Search Results (59)

In airport operations management, accurately estimating the service durations of ground support equipment such as Potable Water Trucks (PWTs) is essential for improving resource allocation efficiency and ensuring timely aircraft turnaround. Traditional estimation methods often use fixed averages or assume normal distributions, failing to capture real-world variability and extreme scenarios effectively. To address these limitations, this study performs a comprehensive statistical analysis of PWT service durations using operational data from Beijing Daxing International Airport (ZBAD) and Shanghai Pudong International Airport (ZSPD). Employing chi-square goodness-of-fit tests, twenty probability distributions—including several heavy-tailed candidates—were rigorously evaluated under segmented scenarios, such as peak versus non-peak periods, varying temperature conditions, and different aircraft sizes. Results reveal that heavy-tailed distributions offer context-dependent advantages: the stable distribution exhibits superior modeling performance during peak operational periods, whereas the Burr distribution excels under non-peak conditions. Interestingly, contrary to existing operational assumptions, service durations at extremely high and low temperatures showed no significant statistical differences, prompting a reconsideration of temperature-dependent planning practices. Additionally, analysis by aircraft category showed that the Burr distribution best described service durations for large aircraft, while stable and log-logistic distributions were optimal for medium-sized aircraft. Numerical simulations confirmed these findings, demonstrating that the proposed heavy-tailed probabilistic models significantly improved resource prediction accuracy, reducing estimation errors by 13% to 25% compared to conventional methods. This research uniquely demonstrates the practical effectiveness of employing context-sensitive heavy-tailed distributions, substantially enhancing resource efficiency and operational reliability in airport ground handling management. Full article

Many plant image-based studies primarily use datasets featuring either a single plant, a plant with only one leaf, or images containing only plants and leaves without any background. However, in real-world scenarios, a substantial portion of acquired images consists of blurred plants or extensive backgrounds rather than high-resolution details of the target plants. In such cases, classification models struggle to identify relevant areas for classification, leading to insufficient information and reduced classification performance. Moreover, the presence of moisture, water droplets, dust, or partially damaged leaves further degrades classification accuracy. To address these challenges and enhance classification performance, this study introduces a plant image segmentation (Pl-ImS) model for segmentation and a plant image classification (Pl-ImC) model for classification. The proposed models were evaluated using a self-collected dataset of 21,760 plant images captured under real field conditions in South Korea, incorporating various environmental factors such as moisture, water droplets, dust, and partial leaf loss. The segmentation method achieved a dice score (DS) of 89.90% and an intersection over union (IoU) of 81.82%, while the classification method attained an F1-score of 95.97%, surpassing state-of-the-art methods. Full article

Continuous use of agricultural chemicals and fertilizers, sporadic sewer overflow events, and an increase in urbanization have led to significant nutrient/pollutant loadings into the semi-arid Arroyo Colorado River basin, which is located in South Texas, U.S. Priority nutrients that require reduction include phosphorus and nitrogen and to mitigate issues of low dissolved oxygen, in some of its river segments. Consequently, the river’s potential to support aquatic life has been significantly reduced, thus highlighting the need for restoration. To achieve this restoration, a watershed protection plan was developed, comprising several preventive mitigation measures, including installing green infrastructure (GI) practices. However, for effective reduction of excessive nutrient loadings, there is a need to study the effects of different combinations of GI practices under current and future land use scenarios to guide decisions in implementing the cost-effective infrastructure while considering factors such as the existing drainage system, topography, land use, and streamflow. Therefore, this study coupled the Soil and Water Assessment Tool (SWAT) model with the System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) model to determine the effects of different combinations of GI practices on the reduction of nitrogen and phosphorus under changing land use conditions in three selected Arroyo Colorado subwatersheds. Two land use maps from the U.S. Geological Survey (USGS) Forecasting Scenarios of land use (FORE-SCE) model for 2050, namely, A1B and B1, were implemented in the coupled SWAT-SUSTAIN model in this study, where the urban area is projected to increase by 6% and 4%, respectively, with respect to the 2018 land use scenario. As expected, runoff, phosphorus, and nitrogen slightly increased with imperviousness. The modeling results showed that implementing either vegetated swales or wet ponds reduces flow and nutrients to meet the Total Maximum Daily Loads (TMDLs) targets, which cost about USD 1.5 million under current land use (2018). Under the 2050 future projected land use changes (A1B scenario), the cost-effective GI practice was implemented in vegetated swales at USD 1.5 million. In contrast, bioretention cells occupied the least land area to achieve the TMDL targets at USD 2 million. Under the B1 scenario of 2050 projected land use, porous pavements were most cost effective at USD 1.5 million to meet the TMDL requirements. This research emphasizes the need for collaboration between stakeholders at the watershed and farm levels to achieve TMDL targets. This study informs decision-makers, city planners, watershed managers, and other stakeholders involved in restoration efforts in the Arroyo Colorado basin. Full article

