Abstract
Early warning systems (EWSs) are designed to effectively and efficiently disseminate appropriate information related to disaster events, in the form of alarms or warnings, to vulnerable communities before or during a disaster so that proactive and preventive measures can be taken to minimize the loss and damage associated with such events. With the advent of the Internet of Things and the advanced technology driven sensor devices, large amounts of data are getting generated at a rapid speed. This data needs to be captured, stored and analyzed by EWS, since it possesses useful indicators and could provide enormous opportunities for monitoring and managing both natural and man-made disasters. The use of artificial intelligence (AI) can enable EWS to mine early warning signals from this data, so that proactive and preventive measures for disaster mitigation, preparedness, response and recovery can be planned leading to timely alerts and warnings being disseminated to the relevant stakeholders. In this paper, an overview of EWS and AI based machine learning techniques capable of being used for designing such systems are discussed. Further, an overview of various real world examples of AI based EWS are also outlined.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali, M. M., Kishtawal, C. M., & Jain, S. (2007). Predicting Cyclone Tracks in the North Indian Ocean: An Artificial Neural Network Approach. Geophysical Research Letters, 34(4).
Bahrepour, M., Meratnia, N., Poel, M., Taghikhaki, Z., & Havinga, P. J. M. (2010). Distributed Event Detection in Wireless Sensor Networks for Disaster Management. 2010 International Conference on Intelligent Networking and Collaborative Systems, 507–512.
Basher, R. (2006). Global Early Warning Systems for Natural Hazards: Systematic and People-Centred. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2167–2182.
Bischke, B., Bhardwaj, P., Gautam, A., Helber, P., Borth, D., & Dengel, A. (2017). Detection of Flooding Events in Social Multimedia and Satellite Imagery Using Deep Neural Networks. MediaEval.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.
Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (2005). At Risk: Natural Hazards, People’s Vulnerability and Disasters. Routledge.
Carrara, A., Guzzetti, F., Cardinali, M., & Reichenbach, P. (1999). Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard. Natural Hazards, 20(2–3), 117–135.
Chatfield, A., & Brajawidagda, U. (2012). Twitter Tsunami Early Warning Network: A Social Network Analysis of Twitter Information Flows.
Cheekiralla, S. (2005). Wireless Sensor Network-Based Tunnel Monitoring. Proceedings of the Real WSN Workshop.
Cheng, J., & Yang, S. (2012). Data Mining Applications in Evaluating Mine Ventilation System. Safety Science, 50(4), 918–922.
Cheng, J., Yang, S., & Luo, Y. (2010). Mathematical Models for Optimizing and Evaluating Mine Ventilation System. Proceedings of the 13th United States/North American Mine Ventilation Symposium, Sudbury, ON, Canada, 13–16.
Cover, T. M., & Hart, P. E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
Cox, D. R. (1958). The Regression Analysis of Binary Sequences. Journal of the Royal Statistical Society: Series B (Methodological), 20(2), 215–232.
DeVries, P. M. R., Viégas, F., Wattenberg, M., & Meade, B. J. (2018). Deep Learning of Aftershock Patterns Following Large Earthquakes. Nature, 560(7720), 632.
Ford, T. E., Colwell, R. R., Rose, J. B., Morse, S. S., Rogers, D. J., & Yates, T. L. (2009). Using Satellite Images of Environmental Changes to Predict Infectious Disease Outbreaks. Emerging Infectious Diseases, 15(9), 1341.
Gens, R. (2010). Remote Sensing of Coastlines: Detection, Extraction and Monitoring. International Journal of Remote Sensing, 31(7), 1819–1836.
Genzel, D. (2016). What Is the Difference Between AI, Machine Learning, NLP, and Deep Learning? Retrieved from Quora website: https://www.quora.com/What-is-the-difference-between-AI-Machine-Learning-NLP-and-Deep-Learning/answer/Dmitriy-Genzel
Gillespie, T. W., Chu, J., Frankenberg, E., & Thomas, D. (2007). Assessment and Prediction of Natural Hazards from Satellite Imagery. Progress in Physical Geography, 31(5), 459–470.
Gourgey, B. (2018). How Artificial Intelligence Could Prevent Natural Disasters. Wired. Retrieved from https://www.wired.com/story/how-artificial-intelligence-could-prevent-natural-disasters/
Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. Prentice Hall PTR.
Ho, T. K. (1995). Random Decision Forests. Proceedings of 3rd International Conference on Document Analysis and Recognition, 1, 278–282.
Hu, L.-Y., Huang, M.-W., Ke, S.-W., & Tsai, C.-F. (2016). The Distance Function Effect on K-Nearest Neighbor Classification for Medical Datasets. Springerplus, 5(1), 1304.
IFRC. (2018). What Is a Disaster? Retrieved from https://www.ifrc.org website: https://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/what-is-a-disaster/
Jo, B., & Khan, R. (2017). An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study. Applied Sciences, 7(9), 925.
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised Machine Learning: A Review of Classification Techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160, 3–24.
Kung, H.-Y., Chen, C.-H., & Ku, H.-H. (2012). Designing Intelligent Disaster Prediction Models and Systems for Debris-Flow Disasters in Taiwan. Expert Systems with Applications, 39(5), 5838–5856.
Langley, P., Iba, W. I., & Thompson, K. (1992). An Analysis of Bayesian Classiers. Proceedings of the Tenth National Conference on Artificial Intelligence, 90(415), 223–228.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436.
