Climate change causes extreme weather in Macao, especially typhoons and flooding. In this paper, some raw flood data is missing from the Macao Meteorological and Geophysical Bureau, due to some flood sensors that were damaged during Typhoon Hato in 2017 and Typhoon Mangkhut in 2018. So we use data interpolation to construct new datasets and curve fitting to simulate real inundation depth. Besides this, we explore Neural Network, Long Short-Term Memory, Random Forest, Adaptive Boosting, and Linear Regression for analyzing, comparing, and evaluating the best combinations of flood prediction models, datasets, and scenarios caused by typhoon presence in Macao. Furthermore, we apply Bayes Network to the aforementioned models to evaluate the accuracy of predicting flood situations because of typhoons. The experiment results show that the different models achieve a different performance in predicting specific scenarios.
|IOP Conference Series: Earth and Environmental Science
|Published - 17 May 2021
|2021 2nd International Conference on Environment Science and Advanced Energy Technologies, ESAET 2021 - Chongqing, China
Duration: 6 Mar 2021 → 7 Mar 2021
- Bayes Network
- Flood Predicting
- Machine Learning