A Optimizing the Long Short-Term Memory (LSTM) model by Bayesian method for salinity intrusion forecasting: a study at Dai Ngai station, Soc Trang province, Vietnam
Keywords:Long Short-Term Memory (LSTM), Bayesian method, multistep-ahead salinity forecasting.
Salinity intrusion forecasting is essential and challenging for hydrometeorology, especially in climate change. Employing machine learning (ML) algorithms and conventional forecasting techniques are gaining popularity and providing high performance. This study presents a method to optimize a machine learning model based on the Long Short-Term Memory (LSTM) algorithm for multistep-ahead salinity forecasting (up to 7 days) at Dai Ngai station, Soc Trang province. The optimization method based on the Bayesian algorithm for hyperparameters optimization and input predictors optimization has been highly effective for predicting salinity with a lead time of 1 day to 7 days. Specifically, the forecast results evaluated by the R2 and RMSE indexes both give satisfactory results on the test data set (with lead time from 1 day to 7 days, R2 ranges from 0.9 to 0.54, and RMSE ranges from 0.27 to 0.53). This study is a premise for improving machine learning models for short-term and long-term salinity intrusion prediction in the Mekong delta and Vietnam.
Van Binh, D., Kantoush, S. A., Saber, M., Mai, N. P., Maskey, S., Phong, D. T., and Sumi, T., 2020. Long-term alterations of flow regimes of the Mekong river and adaptation strategies for the Vietnamese Mekong Delta. Journal of Hydrology: Regional Studies, 32, 100742.
Hoang Lam, D., Huy Phuong, N., Dinh Dat, N., Tien Giang, N., 2022. A setup of Mike 11 model for hydrological and saline intrusion forecast in Ben Tre province. Vietnam Journal of Hydrometeorology, 740(1), 38–49. (in Vietnamese).
Van Dung, D., Dinh Phuong, T., Thi Oanh, L., Thanh Cong, T., 2018. The effectiveness of the Mike 11 ad model for forecasting and warning the salinity intrusion in the Mekong delta. Vietnam Journal of Hydrometeorology, 693, 48–58. (in Vietnamese).
Tran, T. D., Tran, V. N., and Kim, J., 2021. Improving the accuracy of dam inflow predictions using a long short-term memory network coupled with wavelet transform and predictor selection. Mathematics, 9(5), 551.
Yoo, H. J., Kim, D. H., Kwon, H. H., and Lee, S. O., 2020. Data driven water surface elevation forecasting model with hybrid activation function—A case study for Hangang River, South Korea. Applied Sciences, 10(4), 1424.
Ahn, S., Tran, T. D., and Kim, J., 2022. Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast. Ocean Engineering, 264, 112593.
Cong Thanh, N. and Tien Giang, N., 2022. Building LSTM (Long Short-Term Memory) machine learning model for water salinity forecasting in Dai Ngai. Vietnam Journal of Hydrometeorology, 740(1), 98–104. (in Vietnamese).
Dau Hoang, N., Ngoc Tan, N., and Thi Hue, N., 2022. Building a warning and forecasting model by supervised machine learning method and testing saltwater intrusion prediction for Hau river basin. https://tainguyenvamoitruong.vn/xay-dung-mo-hinh-canh-bao-du-bao-theo-phuong-phap-hoc-may-co-giam-sat-thu-nghiem-du-bao-xam-ngap-man-cho-luu-vuc-song-hau-cid11297.html, accessed 01 March 2023 (in Vietnamese). https://tainguyenvamoitruong.vn/xay-dung-mo-hinh-canh-bao-du-bao-theo-phuong-phap-hoc-may-co-giam-sat-thu-nghiem-du-bao-xam-ngap-man-cho-luu-vuc-song-hau-cid11297.html, accessed 01 March 2023 (in Vietnamese).">
Glorot, X., and Bengio, Y., 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249–256). JMLR Workshop and Conference Proceedings.
Cheng, H., Tan, P. N., Gao, J., and Scripps, J., 2006. Multistep-ahead time series prediction. In Advances in Knowledge Discovery and Data Mining: 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9–12, 2006. Proceedings 10 (pp. 765–774). Springer Berlin Heidelberg.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M., 2018. Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 22(11), 6005–6022.
Mantovani, R. G., Rossi, A. L., Vanschoren, J., Bischl, B., and De Carvalho, A. C., 2015. Effectiveness of random search in SVM hyper-parameter tuning. In 2015 international joint conference on neural networks (IJCNN) (pp. 1–8). IEEE.
Brochu, E., Cora, V. M., and De Freitas, N., 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599.
Saad, S., Javadi, A. A., Chugh, T., and Farmani, R., 2022. Optimal management of mixed hydraulic barriers in coastal aquifers using multi-objective Bayesian optimization. Journal of Hydrology, 612, 128021.
Alizadeh, B., Bafti, A. G., Kamangir, H., Zhang, Y., Wright, D. B., and Franz, K. J., 2021. A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction. Journal of Hydrology, 601, 126526.
Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., and Deng, S. H., 2019. Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26–40.
Raissi, M., Perdikaris, P., and Karniadakis, G. E., 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378, 686–707.
Mahesh, R. B., Leandro, J., and Lin, Q., 2022. Physics informed neural network for spatial-temporal flood forecasting. In Climate Change and Water Security: Select Proceedings of VCDRR 2021 (pp. 77–91). Springer Singapore.
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