Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026255 |
Deep Learning for an improved prediction of rainfall retrievals from commercial microwave links | |
Jayaram Pudashine; Adrien Guyot; Francois Petitjean; Valentijn R. N. Pauwels; Remko Uijlenhoet; Alan Seed; Mahesh Prakash; Jeffrey P. Walker | |
2020-05-31 | |
发表期刊 | Water Resources Research |
出版年 | 2020 |
英文摘要 | Commercial microwave links (CMLs) have proven useful for providing rainfall information close to the ground surface. However, large uncertainties are associated with these retrievals, partly due to challenges in the type of data collection and processing. In particular, the most common case is when only minimum and maximum received signal levels (RSLs) over a given time interval (hereafter 15 minutes) are stored by mobile network operators. The average attenuation and the corresponding rainfall rate are then calculated based on a weighted average method using the minimum and maximum attenuation. In this study, an alternative to using a constant weighted average method is explored, based on a machine learning model trained to produce actual attenuation from minimum/maximum values. A rainfall retrieval deep learning model was designed based on a Long Short Term Memory (LSTM) model architecture, and trained with disdrometer data in a form that is comparable to the data provided by mobile network operators. A first evaluation used only disdrometer data to mimic both attenuation from a CML and corresponding rainfall rates. For the test dataset, the relative bias was reduced from 5.99% to 2.84% and the coefficient of determination (R2) increased from 0.86 to 0.97. The second evaluation used this disdrometer‐trained LSTM to retrieve rainfall rates from an actual CML located nearby the disdrometer. A significant improvement in the overall rainfall estimation compared to existing microwave link attenuation models was observed. The relative bias reduced from 7.39% to ‐1.14 % and the R2 improved from 0.71 to 0.82. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/271675 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Jayaram Pudashine,Adrien Guyot,Francois Petitjean,et al. Deep Learning for an improved prediction of rainfall retrievals from commercial microwave links[J]. Water Resources Research,2020. |
APA | Jayaram Pudashine.,Adrien Guyot.,Francois Petitjean.,Valentijn R. N. Pauwels.,Remko Uijlenhoet.,...&Jeffrey P. Walker.(2020).Deep Learning for an improved prediction of rainfall retrievals from commercial microwave links.Water Resources Research. |
MLA | Jayaram Pudashine,et al."Deep Learning for an improved prediction of rainfall retrievals from commercial microwave links".Water Resources Research (2020). |
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