APPLICATION OF LONG SHORT-TERM MEMORY (LSTM) NETWORKS APPROACH FOR RIVER WATER LEVEL FORECASTING USING MULTIPLE RIVER BASINS: A CASE STUDY FOR SRI LANKA
Author:
Affiliation:
1. Graduate School of Engineering, The University of Tokyo
2. Institute of Industrial Science, The University of Tokyo
Publisher
Japan Society of Civil Engineers
Link
https://www.jstage.jst.go.jp/article/journalofjsce/12/2/12_23-16127/_pdf
Reference22 articles.
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2. 2) Okuno, S., Ikeuchi, K. and Aihara, K. : Practical data‐driven flood forecasting based on dynamical systems theory, Water Resources Research, Vol. 57, No. 3, pp. e2020WR028427, 2021.
3. 3) Agudelo-Otálora, L. M., Moscoso-Barrera, W. D., PaipaGaleano, L. A., & Mesa-Sciarrotta, C. : Comparison of physical models and artificial intelligence for prediction of flood levels, Tecnología y ciencias del agua, Vol. 9, No. 4, pp, 209-235, 2018.
4. 4) Govindaraju, R.S. : Artificial neural networks in hydrology I: Preliminary concepts, Journal of Hydrologic Engineering, Vol. 5, No. 2, pp. 115-123, 2000.
5. 5) LeCun, Y., Bengio, Y. and Hinton, G. : Deep learning. nature, Vol. 521, No. 7553, pp. 436-444, 2015.
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