Scour is a primary cause of bridge failures worldwide, posing significant risks to infrastructure stability and safety. Understanding the mechanisms of scouring is crucial for developing effective mitigation strategies. This study employs FLOW-3D software to create a detailed three-dimensional model of local scour around a bridge pier, providing an advanced simulation framework. The model is meticulously calibrated using experimental data obtained from tests conducted on a circular pier, ensuring high accuracy and reliability in the simulation results.

The primary objective of this research is to assess the efficacy of circular collars as countermeasures in reducing scour depth around bridge piers. Circular collars are designed to deflect the flow of water and disrupt the vortices that contribute to sediment erosion at the pier base. By incorporating these countermeasures into the model, we aim to quantify their impact on reducing local scour.

The simulation results reveal that the addition of circular collars significantly diminishes local scour around the pier. The collars effectively alter the flow patterns, reducing the intensity of vortices and the resulting sediment displacement. This study provides valuable insights into the practical application of FLOW-3D in hydraulic engineering and underscores the potential of circular collars as a cost-effective solution for mitigating scour-related bridge failures.

The accurate prediction of scour development at bridge foundations is essential in order to ensure the safety and integrity of engineering structures. The complexity of the scouring process arises from the separation and generation of multiple vortices and the dynamic interaction between the flow and the movable bed during scour hole development. Traditional experimental approaches to scour characterization have primarily focused on the evolution of the movable bed, often with less emphasis on detailed flow field characterization. This study addresses this gap using FLOW-3D software to develop a computational model that simulates the turbulent flow field around bridge pier models in a controlled environment.

The primary objective is to compare the simulation results with experimental data to improve the understanding of the mechanisms driving flow-induced scour. The study involves velocity measurements of the flow around a bridge pier model carried out in a large-scale tilting flume. Detailed measurements of stream-wise, cross-wise, and vertical velocity distributions and Reynolds shear stresses were taken during both the flat and eroded bed stages of scouring.

Preliminary results from the computational model show a strong correlation with the experimental data, capturing the complex flow patterns and vortex formations that contribute to scour. The model successfully reproduces the observed velocity distributions and Reynolds shear stresses, providing valuable insights into the dynamic interactions between the flow and the bed material. These findings demonstrate the potential of advanced computational modelling to complement experimental studies and provide a more comprehensive understanding of scour processes at bridge foundations. This research contributes to the development of more accurate prediction tools, ultimately enhancing the design and safety of bridge structures.

Predicting crop yield, which plays a pivotal role in crop market planning, insurance strategies, and efficient harvest management, presents a significant challenge attributable to the intricate interplay between various environmental and agricultural management factors. This study capitalizes on recent advancements in satellite technology and machine learning techniques to construct a robust prediction model tailored explicitly for the agriculture-intensive Hindon basin located in India. The method used in this study capitalises on the strengths of the Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) models, renowned for their expertise in capturing intricate spatial features and uncovering a variety of phenological traits crucial for accurate crop yield prediction. The model development phase involved training on a diverse set of variables encompassing crop growth indicators, environmental parameters such as MODIS Land Surface Temperature (LST) data and MODIS Surface Reflectance (SR) data, and historical crop yield records sourced from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). An analysis of the outcomes derived from the model indicates that the integrated CNN-LSTM framework exhibits superior performance compared to utilizing either the CNN or LSTM models in isolation. This advanced method holds great promise in enhancing the accuracy of crop yield forecasts, thereby empowering farmers to make informed decisions about the selection and optimal timing for growing specific crops.

The UNESCO Global Geopark Las Loras (Palencia-Burgos, 960 km², Spain) constitutes a significantly sensitive area to changes caused by the impact of climate change, as it is located at the transition between the Atlantic and Mediterranean sides of the biogeographical regions in Spain and is a notably depopulated area.

The analysis of the impact of climate change on groundwater resources has been carried out by applying global climate models to the precipitation and temperature data series available at the Aguilar de Campoo weather station. The obtained projections for future potential climate scenarios (time intervals 2021-2040 and 2051-2070) in Las Loras UGGp at a monthly scale have been based on AEMET Free and open data, Representative Concentration Pathway (RCP), and Global Circulation Models (GCM).

The prevalence of karstic aquifers identified in the Las Loras UGGp renders this area particularly vulnerable to potential declines in water resources. The conservation of springs is an essential indicator of the efficiency in groundwater management. Using the springs as an indicator of water management, ML tools have been applied to estimate the number of active springs versus inactive springs as a first theoretical approach, which is expected to be checked in the near future using field campaigns and other complementary methodologies. The number of known springs amounts to more than 200 according to information obtained from IGN maps. However, the number of active springs compared to those that have disappeared is not well known. This work presents the results obtained as a first approach. The correlation level is higher than 90%, and this methodology could be extrapolated to other areas.

