Merabet Khaled, Heddam Salim
Laboratory of Optimizing Agricultural Production in Subhumid Zones (LOPAZS), Faculty of Science, Agronomy Department, University 20 Août 1955-Skikda, Route El Hadaik, BP 26, Skikda, Algeria.
Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology (LRIBEB), Faculty of Science, Agronomy Department, University 20 Août 1955-Skikda, Route El Hadaik, BP 26, Skikda, Algeria.
Environ Sci Pollut Res Int. 2023 May;30(21):60868-60889. doi: 10.1007/s11356-023-26779-8. Epub 2023 Apr 12.
This paper proposes a hybrid air relative humidity prediction based on preprocessing signal decomposition. New modelling strategy was introduced based on the use of the empirical mode decomposition, variational mode decomposition, and the empirical wavelet transform, combined with standalone machine learning to increase their numerical performances. First, standalone models, i.e., extreme learning machine, multilayer perceptron neural network, and random forest regression, were used for predicting daily air relative humidity using various daily meteorological variables, i.e., maximal and minimal air temperatures, precipitation, solar radiation, and wind speed, measured at two meteorological stations located in Algeria. Second, meteorological variables are decomposed into several intrinsic mode functions and presented as new input variables to the hybrid models. The comparison between the models was achieved based on numerical and graphical indices, and obtained results demonstrate the superiority of the proposed hybrid models compared to the standalone models. Further analysis revealed that using standalone models, the best performances are obtained using the multilayer perceptron neural network with Pearson correlation coefficient, Nash-Sutcliffe efficiency, root-mean-square error, and mean absolute error of approximately ≈0.939, ≈0.882, ≈7.44, and ≈5.62 at Constantine station, and ≈0.943, ≈0.887, ≈7.72, and ≈5.93 at Sétif station, respectively. The hybrid models based on the empirical wavelet transform decomposition exhibited high performances with Pearson correlation coefficient, Nash-Sutcliffe efficiency, root-mean-square error, and mean absolute error of approximately ≈0.950, ≈0.902, ≈6.79, and ≈5.24, at Constantine station, and ≈0.955, ≈0.912, ≈6.82, and ≈5.29, at Sétif station. Finally, we show that the new hybrid approaches delivered high predictive accuracies of air relative humidity, and it was concluded that the contribution of the signal decomposition was demonstrated and justified.
本文提出了一种基于预处理信号分解的混合空气相对湿度预测方法。基于经验模态分解、变分模态分解和经验小波变换的使用，引入了新的建模策略，并结合独立的机器学习方法来提高其数值性能。首先，使用独立模型，即极限学习机、多层感知器神经网络和随机森林回归，利用位于阿尔及利亚的两个气象站测量的各种每日气象变量，即最高和最低气温、降水量、太阳辐射和风速，来预测每日空气相对湿度。其次，将气象变量分解为几个本征模态函数，并作为新的输入变量呈现给混合模型。基于数值和图形指标对模型进行了比较，结果表明所提出的混合模型优于独立模型。进一步分析表明，使用独立模型时，在君士坦丁站，使用多层感知器神经网络获得的最佳性能，其皮尔逊相关系数、纳什-萨特克利夫效率、均方根误差和平均绝对误差分别约为≈0.939、≈0.882、≈7.44和≈5.62；在塞提夫站，分别约为≈0.943、≈0.887、≈7.72和≈5.93。基于经验小波变换分解的混合模型表现出高性能，在君士坦丁站，皮尔逊相关系数、纳什-萨特克利夫效率、均方根误差和平均绝对误差分别约为≈0.950、≈0.902、≈6.79和≈5.24；在塞提夫站，分别约为≈0.955、≈0.912、≈6.82和≈5.29。最后，我们表明新的混合方法提供了高空气相对湿度预测精度，并得出结论，信号分解的贡献得到了证明和验证。
