Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan
Keywords:
Microwave Link, Rainfall Estimation, Artificial Intelligent
Abstract
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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References
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[26] K. Song, X. Liu, M. Zou, D. Zhou, H. Wu, and F. Ji, “Experimental Study of Detecting Rainfall Using Microwave Links: Classification of Wet and Dry Periods,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 5264–5271, 2020, doi: 10.1109/JSTARS.2020.3021555.
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[35] E. Adirosi et al., “Evaluation of Rainfall Estimation Derived From Commercial Interactive DVB Receivers Using Disdrometer, Rain Gauge, and Weather Radar,” IEEE Trans. Geosci. Remote Sens., pp. 1–14, 2020, doi: 10.1109/TGRS.2020.3041448.
[36] M. Colli et al., “Rainfall Fields Monitoring Based on Satellite Microwave Down-Links and Traditional Techniques in the City of Genoa,” IEEE Trans. Geosci. Remote Sens., vol. 58, no. 9, pp. 6266–6280, 2020, doi: 10.1109/TGRS.2020.2976137.
[37] F. Giannetti, M. Moretti, R. Reggiannini, A. Vaccaro, S. Scarfone, and A. Ortolani, “On the Opportunistic use of Commercial Ku and Ka Band Satcom Networks for Rain Rate Estimation: Potentials and Critical Issues,” in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), 2020, pp. 9011–9015, doi: 10.1109/ICASSP40776.2020.9053668.
[38] G. L. Gragnani, M. Colli, E. Tavanti, and D. D. Caviglia, “Advanced real-time monitoring of rainfall using commercial satellite broadcasting service: a case study,” Sensors, vol. 21, p. 691, 2021, doi: 10.3390/s21030691.
[39] Y. Zhao, X. Liu, M. Xian, and T. Gao, “Statistical Study of Rainfall Inversion Using the Earth-Space Link at the Ku Band: Optimization and Validation for 1 Year of Data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 9486–9494, 2021, doi: 10.1109/JSTARS.2021.3111336.
[40] L. Barthès and C. Mallet, “Rainfall measurement from the opportunistic use of an Earth-space link in the Ku band,” Atmos. Meas. Tech., vol. 6, no. 8, pp. 2181–2193, 2013, doi: 10.5194/amt-6-2181-2013.
[41] A. Gharanjik, M. R. Bhavani Shankar, F. Zimmer, and B. Ottersten, “Centralized Rainfall Estimation Using Carrier to Noise of Satellite Communication Links,” IEEE J. Sel. Areas Commun., vol. 36, no. 5, pp. 1065–1073, 2018, doi: 10.1109/JSAC.2018.2832798.
[42] A. Gharanjik, K. V. Mishra, B. Shankar, and B. Ottersten, “Learning-Based Rainfall Estimation via Communication Satellite Links,” 2018 IEEE Stat. Signal Process. Work. SSP 2018, pp. 115–119, 2018, doi: 10.1109/SSP.2018.8450726.
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[44] K. V. Mishra, B. S. M. R., and B. Ottersten, “Deep Rainrate Estimation from Highly Attenuated Downlink Signals of Ground-Based Communications Satellite Terminals,” in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), 2020, pp. 9021–9025, doi: 10.1109/ICASSP40776.2020.9054729.
[45] M. Xian, X. Liu, M. Yin, K. Song, S. Zhao, and T. Gao, “Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 3656–3668, 2020, doi: 10.1109/JSTARS.2020.3004375.
[46] R. Avanzato and F. Beritelli, “An innovative acoustic rain gauge based on convolutional neural networks,” Inf., vol. 11, no. 4, 2020, doi: 10.3390/info11040183.
[2] L. G. Lanza and E. Vuerich, “The WMO Field Intercomparison of Rain Intensity Gauges,” Atmos. Res., vol. 94, no. 4, pp. 534–543, 2009, doi: https://doi.org/10.1016/j.atmosres.2009.06.012.
[3] A. R. Rahimi, A. R. Holt, G. J. G. Upton, and R. J. Cummings, “Use of dual-frequency microwave links for measuring path-averaged rainfall,” J. Geophys. Res. Atmos., vol. 108, no. D15, 2003, doi: https://doi.org/10.1029/2002JD003202.
