Identifying Macro Determinants of Natural Disaster: Applying Machine Learning Approach

Identifying Macro Determinants of Natural Disaster: Applying Machine Learning Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying Macro Determinants of Natural Disaster: Applying Machine Learning Approach M. Jahangir Alam, Pallab Mozumder This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5285803/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzes the macroeconomic determinants of human and economic losses caused by natural disasters using machine learning techniques, particularly the double lasso regression framework. By focusing on floods, storms, and earthquakes, we examine the complex relationships between economic development, geographical characteristics, and the severity of disaster impacts. Our findings indicate a U-shaped relationship between GDP per capita and economic losses, where initial GDP growth reduces losses until a threshold is reached, beyond which further economic development increases the financial damages. Human losses, on the other hand, show an inverse U-shaped relationship with GDP in the case of floods, highlighting that while higher GDP initially leads to more fatalities, continued economic growth eventually reduces mortality rates. Moreover, we find that coastal countries are more vulnerable to both human and economic losses compared to island and landlocked countries, though they demonstrate a greater capacity to leverage GDP growth in reducing economic losses. These results underscore the importance of tailored disaster risk management strategies that consider both economic development and geographic factors to mitigate the adverse impacts of natural disasters. JEL Codes: Q54, O44, C55 Natural Disasters Economic Loss Human Loss Machine Learning Lasso Regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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