A Field Experiment of Rainfall Intensity Estimation Based on the Analysis of Satellite-to-Earth Microwave Link Attenuation

Colli, Matteo; Stagnaro, Mattia; Caridi, Andrea; Lanza, Luca G.; Randazzo, Andrea; Pastorino, Matteo; Caviglia, Daniele D.; Delucchi, Alessandro

doi:10.1007/978-3-030-11973-7_17

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The microcontroller samples the RF signals 64 times per minute; at the end of each minute, a UDP packet including the input RF power measurement and some information is sent to a central server for further processing. The block diagram of the system is reported in Figure 2 of [ 26 ], while the SRS sensing board is shown in Figure 3 [ 26 ].…”

Section: Problem Definitionmentioning

confidence: 99%

Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links

Gianoglio

Alyosef

Colli³

et al. 2023

Sensors

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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.

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Section: Problem Definitionmentioning

confidence: 99%

Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links

Gianoglio

Alyosef

Colli³

et al. 2023

Sensors

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show abstract

“…Menggunakan Kalman filter seperti yang ditunjukkan pada gambar 8, untuk penentuan level baseline saat hujan kemudian mengkonversi curah hujan dengan model ITU-R, model tersebut dapat mendeteksi fluktuasi SNR sampai dengan 0.1 dB saat divalidasi terhadap satu kejadian hujan. Colli dkk melakukan estimasi peta hujan [33] dari Smart Rainfall System (SRS) yang merupakan sistem paten jaringan sensor pemantau curah hujan wilayah Genoa, Itali berbasis redaman sinyal yang diterima dari satelit Ku-band. Curah hujan hasil estimasi dengan model ITU-R dari 3 lokasi antena penerima satelit diinterpolasi untuk mendapatkan peta bidang hujan.…”

Section: Gambar 7 Path-averaged Stratiform Rain-induced (Wet Path)unclassified

Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan

Mardyansyah

Kurniawan

Santoso

et al. 2022

EJI

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As a vast archipelagic country with diverse topographic conditions and has an annual average rainfall of more than 2000 mm, Indonesia is prone to hydrometeorological disasters. Based on Indonesia's disaster data, throughout 2021 there were 3,658 incidents of floods and landslides distributed throughout Indonesia. This makes real-time rainfall monitoring with high density indispensable. Indonesia currently has a rainfall monitoring system about 1000 automatic rain gauges, so an increase in the spatial resolution of network is necessary. The increasing density of monitoring equipment using rain gauges and weather radar poses the problem of high procurement and operational costs. Therefore, several alternative rainfall monitoring systems are needed. In this article, we review several studies that focus on the utilization of terrestrial and satellite communication link operating in high frequency bands as an alternative for measuring rainfall. Optimization of the satellite communication system network is more suitable than terrestrial networks to be applied in Indonesia with archipelagic areas because it has a large number of point distributions with wider coverage. The use of artificial intelligence with deep learning techniques such as one dimensional convolutional neural network (1D-CNN) is also very promising to estimate rainfall intensity because it has a high accuracy of 93%..

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“…Smart Rainfall System (SRS) is an innovative, patented technological solution for estimating and pinpointing rainfall in real-time and providing short-term forecasts of hydrogeological risk [10,[13][14][15]. SRS exploits the signal transmitted by commercial geostationary Digital Video Broadcasting Satellites (DVB-S, DVB-S2) to evaluate the quantity of rain falling over the area to be monitored and to provide continuous observation of atmospheric conditions.…”

Section: The Smart Rainfall System (Srs)mentioning

confidence: 99%

Advanced Real-Time Monitoring of Rainfall Using Commercial Satellite Broadcasting Service: A Case Study

Gragnani

Colli

Tavanti

et al. 2021

Sensors

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Correct regulation of meteoric surface and subsurface flow waters is a fundamental goal for the sustainable development of the territories. A new system, aimed at real-time monitoring of the rainfall and of the cumulated rainfall, is introduced and discussed in the present paper. The system implements a Sensor Network based on the IoT paradigm and can cover safety-critical “hot spots” with a relatively small number of sensors, strategically placed, in areas not covered by traditional weather radars and rain gauges, and lowering the costs of deployment and maintenance with respects to these devices. A real application case, based on the implementation of the pilot plant at the Monte Scarpino landfill (Genoa, Italy), is presented and discussed. The system performances are assessed on the basis of comparisons with data provided by a polarimetric weather radar and by a traditional rain gauge.

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A Field Experiment of Rainfall Intensity Estimation Based on the Analysis of Satellite-to-Earth Microwave Link Attenuation

Cited by 3 publications

References 7 publications

Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links

Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links

Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan

Advanced Real-Time Monitoring of Rainfall Using Commercial Satellite Broadcasting Service: A Case Study

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