Date of Award
Santa Clara : Santa Clara University, 2023.
Computer Science and Engineering
The increasing frequency and intensity of drastic climate change events have underscored the urgency for a comprehensive understanding of climate dynamics and the development of effective mitigation and adaptation strategies. In this context, our thesis addresses the critical issue of drought category prediction in Western America using machine learning techniques. We recognize the interconnectedness of efforts aimed at combating climate change and strive to make a meaningful contribution to the broader understanding of climate change, even if our impact may seem comparatively smaller. By leveraging the power of machine learning algorithms and utilizing a wide range of climatic and environmental variables, we aim to develop a robust predictive model that can accurately classify regions into distinct drought categories. The model’s predictions have significant implications for water resource management, agriculture, ecosystems, and human livelihoods, enabling informed decision-making and proactive measures to mitigate the adverse consequences of droughts.
Through a review of existing methods, we highlight the need for advanced machine learning techniques in drought prediction. Our thesis focuses on the development of a machine learning model that utilizes climate variables, soil moisture levels, vegetation indices, and other relevant spatial and temporal features to predict drought categories at specific coordinates in Western America. By harnessing the vast amount of available data and leveraging the capabilities of machine learning algorithms, we aim to overcome the limitations of existing approaches and provide accurate and timely predictions. The outcomes of this research have the potential to inform policy decisions, enhance resource allocation, and contribute to effective drought mitigation strategies in Western America, ultimately fostering resilience in the face of a changing climate.
Khemkhon, Atiyut and Lin, Dylan, "Climate Cloud" (2023). Computer Science and Engineering Senior Theses. 250.