Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework

Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework

Predicting discharges in sewage systems play an essential role in reducing sewer overflows and impacts on the environment and public health. Choosing a suitable model to predict discharges in these systems is essential to realizing these aforementioned goals. Long Short-Term Memory (LSTM) has been p...

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Bibliographic Details
Main Authors: Lam Van Nguyen, Hoese Michel Tornyeviadzi, Dieu Tien Bui, Razak Seidu
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Water
Subjects:
time-series forecasting
discharge prediction
sewer pipe
Long Short-Term Memory
Ålesund
Norway
Online Access:https://www.mdpi.com/2073-4441/14/3/300

Internet

https://www.mdpi.com/2073-4441/14/3/300

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