基于径向基函数神经网络的太阳辐射两气象参数同步反演算法

Nicholas W. Nzala, Nicolausi Ssebiyonga, Dennis Muyimbwa, Taddeo Ssenyonga
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引用次数: 0

摘要

当地气象参数是了解洪水、干旱等极端天气情况发生频率的关键。在这项研究中,我们提出了一种同时检索两个天气参数的方法。该方法基于2011 - 2016年已有的天气参数月平均值,对前馈径向基函数神经网络(RBFNN)进行训练,得到一种快速准确的计算特定天气参数对全球太阳辐射的方法。在反演模型中,采用一种多维无约束非线性优化方法检索天气参数对。利用Makerere大学物理系(0.31°N, 32.58°E, 1200 m)测量的天气参数数据对新方法进行了验证,并使用统计工具对该方法的性能进行了评估。在前馈人工神经网络(ANN)中,相关系数(R)、平均偏置误差(MnB)、均方根误差(RMSE)和平均百分比误差(MAPE)分别在0.80 ~ 0.95、-0.0011 ~ 0.0077、0.55 ~ 1.04和2.49% ~ 5.82%之间。日照时数、相对湿度和最低气温对的相关系数最高,为0.95。在逆向人工神经网络(ANNi)中,R、MnB、RMSE和MAPE分别在0.3 ~ 0.9、0.01 ~ 0.08、0.51 ~ 11.14和2.1% ~ 14.5%之间。日照时数和相对湿度的相关系数最高,分别为0.92和0.62。该方法有助于在缺乏测量设备的地方或在测量设备故障的日子里获得天气参数数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Radial Basis Function Neural Network Algorithm for the Simultaneous Retrieval of Two Meteorological Parameters from Solar Radiation
Local meteorological parameters are key in understanding the frequency of occurrence of extreme weather conditions such as floods, and droughts, among others. In this study, we present a method for simultaneous retrieval of two weather parameters. The method is based on already measured monthly average values of weather parameters from 2011 to 2016, which were used to train a Feed-forward radial basis function neural network (RBFNN) to obtain a fast and accurate method to compute global solar radiation for specified weather parameters pair. In inverse modelling, a multidimensional unconstrained non-linear optimization was employed to retrieve the weather parameters pair. The new approach was validated using weather parameter data measured at the Department of Physics, Makerere University (0.31° N, 32.58° E, 1200 m). Statistical tools were used to evaluate the method's performance. In the Feed-forward artificial neural network (ANN), the correlation coefficient (R), mean bias error (MnB), root mean square error (RMSE), and mean percentage error (MAPE) were in the ranges 0.80-0.95, -0.0011-0.0077, 0.55-1.04 and 2.49%-5.82%, respectively. The pairs (sunshine hours, relative humidity) and (sunshine hours, minimum temperature) had the highest correlation coefficient of 0.95. In the inverse artificial neural network (ANNi), the R, MnB, RMSE and MAPE were in the ranges 0.3-0.9, 0.01-0.08, 0.51-11.14 and 2.1%-14.5%, respectively. The pair (sunshine hours, relative humidity) had the highest correlation coefficients of 0.92 and 0.62, respectively. The method helps in obtaining weather parameter data sets in places where measuring equipment is lacking or during days when measuring equipment malfunctions.
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