TY - GEN
T1 - A neural network pattern recognition approach to automatic rainfall classification by using signal strength in LTE/4G networks
AU - Beritelli, Francesco
AU - Capizzi, Giacomo
AU - Sciuto, Grazia Lo
AU - Scaglione, Francesco
AU - Połap, Dawid
AU - Woźniak, Marcin
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Accurate and real time rainfall levels estimations are very useful in various applications of hydraulic structure design, agriculture, weather forecasting, climate modeling, etc. An accurate measurement of rainfall with high spatial resolution is possible with an appropriate positioned set of rainfall gauge, but an alternative method to estimate the rainfall is the analysis of electromagnetic wave, in particular the microwave attenuation. Mainly this is done concerning impact of rain on transmission of electromagnetic waves at the level of radio frequency above 10 GHz. In this paper we investigate a new method to estimate rainfall level using the analysis of received signal strength and its variance in mobile LTE/4G terminal to produce a map of prediction.
AB - Accurate and real time rainfall levels estimations are very useful in various applications of hydraulic structure design, agriculture, weather forecasting, climate modeling, etc. An accurate measurement of rainfall with high spatial resolution is possible with an appropriate positioned set of rainfall gauge, but an alternative method to estimate the rainfall is the analysis of electromagnetic wave, in particular the microwave attenuation. Mainly this is done concerning impact of rain on transmission of electromagnetic waves at the level of radio frequency above 10 GHz. In this paper we investigate a new method to estimate rainfall level using the analysis of received signal strength and its variance in mobile LTE/4G terminal to produce a map of prediction.
KW - LTE
KW - Neural network
KW - Radio signal attenuation
KW - Rainfall estimation
UR - https://www.scopus.com/pages/publications/85022325896
U2 - 10.1007/978-3-319-60840-2_36
DO - 10.1007/978-3-319-60840-2_36
M3 - Conference contribution
AN - SCOPUS:85022325896
SN - 9783319608396
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 505
EP - 512
BT - Rough Sets - International Joint Conference, IJCRS 2017, Proceedings,
A2 - Polkowski, Lech
A2 - Yao, Yiyu
A2 - Artiemjew, Piotr
A2 - Slezak, Dominik
A2 - Ciucci, Davide
A2 - Zielosko, Beata
A2 - Liu, Dun
PB - Springer Verlag
T2 - International Joint Conference on Rough Sets, IJCRS 2017
Y2 - 3 July 2017 through 7 July 2017
ER -