Groundwater resources are becoming increasingly scarce, especially in arid regions of western Kazakhstan. By 2070, the domestic and drinking water demands will increase from 640 to 901 thousand m³/day. This deficiency may be overcome by utilizing the Zhem Artesian Basin’s Cretaceous Albian–Cenomanian aquifer complex. The hydrodynamic interactions between the 123 known aquifer segments and recharge zones of these promising, exploitable, high-quality groundwater sources are unclear. While MODFLOW is a nominal platform for groundwater flow assessment, which is usually used for the simulation of simple hydrological scenarios, in this study, integrating several different scales and functional modules over a GIS platform enabled delineation and the forecast of this multi-layer aquifer complex. The MODFLOW simulation assessed exploitable groundwater resources and reservoir interactions, enabling the establishment of a simultaneous operation to the Zhem aquifer complex and its neighboring reservoirs. The model suggests that the total exploitable groundwater resources may grow to 629.4 thousand m³/day during the next 50 years. The simultaneous drawdown model suggests a water level decrease of up to 80 m at the end of this period, which will cause a river flow reduction of approximately 6% of the average long-term river flow. Thus, the assessed exploitable groundwater resources will cover more than 70% of the future regional water demand. The mathematical model developed may be used for monitoring and forecasting groundwater head and water balance changes and may be applied to attain a more detailed groundwater resource transfer scheme with economic criteria. Full article

Accurate water body detection is essential for autonomous navigation and operational planning of unmanned surface vehicles (USVs). To address model adaptability to ambiguous boundaries caused by diverse scenarios and climatic conditions, this study proposes GFANet (Global–Local Feature Attention Network) for the real-time water surface semantic segmentation of camera-captured images. First, a Global–Local Feature (GLF) extraction module is proposed, integrating a self-attention-based local feature extractor and a multi-scale global feature extractor for parallel feature learning, thereby enhancing hierarchical feature representation. Second, a Gated Attention (GA) module is designed with a dual-branch gating mechanism to implement noise suppression and efficient low-level feature utilization. The method was validated on three publicly available datasets in relevant domains. The experimental results on the Riwa dataset show that GFANet achieves state-of-the-art segmentation performance (4.41 M parameters, 7.15 GFLOPs) with an mIoU of 82.29% and an mPA of 89.49%. Comparable performance metrics were obtained on the USVInland and WaterSeg datasets. Additionally, GFANet achieves a 154.98 FPS processing speed, meeting real-time segmentation requirements. The experimental results verify that GFANet achieves an optimal balance between high segmentation accuracy and real-time processing efficiency. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

This research proposes an artificial intelligence (AI)-powered digital twin framework for highway slope stability risk monitoring and prediction. For highway slope stability, a digital twin replicates the geological and structural conditions of highway slopes while continuously integrating real-time monitoring data to refine and enhance slope modeling. The framework employs instance segmentation and a random forest model to identify embankments and slopes with high landslide susceptibility scores. Additionally, artificial neural network (ANN) models are trained on historical drilling data to predict 3D subsurface soil type point clouds and groundwater depth maps. The USCS soil classification-based machine learning model achieved an accuracy score of 0.8, calculated by dividing the number of correct soil class predictions by the total number of predictions. The groundwater depth regression model achieved an RMSE of 2.32. These predicted values are integrated as input parameters for seepage and slope stability analyses, ultimately calculating the factor of safety (FoS) under predicted rainfall infiltration scenarios. The proposed methodology automates the identification of embankments and slopes using sub-meter resolution Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) and generates critical soil properties and pore water pressure data for slope stability analysis. This enables the provision of early warnings for potential slope failures, facilitating timely interventions and risk mitigation. Full article

Water quality management in large basins demands tools that balance scientific rigor with computational efficiency to avoid paralysis by analysis. While traditional models offer detailed insights, their complexity and resource intensity hinder timely decision-making. To address this gap, we present AFAR-WQS, an open-source MATLAB™ toolbox that introduces a novel integration of assimilation factors with graph theory and a Depth-First Search (DFS) algorithm to rapidly simulate 13 water quality determinants across complex topological networks. AFAR-WQS resolves cumulative processes in networks of up to 30,000 segments in just 163 s on standard hardware, enabling real-time scenario evaluations. Its object-oriented architecture ensures scalability, allowing customization for urban drainage systems or macro-basin studies while maintaining computational efficiency. Case studies demonstrate its utility in prioritizing sanitation investments, assessing water quality at the national scale and fostering stakeholder collaboration through participatory workshops. By bridging the gap between simplified and complex models, AFAR-WQS supports adaptive management in contexts of hydrological uncertainty, regulatory compliance, and climate change. The toolbox is freely available at GitHub, offering a transformative approach for integrated water resource management. Full article