Coaggregation is a specific interaction mechanism where genetically distinct bacteria recognize and adhere to each other via complementary protein adhesins and polysaccharide receptors. Despite its critical role in biofilm formation and extensive studies in oral environments, coaggregation remains poorly understood in aquatic systems. Understanding coaggregation is crucial due to its significant implications, including its role in multispecies biofilm formation, water quality effects, impacts on engineered systems' performance, and potential biotechnological applications. This study offers an in-depth characterization of the cell surface properties of Delftia acidovorans isolated from drinking water (DW). Two strains showing different coaggregation abilities were selected (005P—coaggregating and 009P—non-coaggregating). The coaggregating 005P strain demonstrated higher surface hydrophobicity and more negative surface charge compared to the non-coaggregating 009P strain. Additionally, 005P showed higher cell surface and co-adhesion energies. Chemical analysis using Fourier-transform infrared spectroscopy revealed subtle differences in the bacterial surfaces, particularly in spectral regions associated with carbohydrates and proteins (860-930 cm-1 and 1212-1240 cm-1). Cryo-electron tomography highlighted distinct differences in pili structures between the strains. The pili in 005P were identified as pili-like adhesins. This research represents the first comprehensive characterization of a coaggregating strain from DW, employing a combination of advanced analytical techniques to provide new insights into the mechanisms driving bacterial coaggregation in aquatic systems.

Emerging pollutants such as pharmaceuticals, industrial chemicals, heavy metals, and microplastics are a growing ecological risk affecting water and soil resources. Another challenge in current wastewater treatments includes tracking and treating these pollutants, which can be costly. As a growing concern, emerging pollutants do not contain lower limit levels and can be detrimental to aquatic resources in minuscule amounts. Thus, the assessment of multiple emerging water pollutants in community-based water sources such as surface water and groundwater is a prioritized area of study for water resource management. It provides a basis for ecological health management of arising diseases such as cancer and dengue caused by unsafe water sources. Accordingly, by utilizing artificial intelligence, wide-range and data-driven insights can be synthesized to assist water resource management and propose solution pathways without the need for exhaustive experimentation. This systematic review examines artificial intelligence-assisted modelling water resource management for emerging water pollutants, notably machine learning and deep learning models, with proximity dependence and correlated synergistic health effects to both humans and aquatic life. This study underscores the increasing accumulation of these emerging pollutants and their toxicological effects on the community, and how data-driven modelling can be utilized to assist in research gaps related to water treatment methods for these pollutants.

Buoyant jets are employed to discharge saline or hot wastewater into the ambient environment by various industrial plants which cloud pose a threat to marine environments and coastal ecosystems. Therefore, understanding the behavior of these jets is crucial for managing their adverse environmental impacts. To characterize the behavior of a confined buoyant jet, a three-dimensional model was employed to solve the transport of mass and momentum using the Computational Fluid Dynamics (CFD) tool OpenFOAM. Various flow conditions were defined by changing the Reynolds number (Re) and densiometric Froude number (Fr) to examine the flow characteristics of vertical buoyant jets subjected to lateral confinement.

The generated datasets were then utilized to train and test different machine-learning algorithms. The machine learning models were developed to predict the flow characteristics based on flow key parameters, including the geometrical parameters. Various machine learning algorithms, including support vector machines (SVM), Extreme Learning Machine (ELM), and Adaptive Neuro-Fuzzy Inference System (ANFIS), were trained and tested using these datasets. The performance of these models was assessed using multiple statistical metrics, and the results were benchmarked against those obtained from a multigene genetic-programming (MGGP) model and an existing regression-based empirical equation.

This study demonstrates the potential of machine learning algorithms to accurately predict the behavior of laterally confined vertical buoyant jets. The findings suggest that machine learning can serve as a reliable and efficient tool in environmental engineering and impact assessments, providing a fast and viable alternative to traditional CFD methods. These models offer a powerful solution for accurately predicting initial dilution properties, supporting the design and assessment of environmental engineering systems.

This research focused on conducting a crop-type study in Tehran Province, Iran, with a particular emphasis on wheat and barley, essential global agricultural products. Accurate mapping of these crops using remote sensing technologies is crucial for efficient agricultural management and planning. This study covered extensive areas within Tehran Province, including Rey, Varamin, Pakdasht, Pishva, and Qarchak.

A crop type map was created for wheat and barley crops, along with other agricultural products and non-agricultural areas, based on their phenological behavior using the agricultural calendar. Satellite images from Sentinel-1 and Sentinel-2 were used at key stages of crop growth, and features like NDVI, EVI, and VV/VH ratio were extracted to identify plant phenological trends. This study utilized the Google Earth Engine for efficient processing due to the large study area and volume of images.