[4] E. Adirosi, L. Baldini, N. Roberto, G. Vulpiani, and F. Russo, “Using disdrometer measured raindrop size distributions to establish weather radar algorithms,” AIP Conf. Proc., vol. 1648, no. 1, p. 190007, 2015, doi: 10.1063/1.4912476.
[5] R. T. Hitchcock, Radio-frequency and Microwave Radiation, Third Edit. American Industrial Hygiene Assn., 2004.
[6] C. H. Arslan, K. Aydin, J. V. Urbina, and L. Dyrud, “Satellite-Link Attenuation Measurement Technique for Estimating Rainfall Accumulation,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 2, pp. 681–693, 2018, doi: 10.1109/TGRS.2017.2753045.
[7] G. J. G. Upton, A. R. Holt, R. J. Cummings, A. R. Rahimi, and J. W. F. Goddard, “Microwave links: The future for urban rainfall measurement?,” Atmos. Res., vol. 77, no. 1-4 SPEC. ISS., pp. 300–312, 2005, doi: 10.1016/j.atmosres.2004.10.009.
[8] R. Acharya, “Tropospheric impairments: Measurements and mitigation,” in Satellite Signal Propagation, Impairments and Mitigation, 2017, pp. 195–245.
[9] ITU-R, “Recommendation ITU-R P.838-3: Specific attenuation model for rain,” 2005. [Online]. Available: https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.838-3-200503-I!!PDF-E.pdf.
[10] V. Christofilakis et al., “Earth-to-earth microwave rain attenuation measurements: A survey on the recent literature,” Symmetry (Basel)., vol. 12, no. 9, 2020, doi: 10.3390/sym12091440.
[11] M. Fencl, M. Dohnal, J. Rieckermann, and V. Bareš, “Gauge-adjusted rainfall estimates from commercial microwave links,” Hydrol. Earth Syst. Sci., vol. 21, no. 1, pp. 617–634, 2017, doi: 10.5194/hess-21-617-2017.
[12] M. S. Kim and B. H. Kwon, “Rainfall detection and rainfall rate estimation using microwave attenuation,” Atmosphere (Basel)., vol. 9, no. 8, pp. 1–21, 2018, doi: 10.3390/atmos9080287.
[13] T. C. Van Leth, A. Overeem, H. Leijnse, and R. Uijlenhoet, “A measurement campaign to assess sources of error in microwave link rainfall estimation,” Atmos. Meas. Tech., vol. 11, no. 8, pp. 4645–4669, 2018, doi: 10.5194/amt-11-4645-2018.
[14] K. Song, X. Liu, T. Gao, and B. He, “Rainfall estimation using a microwave link based on an improved rain-induced attenuation model,” Remote Sens. Lett., vol. 10, no. 11, pp. 1057–1066, 2019, doi: 10.1080/2150704X.2019.1648902.
[15] M. Graf, C. Chwala, J. Polz, and H. Kunstmann, “Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data,” Hydrol. Earth Syst. Sci., vol. 24, no. 6, pp. 2931–2950, 2020, doi: 10.5194/hess-24-2931-2020.
[16] K. K. Kumah, J. C. B. Hoedjes, N. David, B. H. P. Maathuis, H. O. Gao, and B. Z. Su, “Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation,” Atmosphere (Basel)., vol. 11, no. 9, 2020, doi: 10.3390/atmos11090884.
[17] A. Overeem, H. Leijnse, and R. Uijlenhoet, “Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network,” Atmos. Meas. Tech., vol. 9, no. 5, pp. 2425–2444, 2016, doi: 10.5194/amt-9-2425-2016.
[18] M. F. Rios Gaona, A. Overeem, T. H. Raupach, H. Leijnse, and R. Uijlenhoet, “Rainfall retrieval with commercial microwave links in São Paulo, Brazil,” Atmos. Meas. Tech., vol. 11, no. 7, pp. 4465–4476, 2018, doi: 10.5194/amt-11-4465-2018.
