Preprints
https://doi.org/10.5194/npg-2021-36
https://doi.org/10.5194/npg-2021-36
 
04 Jan 2022
04 Jan 2022
Status: this preprint is currently under review for the journal NPG.

Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta

Joko Sampurno1,2, Valentin Vallaeys1, Randy Ardianto3, and Emmanuel Hanert1,4 Joko Sampurno et al.
  • 1Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
  • 2Department of Physics, Fakultas MIPA, Universitas Tanjungpura, Pontianak, 78124, Indonesia
  • 3Pontianak Maritime Meteorological Station, Pontianak, 78111, Indonesia
  • 4Institute of Mechanics, Materials and Civil Engineering (IMMC), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium

Abstract. Flood forecasting based on water level modeling is an essential non-structural measure against compound flooding over the globe. With its vulnerability increased under climate change, every coastal area became urgently needs a water level model for better flood risk management. Unfortunately, for local water management agencies in developing countries building such a model is challenging due to the limited computational resources and the scarcity of observational data. Here, we attempt to solve the issue by proposing an integrated hydrodynamic and machine learning approach to predict compound flooding in those areas. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we built a hydrodynamic model to simulate several compound flooding scenarios, and the outputs are then used to train the machine learning model. To obtain a robust machine learning model, we consider three machine learning algorithms, i.e., Random Forest, Multi Linear Regression, and Support Vector Machine. The results show that this integrated scheme is successfully working. The Random Forest performs as the most accurate algorithm to predict flooding hazards in the study area, with RMSE = 0.11 m compared to SVM (RMSE = 0.18 m) and MLR (RMSE = 0.19 m). The machine-learning model with the RF algorithm can predict ten out of seventeen compound flooding events during the testing phase. Therefore, the random forest is proposed as the most appropriate algorithm to build a reliable ML model capable of assessing the compound flood hazards in the area of interest.

Joko Sampurno et al.

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Joko Sampurno et al.

Joko Sampurno et al.

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Short summary
In this study, we succeeded in building and evaluating a machine learning model for flood forecasting that did not require extensive observational data in its training phase and did not need high computational cost in its implementation. The issues that we tackled are data scarcity and low computational resources for building flood forecasting models. The proposed approach is suitable for local water management agencies in developing countries that generally encounter those kinds of issues.