Forecasting extreme rainfall events of the USA

Music City Center 

Rainfall is the most important factor in the hydrological system, and it plays a vital role in managing and planning water resources and associated issues. Very few studies have been done to study extreme precipitation by machine learning or deep learning technique. It has been widely proven that predictive model using deep learning has outperformed significantly than other previously existing methods. In this project, we will implement long short-term memory (LSTM) to develop a model to analyze and forecast extreme rainfall events in the US. LSTM is a special type of RNN with memory structures for learning long‐term information. Historically observed daily rainfall data from 1950 to 2022 for the USA obtained from the Historical Climatology Network (HCN) will be used for analysis. After implementing some filtering criteria for each rain gauge station, a total of 1108 rain gauge stations. The performance of the model has been evaluated based on the criterions RMSE, MAPE, and R to identify the potential best model among proposed models. Our results show that the developed model explains more than 95% of variability and can predict extreme precipitation events of the USA.

Extreme rainfall events

LSTM

Climatology 

Section on Statistics and the Environment