Rainstorm-Induced Emergency Recognition from Citizens’ Communications Based on Spatial Feature Extraction and Transfer Learning
Publication: Natural Hazards Review
Volume 24, Issue 1
Abstract
The rapid recognition of rainstorm-induced emergencies (e.g., building and road inundation, facility damage, and trapped citizens) is vital to timely disaster response. One big challenge that limits the performance of emergency recognition is the data imbalance between different emergency domains. The present study aims to develop an effective cross-domain transfer learning framework for rainstorm-induced emergency recognition based on the text reports provided by citizens. The critical component of the framework is the use of joint distribution adaption (JDA) analysis embedded in a discriminative feature mapping procedure, which transfers rich knowledge learned from large-scale datasets to the learning task from small emergence data. Considering the feature incompleteness that is caused by short text length, a basic probability assignment function is constructed and applied to extract important spatial features for rainstorm emergency recognition, with an improved marginal Fisher analysis being adopted to optimize cross-domain text feature representation. The proposed scheme is validated using the empirical data of ten emergency classes from Wuhan City, China. Our experimental results show that the proposed method could significantly address data imbalance and thus help achieve high recognition performance through domain knowledge complementation. Meanwhile, the use of various spatial features is proved to be effective in tackling missing features. This scheme can be further developed into smart systems for rainstorm disaster response with reasonable performance and imbalanced sample sizes.
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Data Availability Statement
The rainstorm-induced emergency data and some spatial data (i.e., drainage network, power system, and telecommunication data) are proprietary or confidential in nature and may only be provided with restrictions. Other data and all related models of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is supported by the Major Research Project of Nation Natural Science Foundation of China named Big data Driven Management and Decision-making Research (No. 91746207), the General Program of Nation Natural Science Foundation of China (No. 71774043), National Natural Science Foundation of China (No. 71904121), the Fundamental Research Funds for the Central Universities (No. 20720221020), and the General Program of Social Science Foundation of Hubei Province (No. 2019051).
References
Allen, D. W., and M. J. Gerike. 2021. “Focus on geodatabases in ArcGIS Pro.” Photogramm. Eng. Remote Sens. 87 (7): 468–469. https://doi.org/10.14358/PERS.87.7.468.
Bai, X. Y., M. I. Hanif, F. S. Li, M. S. Hanif, and Y. H. Gu. 2017. “An empirical study on application and efficiency of gridded management in public service supply of Chinese Government.” J. Sci. Technol. Policy Manage. 8 (1): 2–15. https://doi.org/10.1108/Jstpm-08-2015-0026.
Belkin, M., P. Niyogi, and V. Sindhwani. 2006. “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples.” J. Mach. Learn. Res. 7 (85): 2399–2434.
China Meteorological Administration. 2019. China meteorological yearbook. Beijing: Meteorology Press.
Dai, W., Q. Yang, G.-R. Xue, and Y. Yu. 2007. “Boosting for transfer learning.” In Proc., 24th Int. Conf. on Machine Learning. New York: Association for Computing Machinery. https://doi.org/10.1145/1273496.1273521.
Devaraj, A., D. Murthy, and A. Dontula. 2020. “Machine-learning methods for identifying social media-based requests for urgent help during hurricanes.” Int. J. Disaster Risk Reduct. 51 (Dec): 101757. https://doi.org/10.1016/j.ijdrr.2020.101757.
Devlin, J., M. W. Chang, K. Lee, and K. Toutanova. 2018. “Bert: Pre-training of deep bidirectional transformers for language understanding.” Preprint, submitted October 11, 2018. http://arxiv.org/abs/1810.04805.
Diodato, N., O. Petrucci, and G. Bellocchi. 2012. “Scale-invariant rainstorm hazard modelling for slopeland warning.” Meteorol. Appl. 19 (3): 279–288. https://doi.org/10.1002/met.259.
