Küllahcı Kübra, Altunkaynak Abdüsselam
Department of Civil Engineering Hydraulics and Water Resources Division, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey.
Environ Sci Pollut Res Int. 2023 Apr;30(20):58090-58108. doi: 10.1007/s11356-023-26598-x. Epub 2023 Mar 28.
Rainfall is a vital process in the hydrological cycle of the globe. Accessing reliable and accurate rainfall data is crucial for water resources operation, flood control, drought warning, irrigation, and drainage. In the present study, the main objective is to develop a predictive model to enhance daily rainfall prediction accuracy with an extended time horizon. In the literature, various methods for the prediction of daily rainfall data for short lead times are presented. However, due to the complex and random nature of rainfall, in general, they yield inaccurate prediction results. Generically, rainfall predictive models require many physical meteorological variables and consist of challenging mathematical processes that require high computational power. Furthermore, due to the nonlinear and chaotic nature of rainfall, observed raw data typically has to be decomposed into its trend cycle, seasonality, and stochastic components before being fed into the predictive model. The present study proposes a novel singular spectrum analysis (SSA)-based approach for decomposing observed raw data into its hierarchically energetic pertinent features. To this end, in addition to the stand-alone fuzzy logic model, preprocessing methods SSA, empirical mode decomposition (EMD), and commonly used discrete wavelet transform (DWT) are incorporated into the fuzzy models which are named as hybrid SSA-fuzzy, EMD-fuzzy, W-fuzzy models, respectively. In this study, fuzzy, hybrid SSA-fuzzy, EMD-fuzzy, and W-fuzzy models are developed to enhance the daily rainfall prediction accuracy and improve the prediction time span up to 3 days via three (3) stations' data in Turkey. The proposed SSA-fuzzy model is compared with fuzzy, hybrid EMD-fuzzy, and widely used hybrid W-fuzzy models in predicting daily rainfall in three distinctive locations up to a 3-day time horizon. Improved accuracy in predicting daily rainfall is provided by the SSA-fuzzy, W-fuzzy, and EMD-fuzzy models compared to the stand-alone fuzzy model based on mean square error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) model assessment metrics. Specifically, the advocated SSA-fuzzy model is found to be superior in accuracy to hybrid EMD-fuzzy and W-fuzzy models in predicting daily rainfall for all time spans. The results reveal that, with its easy-to-use features, the advocated SSA-fuzzy modeling tool in this study is a promising principled method for its possible future implementations not only in hydrological studies but in water resources and hydraulics engineering and all scientific disciplines where future state space prediction of a vague nature and stochastic dynamical system is important.
降雨是全球水文循环中的一个重要过程。获取可靠且准确的降雨数据对于水资源运营、防洪、干旱预警、灌溉和排水至关重要。在本研究中，主要目标是开发一个预测模型，以在更长的时间范围内提高日降雨量预测的准确性。在文献中，提出了各种用于短期提前期的日降雨数据预测方法。然而，由于降雨的复杂和随机性质，总体而言，它们产生的预测结果不准确。一般来说，降雨预测模型需要许多物理气象变量，并且由具有挑战性的数学过程组成，需要高计算能力。此外，由于降雨的非线性和混沌性质，在将观测到的原始数据输入预测模型之前，通常必须将其分解为趋势周期、季节性和随机成分。本研究提出了一种基于奇异谱分析（SSA）的新颖方法，用于将观测到的原始数据分解为其层次化的能量相关特征。为此，除了独立的模糊逻辑模型外，预处理方法SSA、经验模态分解（EMD）和常用的离散小波变换（DWT）被纳入模糊模型中，分别命名为混合SSA - 模糊、EMD - 模糊、W - 模糊模型。在本研究中，开发了模糊、混合SSA - 模糊、EMD - 模糊和W - 模糊模型，以通过土耳其三个站点的数据提高日降雨量预测准确性并将预测时间跨度延长至3天。在所提出的SSA - 模糊模型与模糊、混合EMD - 模糊以及广泛使用的混合W - 模糊模型在预测三个不同地点长达3天时间范围内的日降雨量方面进行了比较。与基于均方误差（MSE）和纳什 - 萨特克利夫效率系数（CE）模型评估指标的独立模糊模型相比，SSA - 模糊、W - 模糊和EMD - 模糊模型在预测日降雨量方面提供了更高的准确性。具体而言，在所倡导的SSA - 模糊模型在预测所有时间跨度的日降雨量方面被发现比混合EMD - 模糊和W - 模糊模型在准确性上更优越。结果表明，凭借其易于使用的特性，本研究中所倡导的SSA - 模糊建模工具不仅在水文研究中，而且在水资源和水利工程以及所有未来状态空间预测具有模糊性质和随机动态系统很重要的科学学科中，对于其未来可能的实施来说是一种有前景的原则性方法。
