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Erschienen in:
MCOPS-SPM: Multi-Constrained Optimized Path Selection Based Spatial Pattern Matching in Social Networks Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift

verfasst von : Le Yan, Jun Feng, Yirui Wu, Tingting Hang

Erschienen in: Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications

Verlag: Springer International Publishing

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Abstract

The hydrological data of small and medium watershed develops with the passage of time. The rainfall-runoff patterns in these data often develop over time, and the models established for the analysis of such data will soon not be applicable. In view of the problem that adaptability and accuracy of the existing data-driven flood real-time forecasting model in medium and small watershed with concept drift. We update the data-driven model using incremental training based on support vector machine (SVM) and gated recurrent unit (GRU) model respectively. According to the rapid real-time flood forecasting test results of the Tunxi watershed, Anhui Province, China, the fast real-time flood forecast data-driven model with incremental update can more accurately predict the moment when the flood begins to rise and the highest point of flood stream-flow, and it is an effective tool for real-time flood forecasting in small and medium watersheds.

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Vorheriges Kapitel Time-Varying Water Quality Analysis with Semantical Mining Technology
Nächstes Kapitel A Survey on Dimension Reduction Algorithms in Big Data Visualization
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Metadaten
Titel
Data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift
verfasst von
Le Yan
Jun Feng
Yirui Wu
Tingting Hang
Copyright-Jahr
2020
Buch
Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications

Print ISBN: 978-3-030-48512-2

Electronic ISBN: 978-3-030-48513-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-48513-9_30

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