El-allaly, E. D., M. Sarrouti, N. En-Nahnahi, and S. O. El Alaoui. 2021. “MTTLADE: A multi-task transfer learning-based method for adverse drug events extraction.” Inf. Process. Manage. 58 (3): 102473. https://doi.org/10.1016/j.ipm.2020.102473.
Gao, L., L. Zhang, X. Li, and S. Zhou. 2019. “Evaluating metropolitan flood coping capabilities under heavy storms.” J. Hydrol. Eng. 24 (6): 05019011. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001793.
Garcia, V., J. S. Sanchez, and R. A. Mollineda. 2012. “On the effectiveness of preprocessing methods when dealing with different levels of class imbalance.” Knowl.-Based Syst. 25 (1): 13–21.
Geng, J., X. Y. Deng, X. R. Ma, and W. Jiang. 2020. “Transfer learning for SAR image classification via deep joint distribution adaptation networks.” IEEE Trans. Geosci. Remote Sens. 58 (8): 5377–5392. https://doi.org/10.1109/TGRS.2020.2964679.
Geng, S. 2018. “‘Wuhan micro neighborhood’ with ‘City Brain’: Citizens’ needs are responded to online at any time.” Accessed February 10, 2021. http://www.cjrbapp.cjn.cn/wuhan/p/209880.html.
Ghiassi, M., and S. Lee. 2018. “A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach.” Expert Syst. Appl. 106 (Sep): 197–216. https://doi.org/10.1016/j.eswa.2018.04.006.
Grubinger, T., A. Birlutiu, H. Schoner, T. Natschlager, and T. Heskes. 2017. “Multi-domain transfer component analysis for domain generalization.” Neural Process. Lett. 46 (3): 845–855. https://doi.org/10.1007/s11063-017-9612-8.
Han, S. Q., Y. Y. Xie, D. M. Li, P. Y. Li, and M. L. Sun. 2006. “Risk analysis and management of urban rainstorm water logging in Tianjin.” J. Hydrodyn. 18 (5): 552–558. https://doi.org/10.1016/S1001-6058(06)60134-0.
Howard, J., and S. Ruder. 2018. “Universal language model fine-tuning for text classification.” Preprint, submitted January 18, 2018. http://arxiv.org/abs/1801.06146.
Huang, Z. Y., H. Y. Zhu, J. T. Zhou, and X. Peng. 2019. “Multiple marginal Fisher analysis.” IEEE Trans. Ind. Electron. 66 (12): 9798–9807. https://doi.org/10.1109/TIE.2018.2870413.
Jafari, N. H., X. Li, Q. Chen, C. Y. Le, L. P. Betzer, and Y. Q. Liang. 2021. “Real-time water level monitoring using live cameras and computer vision techniques.” Comput. Geosci. 147 (Feb): 104642. https://doi.org/10.1016/j.cageo.2020.104642.
Jiang, J. C., J. Z. Liu, C. X. Cheng, J. Z. Huang, and A. K. Xue. 2019. “Automatic estimation of urban waterlogging depths from video images based on ubiquitous reference objects.” Remote Sens. 11 (5): 587. https://doi.org/10.3390/rs11050587.
Joulin, A., E. Grave, P. Bojanowski, and T. Mikolov. 2016. “Bag of tricks for efficient text classification.” Preprint, submitted July 6, 2016. http://arxiv.org/abs/1607.01759.
Kulis, B., K. Saenko, and T. Darrell. 2011. “What you saw is not what you get: Domain adaptation using asymmetric kernel transforms.” In Proc., CVPR 2011, 1785–1792. New York: IEEE.
Li, M. Z., X. Lu, Q. Zhang, and Y. Deng. 2014. “Multiscale probability transformation of basic probability assignment.” Math. Probl. Eng. 2014 (Oct): 319264. https://doi.org/10.1155/2014/319264.
Liang, X. Y., H. He, and Y. Zhang. 2019. “Optimization design of micro-piles in landslide safety protection based on machine learning.” Saf. Sci. 118 (Oct): 861–867. https://doi.org/10.1016/j.ssci.2019.06.021.
