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.

Numerical analysis of surface hydrogeological water budget to estimate unconfined aquifers recharge.

Under changing climate, groundwater resources are the main drivers of socioeconomic development and ecosystem sustainability. This study assessed the contribution of two adjacent watersheds, Muse Street (MS) and West Wood (WW), with low and high urban development, to the Memphis aquifer recharge process in central Jackson, Tennessee, USA. The numerical MODFLOW model was created using data from 2017 to 2019 and calibrated using reported water budget components derived from in-situ data.

Assessing the effects of increased impervious surface on the aquifer recharge through river flow network, case study of Jackson, Tennessee, USA.

Understanding pathways connecting urbanization to the recharge process across the land surface and river environment is of great significance in achieving low-impact development. Accordingly, the contribution of an urbanized region with a low and high development rate, along with the expected overflow into the river network resulting from increased impervious surfaces, was assessed in the recharge rate at Jackson, Tennessee. To this end, first, the losses were calculated using the standard and modified SCS-CN methods for the maximum probable flood condition.