Predictive Agent-Based Modeling of Natural Disasters Using Machine Learning


  • Favour Nerrise University of Maryland, College Park


Machine Learning, Multiagent Systems, Geospatial Features, LSTM


Current applications of Agent-based Modeling (ABM) in natural phenomena like wildfire land suppression and hurricane forecasting are in monitoring emergent behavior patterns among large groups of people (Hilljegerdes 2018). However, current evacuation times and plans for natural disaster management leave underserved communities vulnerable to substantial financial and welfare losses, especially when false positives during current predictions continue to influence evacuation decisions. A Machine Learning ABM (ML-ABM) model of hurricane trajectories introduces a pioneering opportunity to capture complex physical processes associated with hurricanes while minimizing computational costs and errors, thereby providing more accurate real-time prediction of hurricanes for improved disaster management. This Hurricane Track Prediction ML-ABM model aims to quickly model and predict hurricane tracks in only a few minutes while retaining some of the complex physical process interactions of real storms through feature engineering and deep learning. This work focuses on the implementation of an RNN with bidirectional time-distributed Long-Short Term Memory cells, accounting for positive and negative time direction in time series forecasting. The observations and predictions were represented as a multi-agent system in NetLogo for further emergent pattern analysis in an expanded research by Arthur Drake et. al (2020). The model also evaluates benchmark comparisons against the National Hurricane Center’s 5-Year Average Forecast Errors and the BCD5 Error Model, a combined intensity and track prediction error model that utilizes best track input and models decay over land.




How to Cite

Nerrise, F. . (2021). Predictive Agent-Based Modeling of Natural Disasters Using Machine Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15976-15977. Retrieved from



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