Comparing and Evaluating Macao Flood Prediction Models

Zeyu Zhang, Jiayue Qiu, Xuefei Huang, Zhiming Cai, Linkai Zhu, Weijun Dai

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number022001
JournalIOP Conference Series: Earth and Environmental Science
Volume769
Issue number2
DOIs
Publication statusPublished - 17 May 2021
Event2021 2nd International Conference on Environment Science and Advanced Energy Technologies, ESAET 2021 - Chongqing, China
Duration: 6 Mar 20217 Mar 2021

Keywords

  • Bayes Network
  • Flood Predicting
  • Machine Learning
  • Typhoon

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