###### Abstract  
In the climate change scenario the world is facing, extreme weather events can lead to increasingly serious disasters. To improve managing the consequent risks, there is a pressing need to have real-time systems that provide accurate monitoring and possibly forecasting which could help to warn people in the affected areas ahead of time and save them from hazards. The oblique earth-space links (OELs) have been used recently as a method for real-time rainfall detection. This technique poses two main issues related to its indirect nature. The first one is the classification of rainy and non-rainy periods. The second one is the determination of the attenuation baseline, which is an essential reference for estimating rainfall intensity along the link. This work focuses mainly on the first issue. Data referring to eighteen rain events were used and have been collected by analyzing a satellite-to-earth link quality and employing a tipping bucket rain gauge (TBRG) properly positioned, used as reference. It reports a comparison among the results obtained by applying four different machine learning (ML) classifiers, namely the support vector machine (SVM), neural network (NN), random forest (RF), and decision tree (DT). Various data arrangements were explored, using a preprocessed version of the TBRG data, and extracting two different sets of characteristics from the microwave link data, containing 6 or 12 different features, respectively. The achieved results demonstrate that the NN classifier has outperformed the other classifiers. s 202320232023  
rainfall and, finally, they compared the estimated rainfall intensity of the proposed system with the measurements that came from the gauge. Horizontal microwave links (HML) have been utilized recently in many applications such as regional rainfall monitoring [5] and rain intensity inversion [6; 7]. HMLs have the ability to monitor precipitation in urban areas [8] and they have formed an important part that supplements the present measurement method. Measuring precipitation can be performed using oblique earthquake links (OELs) [9]. In [10], authors used OELs to retrieve the rain intensity in Paris. A system for real-time rainfall estimation based on the measurements of the carrier to the ratio in the broadband satellite networks and artificial neural networks was proposed by [11]. Since OEL passes through the whole troposphere, there are more complicated factors that affect OEL compared to HML. Different atmospheric features such as turbulence, cloud, and gasses lead to two crucial issues in OELs. The first is the recognition of rainy and non-rainy periods rejecting variations in the signal intensity due to other factors, and the second is the determination of the signal baseline, i.e., the power level received with clear skies, from whose value it is possible to calculate the amount of attenuation induced by the presence of rain [12]. The importance of the second issue comes from the fact that determining the baseline is crucial for getting the attenuation produced by rainfall. The authors of [13] proposed a method for estimating the baseline based on the minimal attenuation values. Such a method does not require the classification of the rainy and non-rainy periods. The interpolation for the signal before and after precipitation has been used to obtain the attenuation baseline for the rainy periods in many works [14; 15]. The slow Kalman tracker provides the last dry baseline and it is used as an initial reference for baseline determination [16]. Many researchers have utilized machine learning techniques to solve the first issue, and those techniques outperformed the traditional ones. A method for identifying the rainy period depending on a metric related to the receiver bit error rate parameter which is provided by satellite receivers has been implemented by [PERSON] et al. [17]. An approach for identifying whether a given period is rainy or not using long short-term memory (LSTM) architecture has been proposed by [18].