29 Undergraduate Research, Part II (Posters)

Session showcases undergraduate research. Topics include geoscience-related fields (e.g., resources, hydrology, oceanography, climate, environmental science, geospatial, informatics, health, policy, & education). Undergraduate student/s must be the lead author/s presenters with faculty mentor/s as co-author/s. This session welcomes both poster and oral presentation submissions.

Tuesday, 10 March 2026
9:00 AM - 5:00 PM
RCC, Lower Level Hall

Precipitation Prediction Using Machine Learning, Deep Learning, and Time Series Models: A Case Study in Milwaukee, Wisconsin

Paper number: 29-13
Program: Technical Sessions
Time: 9:00 AM – 9:00 AM

Authors

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

doi: 10.1130/abs/2026SE-411670

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