.png){kind=icon}
返回
Modeling monthly evaporation using two different neural computing techniques
.png){kind=icon}
PRE
AI
DOI：10.1007/s00271-009-0158-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Two different artificial neural network (ANN) techniques, multi-layer perceptrons (MLP) and radial basis neural networks (RBNN), are employed in the estimation of monthly pan evaporation. The monthly climatic data, air temperature, solar radiation, wind speed, pressure and humidity, of three stations operated by the U.S. Environmental Protection Agency in California are used as inputs to the ANN models to estimate monthly evaporation. In the first part of the study, the accuracy of MLP and RBNN are compared with each other. The multi-linear regression (MLR) and the Stephens-Stewart (SS) methods are also considered for the comparison. The concern of second part of the study is to investigate the ability of neural computing techniques, MLR and SS models in evaporation estimation using data from nearby station(s). The performances of the models are evaluated using mean square errors, mean absolute relative errors and determination coefficient (R (2)). The effect of periodicity on model's estimation performance is also investigated. Based on the comparisons, it is found that the MLP and RBNN computing techniques could be employed successfully in modeling monthly evaporation process.
Keyword:
REFERENCE EVAPOTRANSPIRATION ESTIMATION
NETWORK MODELS
PAN
PREDICTION
期刊
IF:
3.5
论文数:
1.3K
被引数:
3.1K
机构
暂无机构信息
引用论文
Real-time and offline techniques for identifying obstructive sleep apnea patients用于识别阻塞性睡眠呼吸暂停患者的实时和离线技术
PREAI
分享收藏
分享收藏Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia)热带非洲的湖泊蒸发量估计 (埃塞俄比亚Ziway湖).png)
JOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏
查看更多内容