[19] L. W. de Vos, A. Overeem, H. Leijnse, and R. Uijlenhoet, “Rainfall Estimation Accuracy of a Nationwide Instantaneously Sampling Commercial Microwave Link Network: Error Dependency on Known Characteristics,” J. Atmos. Ocean. Technol., vol. 36, no. 7, pp. 1267–1283, 2019, doi: 10.1175/JTECH-D-18-0197.1.
[20] G. Roversi, P. Paolo Alberoni, A. Fornasiero, and F. Porcù, “Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy,” Atmos. Meas. Tech., vol. 13, no. 11, pp. 5779–5797, 2020, doi: 10.5194/amt-13-5779-2020.
[21] J. Pudashine et al., “Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links,” Water Resour. Res., vol. 56, no. 7, 2020, doi: https://doi.org/10.1029/2019WR026255.
[22] H. V. Habi and H. Messer, “Wet-Dry Classification Using LSTM and Commercial Microwave Links,” in 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2018, pp. 149–153, doi: 10.1109/SAM.2018.8448679.
[23] F. Beritelli, G. Capizzi, G. Lo Sciuto, C. Napoli, and F. Scaglione, “Rainfall Estimation Based on the Intensity of the Received Signal in a LTE/4G Mobile Terminal by Using a Probabilistic Neural Network,” IEEE Access, vol. 6, pp. 30865–30873, 2018, doi: 10.1109/ACCESS.2018.2839699.
[24] B. He, X. Liu, S. Hu, K. Song, and T. Gao, “Use of the C-Band Microwave Link to Distinguish between Rainy and Dry Periods,” Adv. Meteorol., vol. 2019, 2019, doi: 10.1155/2019/3428786.
[25] R. Avanzato and F. Beritelli, “Hydrogeological risk management in smart cities: A new approach to rainfall classification based on LTE cell selection parameters,” IEEE Access, vol. 8, pp. 137161–137173, 2020, doi: 10.1109/ACCESS.2020.3011375.
[26] K. Song, X. Liu, M. Zou, D. Zhou, H. Wu, and F. Ji, “Experimental Study of Detecting Rainfall Using Microwave Links: Classification of Wet and Dry Periods,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 5264–5271, 2020, doi: 10.1109/JSTARS.2020.3021555.
[27] F. D. Diba, M. A. Samad, J. Ghimire, and D. Y. Choi, “Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results,” IEEE Access, vol. 9, pp. 66769–66780, 2021, doi: 10.1109/ACCESS.2021.3076781.
[28] J. Polz, C. Chwala, M. Graf, and H. Kunstmann, “Rain event detection in commercial microwave link attenuation data using convolutional neural networks,” Atmos. Meas. Tech., vol. 13, no. 7, pp. 3835–3853, 2020, doi: 10.5194/amt-13-3835-2020.
[29] K. Pu, X. Liu, M. Xian, and T. Gao, “Machine Learning Classification of Rainfall Types Based on the Differential Attenuation of Multiple Frequency Microwave Links,” IEEE Trans. Geosci. Remote Sens., vol. 58, no. 10, pp. 6888–6899, 2020, doi: 10.1109/TGRS.2020.2977393.
[30] F. Giannetti, “Opportunistic Rain Rate Estimation from Measurements of Satellite Downlink Attenuation : A Survey,” Sensors, vol. 21, no. 17, 2021.
[31] E. Adirosi et al., “Exploiting satellite Ka and Ku links for the real-time estimation of rain intensity,” in 2017 32nd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), 2017, pp. 1–4, doi: 10.23919/URSIGASS.2017.8105128.
[32] F. Giannetti, M. Moretti, R. Reggiannini, and A. Vaccaro, “The NEFOCAST System for Detection and Estimation of Rainfall Fields by the Opportunistic Use of Broadcast Satellite Signals,” IEEE Aerosp. Electron. Syst. Mag., vol. 34, no. 6, pp. 16–27, 2019, doi: 10.1109/MAES.2019.2916292.
[33] M. Colli et al., “A Field Experiment of Rainfall Intensity Estimation Based on the Analysis of Satellite-to-Earth Microwave Link Attenuation BT - Applications in Electronics Pervading Industry, Environment and Society,” in International Conference on Applications in Electronics Pervading Industry, Environment and Society, 2019, pp. 137–144.
