TY - GEN
T1 - Disaster Data-Driven Forecasting for Preemptive Crisis Response
AU - Lambe, Zafeer
AU - Alshehri, Jumanah S.
AU - Obradovic, Zoran
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2025.
PY - 2025
Y1 - 2025
N2 - Delays in providing humanitarian aid often stem from a lack of sufficient information about the quantity of aid required for disaster-stricken regions. Responding effectively to disasters begins with understanding the needs of affected communities. This research develops a predictive model to forecast aid requirements using historical data from 2002 to 2022. By integrating multiple datasets—Emergency Events Database (EM-DAT) for disaster statistics, Global Unique Disaster Identifier (GLIDE) and ReliefWeb for event tracking, and Organization for Economic Co-operation and Development (OECD) for aid-related information, we analyze how aid is distributed across different contexts. We examined key features such as population size, disaster frequency, and temporal factors (e.g., months and dates) to assess their impact on changes in aid allocation. Our best-performing model, XGBoost optimized with GridSearchCV, achieved an R2 score of 0.86, indicating high predictive accuracy. This study provides a data-driven framework to improve the efficiency of aid allocation, offering decision-makers reliable estimates to prioritize timely and appropriate responses.
AB - Delays in providing humanitarian aid often stem from a lack of sufficient information about the quantity of aid required for disaster-stricken regions. Responding effectively to disasters begins with understanding the needs of affected communities. This research develops a predictive model to forecast aid requirements using historical data from 2002 to 2022. By integrating multiple datasets—Emergency Events Database (EM-DAT) for disaster statistics, Global Unique Disaster Identifier (GLIDE) and ReliefWeb for event tracking, and Organization for Economic Co-operation and Development (OECD) for aid-related information, we analyze how aid is distributed across different contexts. We examined key features such as population size, disaster frequency, and temporal factors (e.g., months and dates) to assess their impact on changes in aid allocation. Our best-performing model, XGBoost optimized with GridSearchCV, achieved an R2 score of 0.86, indicating high predictive accuracy. This study provides a data-driven framework to improve the efficiency of aid allocation, offering decision-makers reliable estimates to prioritize timely and appropriate responses.
KW - Aid Prediction Model
KW - Crisis Management
KW - Disaster Preparedness
KW - Disaster Response
KW - Humanitarian aid
UR - https://www.scopus.com/pages/publications/105010210738
U2 - 10.1007/978-3-031-96228-8_10
DO - 10.1007/978-3-031-96228-8_10
M3 - Conference contribution
AN - SCOPUS:105010210738
SN - 9783031962271
T3 - IFIP Advances in Information and Communication Technology
SP - 126
EP - 140
BT - Artificial Intelligence Applications and Innovations - 21st IFIP WG 12.5 International Conference, AIAI 2025, Proceedings
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - Papaleonidas, Antonios
A2 - Andreou, Andreas
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2025
Y2 - 26 June 2025 through 29 June 2025
ER -