Different scenarios were tested using a Random Forest classification algorithm with limited training data, resulting in the creation of a crop map. Scenario one, including various spectral bands and indices, achieved an accuracy of 84%, a Kappa coefficient of 69%, and an F1-score of 76%. Scenario two, focusing on spectral indices and the VV/VH ratio, obtained an accuracy of 87%, a Kappa coefficient of 62%, and an F1-score of 64%. The highest accuracy of 94%, Kappa coefficient of 87%, and F1-score of 88% were attained in scenario three by utilizing multispectral bands and VV/VH bands. Scenario four, using only spectral indices, achieved an accuracy of 73%. The superior performance of scenario three was credited to its comprehensive spectral and temporal information, demonstrating the effectiveness of remote sensing in large-scale agricultural mapping.

This research demonstrates the practicality and utility of using remote sensing for agricultural mapping in large areas. The methodologies and results of this research can significantly contribute to efficient monitoring and management of agricultural resources.

Quantifying chlorophyll-a (Chl-a) concentrations is paramount in wetland ecosystems as a key indicator of phytoplankton biomass and overall water quality. In this study, we applied three distinct models—Gilerson, Gulin, and Mishra—to derive the Chl-a concentrations from Sentinel-3 satellite data in the Anzali wetland, Iran, in 2023. The Anzali wetland, located on the southwestern coast of the Caspian Sea in Gilan Province, is one of northern Iran's most significant and biodiverse wetlands.

The Gilerson and Gulin models were based on band ratios, specifically the red-edge band ratio. In contrast, the Mishra model utilized an empirical model based on the Normalized Difference Chlorophyll Index (NDCI). These models initially generated Chl-a maps with a lower spatial resolution, which was subsequently enhanced to a 20-meter spatial resolution using features extracted from Sentinel-2 data. Machine learning played a crucial role in this enhancement process, where a Random Forest classifier was trained with the extracted features to refine the Chl-a maps from the Sentinel-3 data. This approach improved the spatial resolution of the chlorophyll concentration estimations across the Anzali wetland.

Sentinel-3 data were resampled to 20 meters for accuracy assessment and utilized as ground-truth data. Field data were collected using in situ measurements of the Chl-a concentrations, ensuring robust ground-truthing. Evaluation of the accuracy metrics revealed the following outcomes for the Gilerson, Gulin, and Mishra models, respectively: RMSE values of 3.71, 10.12, and 11.63; bias values of 0.65, 1.46, and 2.64; and MAE values of 2.85, 7.74, and 8.83. These results indicate that the Gilerson model had the highest accuracy, followed by the Gulin and Mishra models. The synergistic fusion of the Sentinel-2 (S2) and Sentinel-3 (S3) data enhanced the spatiotemporal resolution, providing valuable insights into the Chl-a dynamics at varying scales, thus aiding in refining management strategies and preserving wetland ecosystems.

Floods are the most frequent and destructive disasters, causing widespread destruction, A significant loss of life, and substantial economic impacts globally. Climate change exacerbates urban flood risks by altering precipitation patterns, increasing the frequency of extreme weather events, and raising sea levels. These escalating risks necessitate innovative solutions for effective urban flood management. Digital Twin (DT) technology offers a promising solution by providing virtual models and data-driven simulations that enhance urban landscape management, enabling timely flood warnings, evacuation planning, and property protection. Integrating Generative Artificial Intelligence (GenAI) further elevates digital twins by enhancing predictive capabilities and creating more realistic and interactive scenarios. Utilizing generative algorithms, digital twins can generate synthetic data to simulate a wide range of potential outcomes, improving the accuracy of modeling complex systems and forecasting variations and challenges. Moreover, GenAI improves the precision and reliability of digital twins in representing real-world environments through high-fidelity simulations. This study synthesizes findings from the literature review to develop a conceptual framework for elucidating the synergies between generative AI and digital twins in the context of urban flood risk assessment. It begins with an introduction to the integration of generative AI and digital twins for simulating flood scenarios. The paper then delves into the fundamentals of generative AI, discussing its principles, applications, and successful implementations across various domains, particularly in urban flood risk assessment. Subsequent sections examine the evolution of digital twins and their critical role in assessing, predicting, and mitigating flood risks. The study further investigates the intersection of generative AI and digital twins, highlighting the enhanced simulation capabilities provided by GenAI. Concluding with an in-depth analysis of specific applications of GenAI-enhanced digital twins in flood risk assessment, the study anticipates future challenges and advancements, emphasizing emerging trends and potential development opportunities.