Application of decision tree algorithm for climate change risk assessment (EMA). · Electronic Theses and Dissertations

Author: Sithole Kundai
Title: Application of decision tree algorithm for climate change risk assessment (EMA).
Abstract: ABSTRACT

Our world is being threatened by climate change, which is far-reaching effects and necessitates quick response. I describe in this thesis the creation and application of a revolutionary decision tree algorithm for thorough risk assessment related to climate change.
Utilizing cutting-edge machine learning methods, the suggested program examines a wide range of environmental, socioeconomic, and geopolitical data. It makes it possible to accurately estimate the effects of climate change on a variety of sectors, from infrastructure and agriculture to public health and national security, by spotting complex patterns and interdependencies.
This work’s principal innovations are as follows:
1. A strong pipeline for preprocessing and data aggregation that unifies various data sources to produce an extensive knowledge base
2. A hierarchical decision tree model with adaptive learning capabilities to understand the intricate connections between the many sources of climate change and their various effects.
3. Tools for comprehensive risk quantification and visualization that give decision-makers practical advice on risk reduction and adaptation
Through case studies in multiple susceptible places, the efficacy of the algorithm is illustrated, demonstrating that it outperforms current risk assessment approaches in terms of accuracy, adaptability and scalability.
Date: JUNE 2024
Publisher: BUSE
Keywords: climate change, risk assessment ,agriculture to public health and national security
Supervisor: NIL

Item sets: Department of Computer Science

Media: Sithole Kundai