Three steps towards better forecasting for streamflow deep learning

Tan, Woon Yang and Lai, Sai Hin and Teo, Fang Yenn and Armaghani, Danial Jahed and Pavitra, Kumar and Ahmed El-Shafie, Ahmed Hussein Kamel (2022) Three steps towards better forecasting for streamflow deep learning. Applied Sciences-Basel, 12 (24). ISSN 2076-3417, DOI

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Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes a three-step artificial intelligence model improvement for streamflow forecasting. Step 1 uses long short-term memory (LSTM), an improvement on the conventional artificial neural network (ANN). Step 2 performs multi-step ahead forecasting while establishing the rates of change as a new approach. Step 3 further improves the accuracy through three different kinds of optimization algorithms. The Stormwater and Road Tunnel project in Kuala Lumpur is the study area. Historical rainfall data of 14 years at 11 telemetry stations are obtained to forecast the flow at the confluence located next to the control center. Step 1 reveals that LSTM is a better model than ANN with R 0.9055, MSE 17,8532, MAE 1.4365, NSE 0.8190 and RMSE 5.3695. Step 2 unveils the rates of change model that outperforms the rest with R = 0.9545, MSE = 8.9746, MAE = 0.5434, NSE = 0.9090 and RMSE = 2.9958. Finally, Stage 3 is a further improvement with R = 0.9757, MSE = 4.7187, MAE = 0.4672, NSE = 0.9514 and RMSE = 2.1723 for the bat-LSTM hybrid algorithm. This study shows that the delta Q model has consistently yielded promising results while the metaheuristic algorithms are able to yield additional improvement to the model's results.

Item Type: Article
Funders: None
Uncontrolled Keywords: Optimization; Metaheuristic algorithms; Streamflow forecasting
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 23 Nov 2023 04:13
Last Modified: 23 Nov 2023 04:13

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