Academic Journal
A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events
| العنوان: | A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events |
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| المؤلفون: | Benjamin Burrichter, Juliana Koltermann da Silva, Andre Niemann, Markus Quirmbach |
| المصدر: | Hydrology, Vol 11, Iss 3, p 41 (2024) |
| بيانات النشر: | MDPI AG |
| سنة النشر: | 2024 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | deep learning, temporal fusion transformer, urban pluvial flooding, urban drainage system, real-time flood forecasting, manhole overflow, Science |
| الوصف: | This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network with 975 manholes. As part of the investigations, the TFT was compared to other deep learning architectures to evaluate its predictive performance. In addition to precipitation measurements and forecasts, the issue of how the additional consideration of measurements in the sewer network as model inputs impacts forecast accuracy was investigated. A varying number of sensors and different measurement signals were compared. The results indicate high performance for the TFT compared to other model architectures like a long short-term memory (LSTM) network or a dual-stage attention-based recurrent neural network (DA-RNN). Additionally, results suggest that considering a single measuring point at the outlet of the sewer network instead of an entire measuring network yields better forecasts. One possible explanation is the high correlation between measurements, which increases model and training complexity without adding much value. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2306-5338 |
| Relation: | https://www.mdpi.com/2306-5338/11/3/41; https://doaj.org/toc/2306-5338; https://doaj.org/article/311b2720b4a34d6fbe4f67c0dd38374a |
| DOI: | 10.3390/hydrology11030041 |
| الاتاحة: | https://doi.org/10.3390/hydrology11030041 https://doaj.org/article/311b2720b4a34d6fbe4f67c0dd38374a |
| رقم الانضمام: | edsbas.B30DAFA6 |
| قاعدة البيانات: | BASE |
| تدمد: | 23065338 |
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| DOI: | 10.3390/hydrology11030041 |