Department of Civil Engineering, Razi University, Kermanshah, Iran.
Department of Civil Engineering, Razi University, Kermanshah, Iran; Environmental Research Center, Razi University, Kermanshah, Iran.
J Environ Manage. 2018 Sep 15;222:190-206. doi: 10.1016/j.jenvman.2018.05.072. Epub 2018 May 27.
A novel hybrid approach is presented that can more accurately predict monthly rainfall in a tropical climate by integrating a linear stochastic model with a powerful non-linear extreme learning machine method. This new hybrid method was then evaluated by considering four general scenarios. In the first scenario, the modeling process is initiated without preprocessing input data as a base case. While in other three scenarios, the one-step and two-step procedures are utilized to make the model predictions more precise. The mentioned scenarios are based on a combination of stationarization techniques (i.e., differencing, seasonal and non-seasonal standardization and spectral analysis), and normality transforms (i.e., Box-Cox, John and Draper, Yeo and Johnson, Johnson, Box-Cox-Mod, log, log standard, and Manly). In scenario 2, which is a one-step scenario, the stationarization methods are employed as preprocessing approaches. In scenario 3 and 4, different combinations of normality transform, and stationarization methods are considered as preprocessing techniques. In total, 61 sub-scenarios are evaluated resulting 11013 models (10785 linear methods, 4 nonlinear models, and 224 hybrid models are evaluated). The uncertainty of the linear, nonlinear and hybrid models are examined by Monte Carlo technique. The best preprocessing technique is the utilization of Johnson normality transform and seasonal standardization (respectively) (R = 0.99; RMSE = 0.6; MAE = 0.38; RMSRE = 0.1, MARE = 0.06, UI = 0.03 &UII = 0.05). The results of uncertainty analysis indicated the good performance of proposed technique (d-factor = 0.27; 95PPU = 83.57). Moreover, the results of the proposed methodology in this study were compared with an evolutionary hybrid of adaptive neuro fuzzy inference system (ANFIS) with firefly algorithm (ANFIS-FFA) demonstrating that the new hybrid methods outperformed ANFIS-FFA method.
提出了一种新的混合方法，通过将线性随机模型与强大的非线性极限学习机方法相结合，可以更准确地预测热带气候的月降雨量。该新混合方法然后通过考虑四个一般情况进行评估。在第一种情况下，建模过程在不预处理输入数据的情况下启动，作为基础案例。而在其他三种情况下，使用一步和两步过程使模型预测更加精确。所提到的情况是基于平稳化技术（即差分、季节性和非季节性标准化和频谱分析）和正态变换（即 Box-Cox、John 和 Draper、Yeo 和 Johnson、Johnson、Box-Cox-Mod、对数、对数标准和 Manly）的组合。在第二种情况，即一步情况中，使用平稳化方法作为预处理方法。在第三种和第四种情况下，考虑了正态变换和平稳化方法的不同组合作为预处理技术。总共有 61 个子情况进行评估，产生了 11013 个模型（10785 个线性方法、4 个非线性模型和 224 个混合模型进行评估）。通过蒙特卡罗技术检查线性、非线性和混合模型的不确定性。最佳预处理技术是使用 Johnson 正态变换和季节性标准化（分别）（R=0.99；RMSE=0.6；MAE=0.38；RMSRE=0.1，MARE=0.06，UI=0.03 和 UII=0.05）。不确定性分析的结果表明了所提出的技术的良好性能（d-因子=0.27；95PPU=83.57）。此外，还将本研究中提出的方法的结果与萤火虫算法的自适应神经模糊推理系统（ANFIS）的进化混合方法进行了比较，表明新的混合方法优于 ANFIS-FFA 方法。
