Supervisor
Dr. Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Subject
Computer Science
Abstract
Climate change is generating unprecedented economic losses through extreme weather events, yet comparative predictive analysis between different hazard types remains limited. This report presents a Machine Learning–Based Comparative Economic Impact Modelling System designed to predict and compare the economic damages caused by floods and droughts on a global scale. The system integrates historical disaster records from the EM-DAT International Disaster Database with country-level socioeconomic indicators from the World Bank's World Development Indicators, applying supervised regression modelling to a unified dataset of 1,301 disaster events across 133 countries from 2000 to 2025. XGBoost and a Multi-Layer Perceptron Neural Network were evaluated as candidate models, with XGBoost selected as the final model based on superior overall performance. The system was deployed as an interactive Gradio web application enabling single-event prediction, country-to-country comparison, and global risk mapping across flood and drought scenarios. The project follows the full six-phase CRISP-DM framework, documenting one structured iteration between phases triggered by modelling findings.
Date of Award
2026
Full Publication Date
2026
Access Rights
open access
Document Type
Undergraduate Project
Resource Type
bachelor thesis
Recommended Citation
Lemos Aguiar, D., & Oliveira Machado, P.
(2026) Comparative Economic Impact Modelling of Floods vs Drought on a Global Scale CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.142