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Data pre-processing effect on ANN-based prediction intervals construction of the evaporation process at different climate regions in Iran
Journal of Hydrology ( IF 6.4 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.jhydrol.2020.125078
Vahid Nourani , Mina Sayyah-Fard , Mohammad Taghi Alami , Elnaz Sharghi

Abstract The point predictions of stochastic processes, such as evaporation, by data-driven methods such as Artificial Neural Network (ANN), are associated with uncertainties. Furthermore, the performance of data-driven models, as well as their uncertainty, are dependent on the quality and quantity of the used data. The main aim of this research was the Uncertainty Quantifying (UQ) of the ANN-based evaporation predictions by Prediction Intervals (PIs) analysis using the data from three stations in Iran (i.e. Tabriz, Urmia, and Ardabil). In this way, data pre-processing methods i.e. Wavelet-based De-noising (WD), training with Jitted Data (JD) and also their combination i.e. Hybrid Wavelet De-noising and Jitted Data (HWDJD) were applied to examine their effects on the estimated values of PIs. The Lower-Upper-Bound Estimation (LUBE) method as the direct NN-based PI construction was utilized for estimating the PI values. Since the efficiency of any ANN model and consequently, the robustness of the uncertainty analysis is sensitive to the correct selection of input variables, the first order Partial Derivation (PaD) sensitivity analysis method was also used to select dominant inputs among all potential input variables. The results indicated that the LUBE technique could provide acceptable results in estimating the uncertainty bounds, whereas the uncertainty quantity would be affected by the used data pre-processing methods. The reduction of uncertainty effect via data pre-processing methods was significant in modeling Urmia and Ardabil stations. Results showed that the reduction of PI bandwidths via HWDJD, WD, and JD methods were up to 30%, 21%, and 9%, respectively. It means that to reduce the ANN-based modeling uncertainty due to the uncertainty involved in the input data set, at first, the contaminant noise of the available data should be eliminated from the time series, then artificially generated time series (JD) that mimic the smoothed time series pattern can be generated and used in the training process.

中文翻译:

数据预处理对伊朗不同气候区蒸发过程基于ANN的预测区间构建的影响

摘要 通过人工神经网络 (ANN) 等数据驱动方法对蒸发等随机过程的点预测与不确定性相关。此外,数据驱动模型的性能及其不确定性取决于所用数据的质量和数量。本研究的主要目的是使用伊朗三个站点(即大不里士、乌尔米亚和阿尔达比勒)的数据,通过预测间隔 (PI) 分析对基于 ANN 的蒸发预测进行不确定性量化 (UQ)。通过这种方式,应用数据预处理方法,即基于小波的去噪(WD),使用抖动数据(JD)进行训练以及它们的组合,即混合小波去噪和抖动数据(HWDJD)来检查它们对PI 的估计值。下限估计 (LUBE) 方法作为直接基于 NN 的 PI 构造用于估计 PI 值。由于任何 ANN 模型的效率以及不确定性分析的稳健性对输入变量的正确选择很敏感,因此还使用一阶偏导数 (PaD) 灵敏度分析方法在所有潜在输入变量中选择主要输入。结果表明LUBE技术在估计不确定度界限时可以提供可接受的结果,而不确定度数量会受到所使用的数据预处理方法的影响。通过数据预处理方法减少不确定性效应对 Urmia 和 Ardabil 站建模具有重要意义。结果表明,通过 HWDJD、WD、和 JD 方法分别高达 30%、21% 和 9%。这意味着为了减少由于输入数据集涉及的不确定性而导致的基于人工神经网络的建模不确定性,首先应从时间序列中消除可用数据的污染噪声,然后人工生成模拟的时间序列(JD)可以生成平滑的时间序列模式并在训练过程中使用。
更新日期:2020-09-01
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