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发表时间：
2006-01-01
影响因子：
2.4
通讯作者：
Terzi, Ö
中科院分区：
文献类型：
--
作者：
Keskin, ME;Terzi, Ö
人工神经网络（ANN）模型被提出作为一种替代方法的蒸发估算埃格迪尔湖。本研究有三个目的：（1）开发人工神经网络模型，以估计每日蒸发皿蒸发量从实测气象数据;（2）比较人工神经网络模型和彭曼模型;（3）评估人工神经网络模型的潜力。利用2001 ~ 2002年埃格迪尔湖490个逐日气象资料建立了蒸发皿日蒸发量估算模型。测量的气象变量包括空气和水温、日照时数、太阳辐射、气压、相对湿度和风速的每日观测。Penman方法和人工神经网络模型的结果进行了比较，蒸发皿蒸发值。比较表明，日蒸发皿蒸发量的ANN估计值与实测值之间的一致性优于其他模型。
Artificial neural network (ANN) models are proposed as an alternative approach of evaporation estimation for Lake Egirdir. This study has three objectives: (1) to develop ANN models to estimate daily pan evaporation from measured meteorological data; (2) to compare the ANN models to the Penman model; and (3) to evaluate the potential of ANN models. Meteorological data from Lake Egirdir consisting of 490 daily records from 2001 to 2002 are used to develop the model for daily pan evaporation estimation. The measured meteorological variables include daily observations of air and water temperature, sunshine hours, solar radiation, air pressure, relative humidity, and wind speed. The results of the Penman method and ANN models are compared to pan evaporation values. The comparison shows that there is better agreement between the ANN estimations and measurements of daily pan evaporation than for other model.