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.

Climate-driven projections of cyanobacterial harmful algal bloom expansion in coastal waters.

Cyanobacterial harmful algal blooms (CyanoHABs) in coastal waters are a growing ecological and environmental concern, especially in climate-vulnerable regions. While many studies have explored historical variations and short-term forecasting of CyanoHABs, this study extends projections into the coming decades, focusing on Oman's vulnerable coastal areas under future climate change scenarios. By integrating General Circulation Models (GCMs) outputs with machine learning and deep learning models, this research aims to enhance predictive accuracy and assess long-term CyanoHAB impacts.

A comprehensive review of geotechnical implications of floods and water-driven disasters.

Past experiences with water-related natural disasters, including floods, and their adverse consequences, such as significant loss of life and economic burdens, underscore the critical need to identify and understand geotechnical phenomena that trigger failures and amplify detrimental effects. This review synthesizes and examines key factors influencing geotechnical issues arising from water-based extreme events, such as hurricanes and floods. These factors comprise seepage forces, shear- and liquefaction-induced scour, excessive pore water pressure, soil stratigraphy, and hydraulic boundary conditions (e.

Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman.

Accurate precipitation predictions are crucial for addressing climate change impacts on water resources, especially in arid regions like Oman. Therefore, this study evaluates three machine learning models-Random Forest (RF), Multilayer Perceptron Neural Networks (MLP-ANN), and Kolmogorov-Arnold Neural Networks (KANNs)-to downscale and predict precipitation patterns under climate scenarios SSP1-2.6, SSP2-4.

Hybrid deep learning downscaling of GCMs for climate impact assessment and future projections in Oman.

Accurate downscaling of global circulation models (GCMs) is critical for assessing the impacts of climate change and water resources management. In this research, Fourteen GCMs were evaluated through a Taylor diagram, including EC-Earth3-CC, ACCESS-CM2, AWI-ESM-1-1-LR, BCC-ESM1, CanESM5, IITM-ESM, MPI ESM1-2HR, INM-CM5-0, IPSL-CM5A2-INCA, KIOST-ESM, NorCPM1, NorESM2-MM, TaiESM1, and ACCESS-ESM1-5. IITM-ESM showed the best performance, making it the preferred model for future climate studies.

Near future flash flood prediction in an arid region under climate change.

Flash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and infrastructure, which in turn results in the loss of lives and significant economic damages. In this study, a comprehensive statistical approach was applied to future flood predictions in the coastal basin of North Al-Abatinah, Oman. In this context, the initial step involves analyzing eighteen General Circulation Models (GCMs) to identify the most suitable one.

Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques.

Urban flood risks have intensified due to climate change and dense infrastructural development, necessitating innovative assessment approaches. This study aimed to integrate advanced hydrodynamic models with machine learning (ML) techniques to improve urban flood prediction and hazard analysis. Integrating 1D and 2D hydrodynamic models calibrated with precise parameters demonstrated exceptional predictive accuracy for flood dynamics.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir.

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.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir.

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.

The impact of stay-at-home orders on air-quality and COVID-19 mortality rate in the United States.

Since the beginning of the pandemic in the U.S., most jurisdictions issued mitigation strategies, such as restricting businesses and population movements.

Understanding occupational heat exposure in the United States and proposing a quantifying stress index.

Purpose: Millions of workers exposed to the outdoor environment are extremely susceptible to extreme heat. Although several articles analyzed heat-related illnesses, injuries, fatalities at the country level, few investigated regional and state statistics especially for OSHA Region 4 and the state of Alabama, U.S, which we explored in this study.

Development of a conceptual framework for risk assessment of elevated internal temperatures in landfills.

Subsurface elevated temperatures (SETs) often occur in landfills and pose great threats to their structural and environmental integrity. Current landfill gas monitoring practices only recommend maintaining certain soil gases percentages, with no integrated strategy for predicting subsurface temperature. As a solution, this paper proposes a comprehensive risk assessment framework specific to SET mitigation.