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  Open-Access Precipitation Networks and Machine Learning Algorithms as Tools for Flood Severity Prediction

Imagiire, L. O. K. M., Mester, B., Haun, S., Seidel, J. (2022): Open-Access Precipitation Networks and Machine Learning Algorithms as Tools for Flood Severity Prediction. - In: Kolathayar, S., Mondal, A., Chian, S. C. (Eds.), Climate Change and Water Security, (Lecture Notes in Civil Engineering ; 178), Singapore : Springer Nature, 131-142.
https://doi.org/10.1007/978-981-16-5501-2_11

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 Urheber:
Imagiire, Luis O. K. M.1, Autor
Mester, Benedikt2, Autor              
Haun, Stefan1, Autor
Seidel, Jochen1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: During the past decades, convective rainfall in the Echaz catchment, which is characterized by steep topography and a high degree of urbanization, led to recurring flash floods. The high spatial and temporal variability of precipitation, in combination with the small drainage basin, contribute to low predictability and to the considerable damage potential of such events. The aim of this study is the development of a simple model to predict flood severity in the Echaz catchment. This model is based on open-access precipitation data from a personal weather station (PWS) network and water level measurements from a low-cost ultrasonic sensor. Machine learning classification methods (logistic regression and decision tree) are trained with observational data to determine maximum rainfall thresholds for different accumulation periods, ranging from 5 to 60 min. Hence, the proposed model uses multiple triggers to predict the exceedance of critical water levels. As a result, severe floods can be recognized earlier and with higher reliability, providing more response time for local authorities. Although the limited data availability increases the risk of overfitting and lower performance for the first upcoming events, the model quality will increase with the incorporation of new measurement data in the future. The reduced complexity and high interpretability of the model allow for a fast decision-making process. Additionally, the model has high potential and can easily be adapted to similar small catchments.

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 Datum: 2021-11-192022-01-15
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1007/978-981-16-5501-2_11
PIKDOMAIN: RD3 - Transformation Pathways
Organisational keyword: RD3 - Transformation Pathways
Research topic keyword: Climate impacts
Research topic keyword: Extremes
Research topic keyword: Weather
Model / method: Machine Learning
MDB-ID: No data to archive
 Art des Abschluß: -

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Titel: Climate Change and Water Security
Genre der Quelle: Buch
 Urheber:
Kolathayar, Sreevalsa1, Herausgeber
Mondal, Arpita1, Herausgeber
Chian, Siau Chen1, Herausgeber
Affiliations:
1 External Organizations, ou_persistent22            
Ort, Verlag, Ausgabe: Singapore : Springer Nature
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 131 - 142 Identifikator: -

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Titel: Lecture Notes in Civil Engineering
Genre der Quelle: Reihe
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Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 178 Artikelnummer: - Start- / Endseite: - Identifikator: -