Designing a decomposition-based multi-phase pre-processing strategy coupled with EDBi-LSTM deep learning approach for sediment load forecasting

Jamei, Mehdi; Ali, Mumtaz; Malik, Anurag; Rai, Priya; Karbasi, Masoud; Farooque, Aitazaz A.; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.ecolind.2023.110478

Cited by 7 publications

(1 citation statement)

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“…Through these distinct gates, LSTM effectively learns to store pertinent information while discarding irrelevant data, enabling it to process and retain essential patterns effectively. [16].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Comparison of KNN and LSTM on the Prediction of the Operational Conditions of Natural Gas Pipeline Transmission Networks

Yusuf,

Hasmawati,

Ihsan

2024

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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During the gas distribution process, a sequence of compressors creates a pressure difference, causing gas to move from regions of high pressure to areas with comparatively lower pressure. The Natural Gas transmission process experiences variations in pressure and temperature, primarily caused by frictional losses, differences in altitude, gas velocity, and the Joule-Thompson effect. Additionally, effective heat transfer to or from the environment contributes to temperature changes throughout the pipeline. The presence of liquid and density changes (hydrate) within the channel also has an impact on the pressure, influencing both pressure and temperature conditions.. This study implements the KNN and LSTM models to predict pressure conditions in natural gas transmission pipelines to analyze the performance comparison of the best model performance using several appropriate parameters to support maximum method performance results. The results show that the LSTM model is better at predicting pressure conditions in natural gas pipeline transmission networks, with an R2 score of 99.45, compared to the KNN model, with an R2 score of 92.82. This study also obtained prediction results from the KNN and LSTM models; the KNN model tends to produce the same pressure value for eight months, while the LSTM model produces pressure values that tend to vary.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%