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首页> 外文期刊>Meteorology and Atmospheric Physics >Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron
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Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron

机译：与多层感知器相比，交互式神经模糊推理系统在锅蒸发建模中的实用性

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摘要

Estimation of pan evaporation (E (pan)) using black-box models has received a great deal of attention in developing countries where measurements of E (pan) are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E (pan) for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E (pan) predictions (r = 0.97, RMSE = 0.81 mm day(-1), MAE = 0.63 mm day(-1) and PE = 0.58 %). The results also showed that the most accurate E (pan) predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E (pan) process.
机译：使用黑匣子模型估算锅蒸发量（E（锅​​））在发展中国家（E和锅的测量在时间和空间上受到限制）受到了广泛的关注。多层感知器（MLP）和主动神经模糊推理系统（CANFIS）模型用于预测伊朗半干旱地区的每日E（泛）。开发了六个MLP和CANFIS模型，包括每日气象参数的各种组合。使用相关系数（r），均方根误差（RMSE），平均绝对误差（MAE）和估计误差百分比（PE）来测试模型的性能。发现具有动量学习算法和Tanh激活函数的MLP6模型（需要所有输入参数）呈现出最准确的E（平移）预测值（r = 0.97，RMSE = 0.81 mm天（-1），MAE = 0.63毫米天（-1）和PE = 0.58％）。结果还表明，使用Takagi-Sugeno-Kang（TSK）模糊模型和高斯隶属函数，可以使用CANFIS模型获得最准确的E（pan）预测。总体性能显示，MLP方法比CANFIS方法更适合用于E（平移）过程建模。

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