Rainfall estimation through the analysis of its impact on electromagnetic waves has sparked increasing interest in the research community. Recent studies have delved into its effects on cellular network performance, demonstrating the potential to forecast rainfall levels based on electromagnetic wave attenuation during precipitations. This paper aims to solve the problem of identifying the nature of specific weather phenomena from the received signal level (RSL) in 4G/LTE mobile terminals. Specifically, utilizing time-series data representing RSL, we propose a novel approach to encode time series as images and model the task as an image classification problem, which we finally address using convolutional neural networks (CNNs). The main benefit of the abovementioned procedure is the opportunity to utilize various data augmentation techniques simultaneously. This encompasses applying traditional approaches, such as moving averages, to the time series and enhancing the generated images. We have investigated various image data augmentation methods to identify the most effective combination for this scenario. In the upcoming sections, we will introduce the task of rainfall estimation and conduct a comprehensive analysis of the dataset used. Subsequently, we will formally propose a new approach for converting time series into images. To conclude, the paper's final section will present and discuss the experiments conducted, providing the reader with a brief yet comprehensive overview of the results.

翻译：通过分析降雨对电磁波的影响进行降雨量估计，已引起研究界日益浓厚的兴趣。近期研究深入探讨了降雨对蜂窝网络性能的影响，证明了基于降水期间电磁波衰减来预测降雨水平的潜力。本文旨在解决从4G/LTE移动终端接收信号电平（RSL）中识别特定天气现象性质的问题。具体而言，利用代表RSL的时间序列数据，我们提出了一种将时间序列编码为图像的新方法，并将该任务建模为图像分类问题，最终通过卷积神经网络（CNN）进行处理。上述方法的主要优势在于能够同时利用多种数据增强技术。这包括对时间序列应用传统方法（如移动平均）以及对生成图像进行增强。我们研究了多种图像数据增强方法，以确定最适合此场景的有效组合。在后续章节中，我们将介绍降雨量估计任务，并对所用数据集进行全面分析。随后，我们将正式提出一种将时间序列转换为图像的新方法。最后，本文的结论部分将展示并讨论所进行的实验，为读者提供简洁而全面的结果概述。