Structured exploration of machine learning model complexity for spatio-temporal forecasting of urban flooding

Abstract. Urban flooding may lead to significant socio-economic impacts and loss of life. To afford preventative actions, researchers have implemented various modeling techniques to gain insight into urban flood occurrences. Using New York City (NYC) as the study area, data-driven techniques, specifically statistical and neural network models with increasing spatio-temporal complexity, are formulated and tested, assessing the potential relative contribution of different modeling constructs. Zones, based on flood characteristics, are first delineated using the unsupervised machine learning technique of spectral clustering. Then, the models are applied to each cluster, with comprehensive performance evaluation, as to understand which algorithmic, structural aspects contribute to the reduction of prediction errors. A chief discovery of this study is the emergence of the Graph Wavenet (GWN) as the most effective model due to its proficiency in capturing spatio-temporal aspects and implementing dynamic graph creation. Furthermore, it is seen that the enhancement of specific temporal and spatial components within a modeling technique proves beneficial, and a novel adoption of graph-based architectures is additive. Offering a unique exploration of spatio-temporal aspects, emphasizing the benefits of component enhancement and the adoption of graph-based architectures, this paper identifies modification techniques, which would allow for insights to prevail in urban flood modeling despite being confronted with limited data availability.