Liu, L., S. Wang, G. X. Su, B. Hu, Y. X. Peng, Q. Y. Xiong, and J. H. Wen. 2017. “A framework of mining semantic-based probabilistic event relations for complex activity recognition.” Inf. Sci. 418–419 (Dec): 13–33. https://doi.org/10.1016/j.ins.2017.07.022.
Liu, P., X. Qiu, and X. Huang. 2016. “Recurrent neural network for text classification with multi-task learning.” In Proc., 25th Int. Joint Conf. on Artificial Intelligence. Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
Liu, Y. L., Z. L. Chen, J. Wang, B. B. Hu, M. W. Ye, and S. Y. Xu. 2012. “Large-scale natural disaster risk scenario analysis: A case study of Wenzhou City, China.” Nat. Hazard. 60 (3): 1287–1298. https://doi.org/10.1007/s11069-011-9909-2.
Liu, Y. Y., L. Li, W. H. Zhang, P. W. Chan, and Y. S. Liu. 2019. “Rapid identification of rainstorm disaster risks based on an artificial intelligence technology using the 2DPCA method.” Atmos. Res. 227 (Oct): 157–164. https://doi.org/10.1016/j.atmosres.2019.05.006.
Liu, Z. G., X. Y. Li, L. M. Qiao, and D. K. Durrani. 2020a. “A cross-region transfer learning method for classification of community service cases with small datasets.” Knowl.-Based Syst. 193 (Apr): 105390. https://doi.org/10.1016/j.knosys.2019.105390.
Liu, Z.-G., X.-Y. Li, and X.-H. Zhu. 2020b. “Joint risk assessment of the secondary disasters of rainstorms based on multisource spatial data in Wuhan, China.” Nat. Hazard. Rev. 21 (4): 04020033. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000403.
Long, M., J. Wang, G. Ding, J. Sun, and P. S. Yu. 2013. “Transfer feature learning with joint distribution adaptation.” In Proc., IEEE Int. Conf. on Computer Vision. New York: IEEE.
Lopatka, K., and A. Czyzewski. 2014. “Acceleration of decision making in sound event recognition employing supercomputing cluster.” Inf. Sci. 285 (Nov): 223–236. https://doi.org/10.1016/j.ins.2013.11.030.
Lyu, F. N., and L. Zhang. 2019. “Using multi-source big data to understand the factors affecting urban park use in Wuhan.” Urban For. Urban Greening 43 (Jul): 126367. https://doi.org/10.1016/j.ufug.2019.126367.
Ma, X. B., W. Liu, X. Y. Zhou, C. X. Qin, Y. Chen, Y. F. Xiang, X. Y. Zhang, and M. Zhao. 2020. “Evolution of online public opinion during meteorological disasters.” Environ. Hazards 19 (4): 375–397. https://doi.org/10.1080/17477891.2019.1685932.
Mao, H. N., G. Thakur, K. Sparks, J. Sanyal, and B. Bhaduri. 2019. “Mapping near-real-time power outages from social media.” Int. J. Digital Earth 12 (11): 1285–1299. https://doi.org/10.1080/17538947.2018.1535000.
Mikolov, T., I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013. “Distributed representations of words and phrases and their compositionality.” Preprint, submitted October 16, 2013. http://arxiv.org/abs/1310.4546.
Pan, S. J., I. W. Tsang, J. T. Kwok, and Q. A. Yang. 2011. “Domain adaptation via transfer component analysis.” IEEE Trans. Neural Netw. 22 (2): 199–210. https://doi.org/10.1109/TNN.2010.2091281.
Quan, R. S. 2014. “Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation.” Nat. Hazard. 73 (3): 1569–1585. https://doi.org/10.1007/s11069-014-1156-x.
Soyata, T., H. Habibzadeh, C. Ekenna, B. Nussbaum, and J. Lozano. 2019. “Smart city in crisis: Technology and policy concerns.” Sustainable Cities Soc. 50 (Oct): 101566. https://doi.org/10.1016/j.scs.2019.101566.
