Three Essays on Managing Extreme Weather Events and Climatic Shocks in Developing and Developed Countries

Climate change and extreme weather events are affecting the environment, and people’s livelihood in both developing and developed countries. Agriculture, forestry, fishing, livestock, water resources, human health, terrestrial ecosystems, biodiversity, and coastal zones are among the major sectors impacted by these shocks. The challenge of adaptation is particularly acute in the developing countries, as poverty and resource constraints limit their capacity to act. Bangladesh fits in this category, and thus I use data from Bangladesh to analyze the adaptation process in the first and second chapter of my dissertation. In the first chapter, I investigate whether transient shocks (flood, cyclone) or permanent shocks (e.g., river erosion that leads to permanent loss of lands) have more influence on interregional migration. Findings of the study suggest that the households prefer to move to the nearest city when the environmental shock is temporary, whereas they tend to relocate over a greater distance when the environmental shock is more permanent in nature. In the second chapter, I investigate the feasibility of a set of adaptation measures to cope with hydro-climatic shocks (e.g. floods, drought, cyclones, tidal waves) and epidemic shocks (emergence or re-emergence of infectious diseases on livestock and poultry) in the agricultural sector in Bangladesh. Findings suggest that a decrease in agricultural income due to climatic and/or epidemic shocks is likely to induce households to adapt more. Developed countries are also vulnerable to extreme weather events and climatic shocks. In 2017, United States was hit by three consecutive hurricanes: Harvey, Irma, and Maria. Given the rising exposure and the increasing need to manage coastal vulnerability, the third essay focusses on understanding household preferences for financing adaptation activities in the U. S. and analyzes which mechanism, i.e., state or federal adaptation fund approach, is better suited to managing exposure to such types of natural disaster in the future

Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection

Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection