Authors : N. Bhavana ; Tandlam Maheswari

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/364uks7y

DOI : https://doi.org/10.38124/ijisrt/25may372

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Providing an accurate rainfall estimate at individual points is a challenging problem in order to mitigate risks derived from severe rainfall events, such as floods and landslides. Dense networks of sensors, named rain gauges (RGs), are typically used to obtain direct measurements of precipitation intensity in these points. These measurements are usually interpolated by using spatial interpolation methods for estimating the precipitation field over the entire area of interest. However, these methods are computationally expensive, and to improve the estimation of the variable of interest in unknown points, it is necessary to integrate further information. To overcome these issues, this work proposes a machine learning- based methodology that exploits a classifier based on ensemble methods for rainfall estimation and is able to integrate information from different remote sensing measurements. The proposed approach supplies an accurate estimate of the rainfall where RGs are not available, permits the integration of heterogeneous data sources exploiting both the high quantitative precision of RGs and the spatial pattern recognition ensured by radars and satellites, and is computationally less expensive than the interpolation methods.

Keywords : Exploits, Measurements, Precipitation, Information.

References :

1. Chen, S., Hong, Y., Cao, Q., Gourley, J. J., Hu, J., & Adler, R. (2013). Evaluation of the TRMM Multi‐Satellite Precipitation Analysis Using Gauge Observations and Radar Estimates in the Central United States. Journal of Geophysical Research: Atmospheres, 118(1), 361–375. https://doi.org/10.1029 /2012JD018137
2. Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., & Nearing, G. (2019). Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine Learning Applied to Large-Sample Datasets. Hydrology and Earth System Sciences, 23(12), 5089–5110. https://doi.org/10.51 94/hess-23-5089-2019
3. Rahman, M. M., Di, L., Yu, E. G., & Deng, M. (2021). A Deep Learning Approach for Rainfall Estimation Using Multisensor Satellite Data. Remote Sensing, 13(2), 298. https://doi.org/10.3390/rs13020298
4. Tao, Y., Wang, H., & Tang, G. (2020). Rainfall Estimation from Multi-Source Satellite Observations Using Deep Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3781–3792. https://doi.org/10.1109/JSTARS.20 20.3007433
5. Awadallah, A. G., & Awadallah, M. A. (2013). Integration of Radar, Satellite, and Rain Gauge Data for Rainfall Estimation over Nile Basin Using a Multi-Sensor Precipitation Estimator (MPE). Atmospheric Research, 131, 10–19. https://doi.org/10.1016/j.atmos res.2013.04.010
6. Zhang, Y., Qin, H., Yang, J., & Liu, Y. (2020). Precipitation Prediction Based on Deep Learning Using Remote Sensing Data. Atmosphere, 11(3), 304. https://doi.org/10.3390/atmos11030304

7. Bhuiyan, M. A. E., & Dey, N. (2021). Deep Learning and Multisource Remote Sensing Data Integration for Precipitation Prediction. Earth Science Informatics, 14(4), 1619–1634. https://doi.org/10.1007/s12145-021-00608-5
8. Tang, G., Ma, Y., Long, D., Zhong, L., & Hong, Y. (2016). Evaluation of GPM Day-1 IMERG and TMPA Version-7 Rainfall Products over Mainland China. Remote Sensing, 8(6), 481. https://doi.org/10.3390 /rs8060481
9. Liang, X., Li, Y., & Li, S. (2020). Deep Learning-Based Rainfall Prediction Using Radar and Reanalysis Data. Journal of Hydrology, 585, 124760. https://doi.org /10.1016/j.jhydrol.2020.124760