Three steps towards better forecasting for streamflow deep learning

• Main Authors: Tan, Woon Yang, Lai, Sai Hin, Teo, Fang Yenn, Armaghani, Danial Jahed, Pavitra, Kumar, Ahmed El-Shafie, Ahmed Hussein Kamel
• Format: Article
• Published: MDPI 2022
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/40313/

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 p...