Tehrany, M. S., S. Jones, and F. Shabani. 2019. “Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques.” Catena 175 (Apr): 174–192. https://doi.org/10.1016/j.catena.2018.12.011.
Thabtah, F., S. Hammoud, F. Kamalov, and A. Gonsalves. 2020. “Data imbalance in classification: Experimental evaluation.” Inf. Sci. 513 (Mar): 429–441. https://doi.org/10.1016/j.ins.2019.11.004.
Tong, H. Z., and M. Ng. 2018. “Analysis of regularized least squares for functional linear regression model.” J. Complexity 49 (Dec): 85–94. https://doi.org/10.1016/j.jco.2018.08.001.
Wang, G. Z., X. Li, X. H. Wu, and J. Yu. 2015. “The rainstorm comprehensive economic loss assessment based on CGE model: Using a July heavy rainstorm in Beijing as an example.” Nat. Hazard. 76 (2): 839–854. https://doi.org/10.1007/s11069-014-1521-9.
Wang, Y. F., H. R. Zhou, Z. L. Jing, L. H. Xiang, and W. J. Cai. 2006. “The exploration of urban gridded management in E-government.” In Proc., 5th Int. Conf. on Grid and Cooperative Computing Workshops. New York: IEEE.
Wang, Y. J., Y. Lei, Y. Zhang, and L. Yao. 2021. “A robust indoor localization method with calibration strategy based on joint distribution adaptation.” Wirel. Netw. 27 (3): 1739–1753. https://doi.org/10.1007/s11276-020-02483-0.
Wang, Z., and X. Sun. 2012. “Optimal kernel marginal Fisher analysis for face recognition.” J. Comput. 7 (9): 2298–2305.
Wu, X. H., Y. R. Cao, Y. Xiao, and J. Guo. 2020. “Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics.” Ann. Oper. Res. 290 (1–2): 865–896. https://doi.org/10.1007/s10479-018-2904-1.
Xiao, M., and Y. Guo. 2012. “Semi-supervised kernel matching for domain adaptation.” In Proc., 26th AAAI Conf. on Artificial Intelligence. Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
Xiao, Y., B. Q. Li, and Z. W. Gong. 2018. “Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data.” Nat. Hazard. 94 (2): 833–842. https://doi.org/10.1007/s11069-018-3427-4.
Xu, D., S. C. Yan, D. C. Tao, S. Lin, and H. J. Zhang. 2007. “Marginal Fisher analysis and its variants for human gait recognition and content-based image retrieval.” IEEE Trans. Image Process. 16 (11): 2811–2821. https://doi.org/10.1109/TIP.2007.906769.
Xu, P. D., Y. Deng, X. Y. Su, and S. Mahadevan. 2013. “A new method to determine basic probability assignment from training data.” Knowl.-Based Syst. 46 (Jul): 69–80. https://doi.org/10.1016/j.knosys.2013.03.005.
Zhang, Z., X. Han, Z. Liu, X. Jiang, M. Sun, and Q. Liu. 2019. “ERNIE: Enhanced language representation with informative entities.” In Proc., 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics.
Zhou, S., E. Smirnov, G. Schoenmakers, and R. Peeters. 2017. “Conformity-based source subset selection for instance transfer.” Neurocomputing 258 (Oct): 41–51. https://doi.org/10.1016/j.neucom.2016.11.071.
Zhu, X., X. Li, and Z. Liu. 2020. “Constructing scenario dimension model of city waterlogging under big data environment.” Geomatics Inf. Sci. Wuhan Univ. 45 (11): 1818. https://doi.org/10.13203/j.whugis20190225.
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Published In
Natural Hazards Review
Volume 24 • Issue 1 • February 2023
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© 2022 American Society of Civil Engineers.
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Received: Jul 14, 2021
Accepted: Jun 28, 2022
Published online: Nov 26, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 26, 2023
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