Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.

Application of Machine Learning to predict groundwater levels in Southeast Florida
Miami-Dade County has complex hydrological and climate conditions, creating challenges
for predicting groundwater levels. Due to the potential impacts of increasing sea level and changes
in rain patterns on flooding and saltwater intrusion for example, it is essential to be able to predict
groundwater levels with more accuracy and to understand the drivers of groundwater level. The
method traditionally used to predict groundwater levels in South Florida and elsewhere is
implementing a numerical model. This research looks at machine learning techniques as an
alternative method to provide improved predictions of groundwater levels. Extreme Gradient
Boosting is one of the machine learning techniques that uses algorithms to train a model by
identifying hidden patterns and relationships between groundwater level drivers and groundwater
levels. The accuracy of the Extreme Gradient Boosting model and numerical model are compared
by computing the Root Mean Square error as a model performance measure. Comparing these two
methods showed that the Extreme Gradient Boosting model had better accuracy in predicting
groundwater levels at the majority of groundwater monitoring sites. Furthermore, comparing these
two models helped us identify problematic areas where both models performed poorly and narrow
down the factors that could cause this poor model performance. Finally, the Extreme Gradient
Boosting method gave us the importance of each feature included among the potential groundwater
level drivers, including the ranked importance of a particular factor and its lag time, at each of the
considered groundwater level monitoring sites.

All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, however, important regional differences, resulting from distinct processes at different timescales (temperature-driven changes being a major contributor on multi-year timescales). In view of this complexity, it deems essential to move towards more sophisticated data-driven techniques as well as diagnostic and prognostic prediction models to interpret observations of ocean warming and sea level variations at local or regional sea basins. In this context, we present a machine learning approach that exploits key ocean temperature estimates (as proxies for the regional thermosteric sea level component) to model coastal sea level variability and associated uncertainty across a range of timescales (from months to several years). Our findings also demonstrate the utility of machine learning to estimate the possible tendency of near-future regional sea levels. When compared to actual sea-level records, our models perform particularly well in the coastal areas most influenced by internal climate variability. Yet, the models are widely applicable to evaluate the patterns of rising and falling sea levels across many places around the globe. Thus, our approach is a promising tool to model and anticipate sea level changes in the coming (1–3) years, which is crucial for near-term decision making and strategic planning about coastal protection measures.