El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where temperature and precipitation are influenced across the globe, causing droughts and floods simultaneously. In this dissertation, we first derived a drought vulnerability index for the Nile basin, identifying regions with high and low drought risk under ENSO conditions. Next, we evaluated the coherence and periodicity of the ENSO signal to detect its implications on MENA Region using earth observations, machine learning, and advanced signal processing techniques. Moreover, we examined ecological and environmental crises created by global warming and unusual weather patterns caused by El Nino and marine heatwaves in nesting sea turtle habitats. Finally, expanding this study on ENSO yielded novel ways to analyze and understand the underlying processes driving unprecedented global heat waves and their association with ENSO.
","abstract_html":"<p>El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where temperature and precipitation are influenced across the globe, causing droughts and floods simultaneously. In this dissertation, we first derived a drought vulnerability index for the Nile basin, identifying regions with high and low drought risk under ENSO conditions. Next, we evaluated the coherence and periodicity of the ENSO signal to detect its implications on MENA Region using earth observations, machine learning, and advanced signal processing techniques. Moreover, we examined ecological and environmental crises created by global warming and unusual weather patterns caused by El Nino and marine heatwaves in nesting sea turtle habitats. Finally, expanding this study on ENSO yielded novel ways to analyze and understand the underlying processes driving unprecedented global heat waves and their association with ENSO.</p>","abstract_has_math":false,"creators":["Perera, Sachi"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Hesham El-Askary","Mohamed Allali","Cyril Rakovski","Erik Linstead","Joshua B. Fisher"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-01T07:00:00Z","date_published":"2024-05-01T07:00:00Z","updated_at":"2026-07-24T01:38:37Z","subjects":["signal","Data Science","Environmental Health and Protection","Hydrology","Longitudinal Data Analysis and Time Series","Natural Resources and Conservation","Other Earth Sciences","Signal Processing"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/43","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hesham El-Askary","Mohamed Allali","Cyril Rakovski","Erik Linstead","Joshua B. Fisher"]},{"key":"dc:creator","label":"Author","values":["Perera, Sachi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-08-31T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["signal","Data Science","Environmental Health and Protection","Hydrology","Longitudinal Data Analysis and Time Series","Natural Resources and Conservation","Other Earth Sciences","Signal Processing"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/43"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where temperature and precipitation are influenced across the globe, causing droughts and floods simultaneously. In this dissertation, we first derived a drought vulnerability index for the Nile basin, identifying regions with high and low drought risk under ENSO conditions. Next, we evaluated the coherence and periodicity of the ENSO signal to detect its implications on MENA Region using earth observations, machine learning, and advanced signal processing techniques. Moreover, we examined ecological and environmental crises created by global warming and unusual weather patterns caused by El Nino and marine heatwaves in nesting sea turtle habitats. Finally, expanding this study on ENSO yielded novel ways to analyze and understand the underlying processes driving unprecedented global heat waves and their association with ENSO.
"]},{"key":"dc:source","label":"Dc Source","values":["S. Perera, \"Machine learning and geostatistical approaches for discovery of weather and climate events related to El Niño phenomena,\" Ph.D. dissertation, Chapman University, Orange, CA, 2024. https://doi.org/10.36837/chapman.000539"]},{"key":"dc:title","label":"Title","values":["Machine Learning and Geostatistical Approaches for Discovery of Weather and Climate Events Related to El Niño Phenomena"]}]}],"canonical_facts":{"dc:contributor":["Hesham El-Askary","Mohamed Allali","Cyril Rakovski","Erik Linstead","Joshua B. Fisher"],"dc:creator":["Perera, Sachi"],"dc:date.available":["2024-08-31T07:00:00Z"],"dc:description.abstract":["El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where temperature and precipitation are influenced across the globe, causing droughts and floods simultaneously. In this dissertation, we first derived a drought vulnerability index for the Nile basin, identifying regions with high and low drought risk under ENSO conditions. Next, we evaluated the coherence and periodicity of the ENSO signal to detect its implications on MENA Region using earth observations, machine learning, and advanced signal processing techniques. Moreover, we examined ecological and environmental crises created by global warming and unusual weather patterns caused by El Nino and marine heatwaves in nesting sea turtle habitats. Finally, expanding this study on ENSO yielded novel ways to analyze and understand the underlying processes driving unprecedented global heat waves and their association with ENSO.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/43"],"dc:source":["S. Perera, \"Machine learning and geostatistical approaches for discovery of weather and climate events related to El Niño phenomena,\" Ph.D. dissertation, Chapman University, Orange, CA, 2024. https://doi.org/10.36837/chapman.000539"],"dc:subject":["signal","Data Science","Environmental Health and Protection","Hydrology","Longitudinal Data Analysis and Time Series","Natural Resources and Conservation","Other Earth Sciences","Signal Processing"],"dc:title":["Machine Learning and Geostatistical Approaches for Discovery of Weather and Climate Events Related to El Niño Phenomena"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:37Z"}