## 5 Conclusions  
In this work, four different machine learning-based classification techniques were applied to classify rainy and non-rainy periods on different events using a rain intensity threshold of 0.1 mm/min. The features for the dataset have been extracted from the data obtained from a smart rainfall system (SRS) installation, with a dish diameter of 85 cm pointed to the Turksat 42\({}^{\circ}\) E constellation. Reference rain measurements from an improved TBRG have been used to obtain the class label for this work. Eighteen days  
Figure 5: Day 17 classified by NN trained with 12 features (**a**) and with 6 features (**b**). The blue dots refer to not-rainy observations correctly classified, the purple ones are not-rainy observations classified as rain, the green ones are rainy observations correctly classified, and the red dots represent rainy observations classified as not rain. The cyan bars represent the TBRG measurements different from 0 mm/h, i.e., associated with the rain observations. (**a**) Day 17 Neural Network 12 Features. (**b**) Day 17 Neural Network 6 Features.  
of observations were collected in a dataset, using iteratively one day for testing and the remaining seventeen for the training of four algorithms. From the SRS signals, 12 features were extracted, and a reduced subset (i.e., 6 features) for the sake of comparison has been selected. The results show that the best trade-off evaluated on the 18 days in terms of specificity, recall, and F1-score, is achieved by the neural network classifier for both the feature sets. This work paves the way for developing a fully automatic real-time rainfall monitoring system, with a dense population of sensors deployed over the territory, where the rain discrimination algorithm is hosted at the edge, by devices with constrained resources, both on the computational and the energy requirements point of view. In this way, it will be possible to reduce the data transfer to the server on the cloud, as well as the cost of the service, to the essentials, just when rainfall occurs. Further developments will concern the deployment of the classification algorithm on an embedded device for the continuous monitoring of precipitations. Moreover, for continuous rainfall monitoring and because of the seasonality of the data, it will be necessary to fine-tune the classifier online after a period of observations.  
Conceptualization, C.G., A.A., M.C., S.Z. and D.D.C.; methodology, C.G., A.A., M.C., S.Z. and D.D.C.; software, C.G.; validation, C.G.; formal analysis, C.G. and A.A.; investigation, C.G.; resources, M.C., S.Z. and D.D.C.; data curation, M.C. and S.Z.; writing--original draft preparation, A.A.; writing--review and editing, C.G. and D.D.C.; visualization, C.G. and A.A.; supervision, M.C., S.Z. and D.D.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability Statement: Data are publicly available at: [[https://github.com/cosmiclabunige/Rainfall_Prediction_18_days](https://github.com/cosmiclabunige/Rainfall_Prediction_18_days)]([https://github.com/cosmiclabunige/Rainfall_Prediction_18_days](https://github.com/cosmiclabunige/Rainfall_Prediction_18_days)) (accessed on 18 January 2023).  
We thank the Hydraulic Laboratory of the University of Genova, DICCA department, and WMO-CIMO Lead Centre \"[PERSON]\" on Precipitation Intensity for the TBRG measurements; we also acknowledge [PERSON]--CNR-IMAA and CETEMPS, L'Aquila, Italy and [PERSON]--University of Pisa, Computer Engineering Department--for useful discussion and suggestions.  
The authors declare no conflict of interest.