Hybrid Machine Learning models with Principal Component Analysis for Multi-Step-Ahead Urban Flood Inundation
Abstract
Due to climate change and urbanization, the frequency and severity of flood disasters have increased in recent years. To reduce damage losses, governments around the globe have paid great attention to flood warnings and disaster response to respond to floods earlier to minimize the impact of disasters. This study proposed a new methodology that combines Principal Component Analysis (PCA), Self-Organizing Map (SOM), and Nonlinear Autoregressive with Exogenous Inputs (R-NARX) to establish urban flood forecast models. The PCA was performed on flood inundation simulation data to obtain four principal components representing the different spatial distributions of inundation. According to the characteristics of flood events, the SOM was used to cluster the gridded inundation simulation data into the neurons of a two-dimensional topological feature map, where each neuron in the topological map represented the average depth and spatial distribution of inundation grids. The R-NARX model used its feedback value and rainfall data as inputs to establish a flood forecast model for the next hour, with a forecast horizon of 10 minutes (i.e., T+1 T+6). We collected the rainfall patterns of flood events in Taipei in recent years. We used the SOBEK model for making 2-D inundation simulation of actual rainfall events and designed ones (51 events), where a total of 2047 datasets were generated, and each dataset contained 45101 grids of inundation depths in the study area for illustrating the rainfall-flooding process. The datasets were then used to construct the machine learning models, where 23 events (919 datasets) were used for training, 14 events (564 datasets) were used for validating, and 14 events (564 records) were used for testing. The results demonstrate the proposed methodology that integrates PCA (for characterizing the spatial distribution of flood inundation) with the SOM and R-NARX models (for forecasting flood inundation depths) can grasp the inundation status caused by the different spatial distribution of rainfall to provide real-time flood inundation forecasts at urban areas. The proposed approach can help decision-makers respond to floods earlier and reduce the impact of disasters.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMNH35F..09C