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Technical note: a simple feedforward artificial neural network for high-temporal-resolution rain event detection using signal attenuation from commercial microwave links

  • Two simple feedforward neural networks (multilayer perceptrons – MLPs) are trained to detect rainfall events using signal attenuation from commercial microwave links (CMLs) as predictors and high-temporal-resolution reference data as the target. MLPGA is trained against nearby rain gauges, and MLPRA is trained against gauge-adjusted weather radar. Both MLPs were trained on 26 CMLs and tested on 843 CMLs, all located within 5 km of a rain gauge. Our results suggest that these MLPs outperform existing methods, effectively capturing the intermittent behaviour of rainfall. This study is the first to use both radar and rain gauges for training and testing CML rainfall detection. While previous studies have mainly focused on hourly reference data, our findings show that it is possible to classify rainy and dry time steps with a higher temporal resolution.

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Metadaten
Author:Erlend Øydvin, Maximilian GrafORCiDGND, Christian ChwalaORCiDGND, Mareile Astrid Wolff, Nils-Otto Kitterød, Vegard Nilsen
URN:urn:nbn:de:bvb:384-opus4-1172113
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/117211
ISSN:1607-7938OPAC
Parent Title (English):Hydrology and Earth System Sciences
Publisher:Copernicus Publications
Place of publication:Göttingen
Type:Article
Language:English
Date of first Publication:2024/11/29
Publishing Institution:Universität Augsburg
Release Date:2024/12/02
Volume:28
Issue:23
First Page:5163
Last Page:5171
DOI:https://doi.org/10.5194/hess-28-5163-2024
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Geographie
Fakultät für Angewandte Informatik / Institut für Geographie / Lehrstuhl für Regionales Klima und Hydrologie
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)