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

Abstract

Flood forecasting based on hydrodynamic modeling is an essential non-structural measure against compound flooding across the globe. With the risk increasing under climate change, all coastal areas are now in need of flood risk management strategies. 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. We attempt to solve this issue by proposing an integrated hydrodynamic and machine learning (ML) approach to predict water level dynamics as a proxy for the risk of compound flooding in a data-scarce delta. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we build a hydrodynamic model to simulate several compound flooding scenarios. The outputs are then used to train the ML model. To obtain a robust ML model, we consider three ML algorithms, i.e., random forest (RF), multiple linear regression (MLR), and support vector machine (SVM). Our results show that the integrated scheme works well. The RF is the most accurate algorithm to model water level dynamics in the study area. Meanwhile, the ML model using the RF algorithm can predict 11 out of 17 compound flooding events during the implementation phase. It could be concluded that RF is the most appropriate algorithm to build a reliable ML model capable of estimating the river's water level dynamics within Pontianak, whose output can be used as a proxy for predicting compound flooding events in the city.