Non-stationary drought patterns in hyper-arid regions: Spatiotemporal and multi-timescale drought analysis.

Droughts rank among the most devastating natural disasters, particularly in arid regions such as Oman. However, traditional drought assessment based on stationarity may not be applicable under climate change. Moreover, most previous studies have been point-based, relying on station observations without capturing the spatial variability of drought.

Enhancing water security through integrated decision-making and selective withdrawal for sustainable reservoir management.

Effective management of water quantity and quality in reservoir systems is vital for strengthening regional water security. Selective Withdrawal Systems (SWSs) contribute to this goal by allowing the precise extraction of water from specific layers in stratified reservoirs, where water quality and other properties differ across depths. Climate change and management policies further influence the hydrodynamics of SWSs, significantly impacting reservoir water quantity and quality.

Simulation of anaerobic digestion process under variable feeding sludge using a hybrid machine learning model.

Anaerobic digestion (AD) is a key method for stabilizing sludge in wastewater treatment plants. With advancements in artificial intelligence, this study investigates a hybrid approach to simulate the performance of AD systems using data from a laboratory-scale setup. Five machine learning (ML) techniques-decision tree (DT), random forest (RF), gradient boosting (GB), support vector regression (SVR), and multilayer perceptron (MLP)-were employed to estimate biogas production, the volatile solids removal, and the total solids output of the digester based on varying input sludge conditions.

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.

BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios.

Traditional downscaling techniques often fail to accurately represent critical extremes necessary for effective adaptation planning. This paper introduces the first application of Bidirectional Long Short-Term Memory (BiLSTM) networks with an adaptive Kalman filter for multi-scenario, high-resolution precipitation downscaling. We applied our methodology to Tehran, Iran, and systematically compared and ranked the performance of different CMIP6 projections, with the best performing model being MIROC (NSE: 0.

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.

Future projections of thermal and chemical stratifications in reservoir under the impact of climate change.

Vertical stratification is a fundamental characteristic of water bodies that significantly affects vertical convection and mixing dynamics. With the impact of climate change, thermal and chemical stratification in lakes and reservoirs has been exacerbated, leading to more pronounced environmental and ecological challenges. While previous studies have identified the impact of climate change on reservoir stratification, they have primarily focused on temperature variations in future periods.

Assimilation of satellite-derived profile observations for enhanced estimation of water quality variables in a deep reservoir.

Accurate and reliable estimation of the temperature and Dissolved Oxygen (DO) profiles in deep reservoirs is crucial for effective water quality management. Integrating incoming observations through a data assimilation scheme can ensure the precision and certainty of the predictions. As surface water quality variables cannot reflect the overall conditions of deep reservoirs, assimilating vertical profile observations is essential.

Improving index-based coastal vulnerability assessment using machine learning in Oman.

Coastal vulnerability assessments are crucial for evaluating the potential impacts of environmental hazards. Traditional methods that typically rely on index-based approaches are often limited by their inability to account for the relative importance of individual parameters. This study integrated machine learning models (Random Forest and XGBoost), which were optimized through Particle Swarm Optimization, with an index-based method to determine the weights of vulnerability parameters using feature importance analysis.

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.

Assessing uncertainties in modeling the risk of geogenic groundwater contamination.

Assessing groundwater contamination risk is a critical aspect of preventing and managing groundwater pollution. There was a research gap in the investigation of uncertainties in modeling groundwater contamination risks in aquifers. This study addresses this gap using Bayesian Model Averaging (BMA), with a novel focus on risk exposures from geogenic contaminants, such as lead (Pb).

A copula-based multivariate flood frequency analysis under climate change effects.

Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but climate change is expected to influence flood frequency analysis and flood system design in the future.

Backward induction-based multi-layer approach for watershed flood management in arid regions.

Managing floods in interconnected nonurban and urban areas of arid regions prone to flash floods requires a more dynamic and integrated approach than traditional linear methods. In this regard, this study proposes a novel multi-stage decision-making framework which reshapes the traditional cascade approach to flood management by addressing the interdependencies between upstream and downstream regions. The proposed cyclic decision-making process involves five main steps: First, the Hydraulic and Hydrological (H/H) conditions in urban and nonurban areas were modeled using the SWMM.

Estimating the vertical profile of water quality variables in reservoirs: Application of remotely sensed data and machine learning techniques.

Water quality assessment and management of reservoirs depend on accurate, large-scale, and continuous monitoring of the vertical profile of Water Quality Variables (WQVs). Remote sensing data have been widely used to retrieve high spatiotemporal water quality data; however, their application has practically been limited to evaluating surface WQVs. In this paper, a novel and efficient approach is introduced for assessing the profile of WQVs in reservoirs that depend on stratification, by taking into account the shape of profile as prior knowledge.

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.

Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach.

Monitoring of groundwater (GW) resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization of water quality index (WQI) models has proven effective in monitoring GW resources, it has faced substantial criticism due to its inconsistent outcomes, prompting the need for more reliable assessment methods. Therefore, this study addressed this concern by employing the data-driven root mean squared (RMS) models to evaluate groundwater quality (GWQ) in the coastal Bhola district near the Bay of Bengal, Bangladesh.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy.

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area.

Long-term resilience in wastewater management: Optimizing treated wastewater allocation with a dynamic multi-agent approach.

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space.

Performance evaluation of a new sponge-based moving bed biofilm reactor for the removal of pharmaceutical pollutants from real wastewater.

Pharmaceutical pollutants, a group of emerging contaminants, have attracted outstanding attention in recent years, and their removal from aquatic environments has been addressed. In the current study, a new sponge-based moving bed biofilm reactor (MBBR) was developed to remove chemical oxygen demand (COD) and the pharmaceutical compound Ibuprofen (IBU). A 30-L pilot scale MBBR was constructed, which was continuously fed from the effluent of the first clarifier of the Southern Tehran wastewater treatment plant.

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.