Evaluation Of Machine Learning Methods For Flood Forecasting In Piedmont And Coastal Areas Of North Carolina

Flooding is one of the most frequent and destructive natural disasters, posing significant risks to communities, infrastructure, and economies worldwide. In North Carolina, diverse flood types—driven by varied topographic and climatic conditions—necessitate adaptable forecasting methods. Traditional flood prediction models rely on hydrological and meteorological data but often struggle to incorporate the complexity of different flood types within a single framework. This research explored the application of machine learning (ML) techniques in flood forecasting, specifically evaluating how different ML models handle varying data requirements across different regions with flood types.
Focusing on the Upper Haw River and Cape Fear River watersheds in North Carolina, this study employed a Convolutional Neural Network (CNN) to predict flood stages using hydrometeorological data, including rainfall, elevation, distance from the river, soil type, land use/cover, wind speed, wind direction, soil water volume, lag feature, and gauge stages. Rather than comparing ML accuracy against traditional hydrological models, this study examined how ML models can adapt to diverse flood conditions and data constraints. The results indicate that CNN-based models effectively capture spatial dependencies and patterns, providing valuable insights into the role of different input features, such as lag effects and rainfall distribution, in flood prediction.
A Command Line Interface (CLI) was developed to enable real-time interaction with the model, enhancing its usability for decision-makers. The study highlighted the strengths and limitations of ML-based forecasting, demonstrating its potential while identifying areas requiring further refinement, such as incorporating additional meteorological variables and real-time data. Aimed to evaluate the feasibility of using ML for statewide flood prediction to encompass a range of flood causes, this research also contributed to determining the boundaries of coastal and piedmont regions and the type of data requirements to develop flood forecasting models in these regions.