Abstract
The most frequent flooding situations in the Azores are caused by torrential rainfall, which is difficult to predict due to its characteristics. Using data collected by sensors, rainfall and stream flow values, from natural occurring flood events, we describe results from learning RNNs, ARIMA time series forecast model, and a warning system that can empower civil protection decision-makers to safeguard property and lives. For dealing with all the difficulties resulting from forecasting rare events undersampling are used to get a richer sample of positive events, combined with simulation of new events. GRU and ARIMA models performed better than LSTM, using the hit hate measure and 30% of the positive events as test sample and 70% for learning sample. Even though the alert messages are sent by SMS to relevant deciders, two apps were developed to deploy the forecast models. An WWW application to manage the alerts and the sensors spread by all the islands, and a mobile app for operational staff working in all Azores archipelago.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jacques-Dumas, V., Ragone, F., Borgnat, P., Abry, P., Bouchet, F.: Deep learning-based extreme heatwave forecast. Front. Clim. 4, 789641 (2022). https://www.frontiersin.org/articles/10.3389/fclim.2022.789641
Dueben, P.D., Bauer, P.: Challenges and design choices for global weather and climate models based on machine learning. Geosci. Model Dev. 11(10), 3999–4009 (2018). https://doi.org/10.5194/gmd-11-3999-2018
Scher, S., Messori, G.: Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground. Geosci. Model Dev. 12(7), 2797–2809 (2019). https://doi.org/10.5194/gmd-12-2797-2019
Weyn, J.A., Durran, D.R., Caruana, R.: Can machines learn to predict weather? Using deep learning to predict gridded 500-hPa geopotential height from historical weather data. J. Adv. Model. Earth Syst. 11(8), 2680–2693 (2019). https://doi.org/10.1029/2019MS001705
Chattopadhyay, A., Nabizadeh, E., Hassanzadeh, P.: Analog forecasting of extreme-causing weather patterns using deep learning. J. Adv. Model. Earth Syst. 12(2), e2019MS001958 (2020). https://doi.org/10.1029/2019MS001958
McGovern, A., et al.: Using artificial intelligence to improve real-time decision-making for high-impact weather. Bull. Am. Meteor. Soc. 98(10), 2073–2090 (2017). https://doi.org/10.1175/BAMS-D-16-0123.1
Karevan, Z., Suykens, J.A.K.: Transductive LSTM for time-series prediction: an application to weather forecasting. Neural Netw. 125, 1–9 (2020). https://doi.org/10.1016/j.neunet.2019.12.030
Pratt, L., Thrun, S.: Guest editors’ introduction. Mach. Learn. 28(1), 5 (1997). https://doi.org/10.1023/A:1007322005825
Krawczyk, B.: Learning from imbalanced data: open challenges and future directions. Prog. Artif. Intell. 5(4), 221–232 (2016). https://doi.org/10.1007/s13748-016-0094-0
Johnson, J.M., Khoshgoftaar, T.M.: The effects of data sampling with deep learning and highly imbalanced Big Data. Inf. Syst. Front. 22(5), 1113–1131 (2020). https://doi.org/10.1007/s10796-020-10022-7
Yadav, S., Jain, A., Sharma, K.C., Bhakar, R.: Load forecasting for rare events using LSTM. In: 2021 9th IEEE International Conference on Power Systems (ICPS), December 2021, pp. 1–6 (2021). https://doi.org/10.1109/ICPS52420.2021.9670200
Silver, N.: The Signal and the Noise. Penguin Books (2013)
Kelleher, J.D.: Deep Learning. The MIT Press Essential Knowledge. The MIT Press, Cambridge (2019)
VipulNaik: Forecasting rare events. https://www.lesswrong.com/posts/Yb26htyo6SMirnzqt/forecasting-rare-events. Accessed 12 Jun 2023
Mills, T.C.: Time Series Econometrics - A Concise Introduction, 1st edn., vol. 1. Palgrave Macmillan (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Moura, R., Mendes, A., Cascalho, J., Mendes, S., Melo, R., Barcelos, E. (2024). Predicting Flood Events with Streaming Data: A Preliminary Approach with GRU and ARIMA. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981. Springer, Cham. https://doi.org/10.1007/978-3-031-53025-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-53025-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53024-1
Online ISBN: 978-3-031-53025-8
eBook Packages: Computer ScienceComputer Science (R0)