Li, M., & Liu, Y. (2009). Underground Coal Mine Monitoring with Wireless Sensor Networks. ACM Transactions on Sensor Networks, 5(2), 10:1–10:29. https://doi.org/10.1145/1498915.1498916
Liu, P. L.-F., Wang, X., & Salisbury, A. J. (2009). Tsunami Hazard and Early Warning System in South China Sea. Journal of Asian Earth Sciences, 36(1), 2–12.
Liu, T., & Yang, X. (2015). Monitoring Land Changes in an Urban Area Using Satellite Imagery, GIS and Landscape Metrics. Applied Geography, 56, 42–54.
Lohr, S. (2016, February). The Promise of Artificial Intelligence Unfolds in Small Steps. The New York Times. Retrieved from https://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html
Matias, Y. (2018). Keeping People Safe with AI-Enabled Flood Forecasting. Retrieved from Google Blog website: https://www.blog.google/products/search/helping-keep-people-safe-ai-enabled-flood-forecasting/
Nayak, S., & Zlatanova, S. (2008). Remote Sensing and GIS Technologies for Monitoring and Prediction of Disasters. Springer.
Poompavai, V., & Ramalingam, M. (2013). Geospatial Analysis for Coastal Risk Assessment to Cyclones. Journal of the Indian Society of Remote Sensing, 41(1), 157–176.
PTI. (2018). IBM to Invest in Tech to Predict Floods, Cyclones in India. The Economic Times. Retrieved from https://economictimes.indiatimes.com/tech/software/ibm-to-invest-in-tech-to-predict-floods-cyclones-in-india/articleshow/64319639.cms
Pyayt, A. L., Mokhov, I. I., Lang, B., Krzhizhanovskaya, V. V., Meijer, R. J., et al. (2011). Machine learning methods for environmental monitoring and flood protection. World Academy of Science, Engineering and Technology, 78, 118–123.
Rouse, M. (2010, November). AI (Artificial Intelligence). SearchEnterpriseAI. Retrieved from https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence
Ruder, S. (2016). An Overview of Gradient Descent Optimization Algorithms. ArXiv Preprint ArXiv:1609.04747.
Rudloff, A., Lauterjung, J., Münch, U., & Tinti, S. (2009). Preface “The GITEWS Project (German-Indonesian Tsunami Early Warning System)”. Natural Hazards and Earth System Sciences, 9(4), 1381–1382.
Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Boston: Pearson Education Limited.
Sahoo, B., & Bhaskaran, P. K. (2019). Prediction of Storm Surge and Coastal Inundation Using Artificial Neural Network – A Case Study for 1999 Odisha Super Cyclone. Weather and Climate Extremes, 23, 100196.
Sebastiani, F. (2002). Machine Learning in Automated Text Categorization. ACM Computing Surveys (CSUR), 34(1), 1–47.
Tadesse, T., Brown, J. F., & Hayes, M. J. (2005). A New Approach for Predicting Drought-Related Vegetation Stress: Integrating Satellite, Climate, and Biophysical Data over the US Central Plains. ISPRS Journal of Photogrammetry and Remote Sensing, 59(4), 244–253.
The Disaster Management Act. (2005). Retrieved from https://www.ndmindia.nic.in/images/The Disaster Management Act, 2005.pdf
TheEconomist. (2016). Why Firms Are Piling into Artificial Intelligence. The Economist Newspaper Limited. Retrieved from https://www.economist.com/the-economist-explains/2016/03/31/why-firms-are-piling-into-artificial-intelligence
Twigg, J. (2003). The Human Factor in Early Warnings: Risk Perception and Appropriate Communications. In Early Warning Systems for Natural Disaster Reduction (pp. 19–26). Springer.
Vogelmann, J. E., Kost, J. R., Tolk, B., Howard, S., Short, K., Chen, X., et al. (2010). Monitoring Landscape Change for LANDFIRE Using Multi-Temporal Satellite Imagery and Ancillary Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2), 252–264.
Walker, S. H., & Duncan, D. B. (1967). Estimation of the Probability of an Event as a Function of Several Independent Variables. Biometrika, 54(1–2), 167–179.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
World Health Organization International. (2002). Disasters & Emergencies: Definitions. Retrieved from http://apps.who.int/disasters/repo/7656.pdf
Zhao, Y., & Wang, H. (2009). Study on Ventilation System Reliability Early-Warning Based on RS-ANN. 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 3, 598–602.
Zhou, J., Pei, H., & Wu, H. (2018). Early Warning of Human Crowds Based on Query Data from Baidu Maps: Analysis Based on Shanghai Stampede. In Z. Shen & M. Li (Eds.), Big Data Support of Urban Planning and Management: The Experience in China (pp. 19–41). https://doi.org/10.1007/978-3-319-51929-6_2
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Author(s)
About this chapter
Cite this chapter
Lamsal, R., Kumar, T.V.V. (2020). Artificial Intelligence and Early Warning Systems. In: Kumar, T.V.V., Sud, K. (eds) AI and Robotics in Disaster Studies. Disaster Research and Management Series on the Global South. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-15-4291-6_2
Download citation
DOI: https://doi.org/10.1007/978-981-15-4291-6_2
Published:
Publisher Name: Palgrave Macmillan, Singapore
Print ISBN: 978-981-15-4290-9
Online ISBN: 978-981-15-4291-6
eBook Packages: Business and ManagementBusiness and Management (R0)