Abstract
To minimize the negative impacts brought by floods, researchers pay special attention to the problem of flood prediction. In this paper, we propose a hierarchical Bayesian network based incremental model to predict floods for small rivers. The proposed model not only appropriately embeds hydrology expert knowledge with Bayesian network for high rationality and robustness, but also designs an incremental learning scheme to improve the self-improving and adaptive ability of the proposed model. Following the idea of a famous hydrology model, i.e., XAJ model, we firstly present the construction of hierarchical Bayesian network as local and global network construction. After that, we propose an incremental learning scheme, which selects proper incremental data to improve the completeness of prior knowledge and updates parameters of Bayesian network to prevent training from scratch. We demonstrate the accuracy and effectiveness of the proposed model by conducting experiments on a collected dataset with one comparative method.
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References
Cheng, C., Niu, W., Feng, Z., Shen, J., Chau, K.: Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization. Water 7(8), 4232–4246 (2015)
Diehl, C.P., Cauwenberghs, G.: SVM incremental learning, adaptation and optimization. In: Proceedings of International Joint Conference on Neural Networks, vol. 4, pp. 2685–2690 (2003)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)
Han, S., Coulibaly, P.: Bayesian flood forecasting methods: a review. J. Hydrol. 551, 340–351 (2017)
Jing, P., Su, Y., Nie, L., Bai, X., Liu, J., Wang, M.: Low-rank multi-view embedding learning for micro-video popularity prediction. IEEE Trans. Knowl. Data Eng. 30(8), 1519–1532 (2018)
Liu, F., Xu, F., Yang, S.: A flood forecasting model based on deep learning algorithm via integrating stacked autoencoders with BP neural network. In: Proceedings of IEEE International Conference on Multimedia Big Data, pp. 58–61 (2017)
Nie, L., Zhang, L., Yan, Y., Chang, X., Liu, M., Shaoling, L.: Multiview physician-specific attributes fusion for health seeking. IEEE Trans. Cybern. 47(11), 3680–3691 (2017)
Paquet, E., Garavaglia, F., Garçon, R., Gailhard, J.: The schadex method: a semi-continuous rainfall-runoff simulation for extreme flood estimation. J. Hydrol. 495, 23–37 (2013)
Reggiani, P., Weerts, A.: Probabilistic quantitative precipitation forecast for flood prediction: an application. J. Hydrometeorol. 9(1), 76–95 (2008)
Ren-Jun, Z.: The Xinanjiang model applied in China. J. Hydrol. 135(1–4), 371–381 (1992)
Rogger, M., Viglione, A., Derx, J., Blöschl, G.: Quantifying effects of catchments storage thresholds on step changes in the flood frequency curve. Water Resour. Res. 49(10), 6946–6958 (2013)
Villarini, G., Mandapaka, P.V., Krajewski, W.F., Moore, R.J.: Rainfall and sampling uncertainties: a rain gauge perspective. J. Geophys. Res. Atmos. 113(D11) (2008)
Wu, Y., Liu, Z., Xu, W., Feng, J., Shivakumara, P., Lu, T.: Context-aware attention LSTM network for flood prediction. In: Proceedings of International Conference on Pattern Recognitions (2018)
Wu, Y., Xu, W., Feng, J., Shivakumara, P., Lu, T.: Local and global Bayesian network based model for flood prediction. In: Proceedings of International Conference on Pattern Recognition (2018)
Wu, Y., Yue, Y., Tan, X., Wang, W., Lu, T.: End-to-end chromosome Karyotyping with data augmentation using GAN. In: Proceedings on International Conference on Image Processing, pp. 2456–2460 (2018)
Yao, C., Zhang, K., Yu, Z., Li, Z., Li, Q.: Improving the flood prediction capability of the Xinanjiang model in ungauged nested catchments by coupling it with the geomorphologic instantaneous unit hydrograph. J. Hydrol. 517, 1035–1048 (2014)
Zhao, R., Zhuang, Y., Fang, L., Liu, X., Zhang, Q.: The Xinanjiang model. In: Proceedings Oxford Symposium Hydrological Forecasting, vol. 129, pp. 351–356 (1980)
Zhuang, W.Y., Ding, W.: Long-lead prediction of extreme precipitation cluster via a spatiotemporal convolutional neural network. In: Proceedings of the 6th International Workshop on Climate Informatics: CI (2016)
Acknowledgement
This work was supported by National Key R&D Program of China under Grant 2018YFC0407901, the Natural Science Foundation of China under Grant 61702160, Grant 61672273 and Grant 61832008, the Fundamental Re-search Funds for the Central Universities under Grant 2016B14114, the Science Foundation of Jiangsu under Grant BK20170892, the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant BK20160021, Scientific Foundation of State Grid Corporation of China (Research on Ice-wind Disaster Feature Recognition and Prediction by Few-shot Machine Learning in Transmission Lines), and the open Project of the National Key Lab for Novel Software Technology in NJU under Grant K-FKT2017B05.
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Wu, Y., Xu, W., Yu, Q., Feng, J., Lu, T. (2019). Hierarchical Bayesian Network Based Incremental Model for Flood Prediction. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_46
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DOI: https://doi.org/10.1007/978-3-030-05710-7_46
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