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29-14 Precipitation Prediction Using Machine Learning, Deep Learning, and Time Series Models: A Case Study in Milwaukee, Wisconsin
Session: Undergraduate Research, Part II (Posters)
Poster Booth No.: 65
Presenting Author:
Megan Snow
Authors:
Snow, Megan1, Mukherjee, Arindam2
(1) Ohio University, Athens, Ohio, USA, (2) Ohio University, Athens, Ohio, USA,
Abstract:
This study aims to comparatively assess the efficacy of machine learning (ML), deep learning (DL), and time series modeling approaches for predicting precipitation patterns in Milwaukee, Wisconsin. We intend to compare the performance of ML and DL methods against traditional time series techniques commonly used in meteorological predictions. The model performances will be evaluated based on their key performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE).
Geological Society of America Abstracts with Programs. Vol. 58, No. 1, 2026