Improving disaster resilience with causal machine learning for flood damage estimation.

Machine Learning (ML) models have become a pivotal tool in the scientific community, successfully addressing complex problems across various domains, including flood risk management. Despite these advancements, traditional data-driven models often struggle when training data is scarce and primarily rely on correlation rather than causal relationships, making them vulnerable to shifts in data distribution. This paper introduces a Causally Informed Neural Network (CINN) framework that integrates causal prior knowledge as an inductive bias to improve flood damage predictions for residential properties and address these limitations.

Environmental exposure and respiratory health: Unraveling the impact of toxic release inventory facilities on COPD prevalence.

This cross-sectional geospatial analysis explores the prevalence of Chronic Obstructive Pulmonary Disease (COPD) concerning the proximity to toxic release inventory (TRI) facilities in Jefferson County, Alabama. Employing the fuzzy analytical hierarchy process (FAHP), the study evaluates COPD prevalence, comorbidities, healthcare access, and individual health assessments. Given the mounting evidence linking environmental pollutants to COPD exacerbations, the research probes the influence of TRI sites on respiratory health, integrating Geographic Information Systems (GIS) to scrutinize the geospatial vulnerability of communities neighboring TRI sites.

Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach.

Flooding is a global threat and predicting flood risk accurately is vital for effective mitigation and increasing society's awareness of the negative impacts of floods. Over the years, researchers have worked on physical and data-driven models to predict flood damage, striving to improve accuracy and understanding. However, the challenge lies in the scarcity and limitedness of comprehensive datasets needed to develop these models.