Machine Learning Approach to Rainfall Prediction in the Oliwski Stream Watershed, Gdańsk

Climate change in the Gdańsk region, particularly in terms of precipitation, is marked by an increase in the intensity of individual rainfall events, while the annual total precipitation remains relatively stable. High-intensity rainfall often triggers flood surges, as seen in two major episodes in July 2016 and 2017, which caused flash floods in the catchments of streams flowing through the city.

In this context, accurate precipitation forecasting is crucial for safeguarding the city against flooding. This study aims to predict precipitation over the Oliwski Stream watershed using data-driven machine learning techniques, focusing on hourly and daily precipitation prediction. The dataset comprises observed temperature and rainfall data from three stations surrounding the watershed, sourced from the municipal monitoring system (Oliwa IBW and Matemblewo stations) and the national meteorological network (Gdańsk Airport), covering the period from 2005 to 2024.

Three machine learning regression models—Artificial Neural Network Multilayer Perceptron (ANN-MLP), Multiple Linear Regression (MLR), and Random Forest (RF)—will be applied for rainfall forecasting. Model performance will be evaluated using statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). This study will be helpful for water managers and researchers in the future.