[34] H. An, W. Yan, S. Bian, and S. Ma, “Rain monitoring with polarimetric GNSS signals: Ground-based experimental research,” Remote Sens., vol. 11, no. 19, pp. 1–20, 2019, doi: 10.3390/rs11192293.
[35] E. Adirosi et al., “Evaluation of Rainfall Estimation Derived From Commercial Interactive DVB Receivers Using Disdrometer, Rain Gauge, and Weather Radar,” IEEE Trans. Geosci. Remote Sens., pp. 1–14, 2020, doi: 10.1109/TGRS.2020.3041448.
[36] M. Colli et al., “Rainfall Fields Monitoring Based on Satellite Microwave Down-Links and Traditional Techniques in the City of Genoa,” IEEE Trans. Geosci. Remote Sens., vol. 58, no. 9, pp. 6266–6280, 2020, doi: 10.1109/TGRS.2020.2976137.
[37] F. Giannetti, M. Moretti, R. Reggiannini, A. Vaccaro, S. Scarfone, and A. Ortolani, “On the Opportunistic use of Commercial Ku and Ka Band Satcom Networks for Rain Rate Estimation: Potentials and Critical Issues,” in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), 2020, pp. 9011–9015, doi: 10.1109/ICASSP40776.2020.9053668.
[38] G. L. Gragnani, M. Colli, E. Tavanti, and D. D. Caviglia, “Advanced real-time monitoring of rainfall using commercial satellite broadcasting service: a case study,” Sensors, vol. 21, p. 691, 2021, doi: 10.3390/s21030691.
[39] Y. Zhao, X. Liu, M. Xian, and T. Gao, “Statistical Study of Rainfall Inversion Using the Earth-Space Link at the Ku Band: Optimization and Validation for 1 Year of Data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 9486–9494, 2021, doi: 10.1109/JSTARS.2021.3111336.
[40] L. Barthès and C. Mallet, “Rainfall measurement from the opportunistic use of an Earth-space link in the Ku band,” Atmos. Meas. Tech., vol. 6, no. 8, pp. 2181–2193, 2013, doi: 10.5194/amt-6-2181-2013.
[41] A. Gharanjik, M. R. Bhavani Shankar, F. Zimmer, and B. Ottersten, “Centralized Rainfall Estimation Using Carrier to Noise of Satellite Communication Links,” IEEE J. Sel. Areas Commun., vol. 36, no. 5, pp. 1065–1073, 2018, doi: 10.1109/JSAC.2018.2832798.
[42] A. Gharanjik, K. V. Mishra, B. Shankar, and B. Ottersten, “Learning-Based Rainfall Estimation via Communication Satellite Links,” 2018 IEEE Stat. Signal Process. Work. SSP 2018, pp. 115–119, 2018, doi: 10.1109/SSP.2018.8450726.
[43] K. V. Mishra, A. Gharanjik, M. R. B. Shankar, and B. Ottersten, “Deep Learning Framework for Precipitation Retrievals from Communication Satellites,” in 10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY, 2018, no. July.
[44] K. V. Mishra, B. S. M. R., and B. Ottersten, “Deep Rainrate Estimation from Highly Attenuated Downlink Signals of Ground-Based Communications Satellite Terminals,” in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), 2020, pp. 9021–9025, doi: 10.1109/ICASSP40776.2020.9054729.
[45] M. Xian, X. Liu, M. Yin, K. Song, S. Zhao, and T. Gao, “Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 3656–3668, 2020, doi: 10.1109/JSTARS.2020.3004375.
[46] R. Avanzato and F. Beritelli, “An innovative acoustic rain gauge based on convolutional neural networks,” Inf., vol. 11, no. 4, 2020, doi: 10.3390/info11040183.
Published
2022-07-02
How to Cite
Mardyansyah, R. Y., Kurniawan, B., Soekirno, S., & Nuryanto, D. (2022, July 2). Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan. Elektron : Jurnal Ilmiah, 14(1), 21-29. https://doi.org/https://doi.org/10.30630/eji.14.1.278
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Articles
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