The encroachment of buildings into the waters of rivers and lakes can lead to increased safety hazards, but current semantic segmentation algorithms have difficulty accurately segmenting buildings in such environments. The specular reflection of the water and boats with similar features to the buildings in the environment can greatly affect the performance of the algorithm. Effectively eliminating their influence on the model and further improving the segmentation accuracy of buildings near water will be of great help to the management of river and lake waters. To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. First, we designed a residual dynamic short-cut down-sampling (RDSC) module to minimize the interference of complex building shapes and building scale differences on the segmentation results; second, we reduced the semantic and resolution gaps between multi-scale features using a multi-channel cross fusion transformer module (MCCT); finally, a double-feature channel-wise fusion attention (DCF) was designed to improve the model’s ability to depict building edge details and to reduce the influence of similar features on the model. Additionally, an HRI Building dataset was constructed, comprising water-edge buildings situated in a riverine and lacustrine regulatory context. This dataset encompasses a plethora of water-edge building sample scenarios, offering a comprehensive representation of the subject matter. The experimental results indicated that the statistical metrics achieved by RDAU-Net using the HRI and WHU Building datasets are better than those of others, and that it can effectively solve the building segmentation problems in the management of river and lake waters. Full article

Infrared image segmentation in marine environments is crucial for enhancing nighttime observations and ensuring maritime safety. While recent advancements in deep learning have significantly improved segmentation accuracy, challenges remain due to nighttime marine scenes including low contrast and noise backgrounds. This paper introduces a cross-granularity infrared image segmentation network CGSegNet designed to address these challenges specifically for infrared images. The proposed method designs a hybrid feature framework with cross-granularity to enhance segmentation performance in complex water surface scenarios. To suppress feature semantic disparity against different feature granularity, we propose an adaptive multi-scale fusion module (AMF) that combines local granularity extraction with global context granularity. Additionally, incorporating a handcrafted histogram of oriented gradients (HOG) features, we designed a novel HOG feature fusion module to improve edge detection accuracy under low-contrast conditions. Comprehensive experiments conducted on the public infrared segmentation dataset demonstrate that our method outperforms state-of-the-art techniques, achieving superior segmentation results compared to professional infrared image segmentation methods. The results highlight the potential of our approach in facilitating accurate infrared image segmentation for nighttime marine observation, with implications for maritime safety and environmental monitoring. Full article

In recent years, remote sensing has experienced a significant transformation due to rapid advancements in deep learning technology, which have greatly outpaced traditional methodologies. This integration has attracted substantial interest within the academic community. To address the complex challenges of extracting data on intricate water bodies during disaster scenarios, this study developed a post-disaster floodwater body dataset and an enhanced multi-scale transformer model architecture. Through end-to-end training, the precision of the model in extracting floodwater contours has been significantly improved. Additionally, by utilizing the vast amounts of unannotated data in remote sensing through an unsupervised pre-training task, the model’s backbone network has been fortified, greatly enhancing its performance in remote sensing applications. Experimental analyses have shown that the multi-scale transformer-based algorithm for floodwater contour extraction proposed in this study is not only widely applicable but also excels in delivering precise segmentation results in complex environments. This refined approach ensures that the model adeptly handles the intricacies of floodwater body delineation, providing a robust solution for accurate extraction, even in disaster-stricken areas. This innovation represents a substantial leap forward in remote sensing, offering valuable insights and tools for disaster management and environmental monitoring. Full article

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis. Full article

The expanding cropland profoundly affects stream water quality. However, the relationships between landscape patterns and stream water quality in different cropland composition classes remain unclear. We observed total nitrogen (TN), total phosphorus (TP) concentrations, and landscape patterns changed in 78 sub-watersheds of the Taihu Lake Basin’s Jiangsu segment from 2005 to 2020. The results showed that cropland area was positively correlated with TN and TP concentrations. The 21.10% reduction in cropland area, coupled with a 41.00% increase in building land, has led to an escalation in cropland fragmentation. Meanwhile, TN and TP concentrations declined by 26.67% and 28.57%, respectively. Partial least squares suggested that forest interspersion and juxtaposition metrics and forest area percentage were dominant factors influencing water quality in high- and medium-density cropland zones, respectively. The Cellular Automata–Markov Model shows reasonable distribution of forests. Scenarios with enhanced forest interspersion and juxtaposition metrics (75.28 to 91.12) showed reductions in TP (26.92% to 34.61%) and TN (18.45% to 25.89%) concentrations by 2025 compared to a natural economic development scenario. Landscape configuration optimization could assist managers in improving water quality. Full article

Contamination poses a significant risk to public health by degrading water quality in water distribution systems (WDSs). As one of the key tasks of a response strategy to contamination incidents in a WDS, pipe system flushing has been widely implemented in practice. However, due to the complexity of the network structure and chemical reaction within the pipe system, determining the flushing duration is still one of the significant challenges for a given network. To address this problem, a model for determining the flushing duration is developed. This model is based on calculating the traveling trajectory of the contaminant inside the network. This is carried out by discretizing the one-dimension advection equation and calculating the variation of the contaminant concentration from one segment to another over time. As a preliminary study, we focus on simplified scenarios where contaminants exhibit no chemical reaction within the WDS. The proposed model is applied and analyzed through a simulation study and a laboratory testbed. The results demonstrate the efficacy of the model for determining flushing duration, which can offer valuable insights for real-world applications and serve as a crucial reference for water utility companies. Full article

The Yangtze River’s substantial variation in water depth and current speeds means that inland ships face diverse operational conditions within a single voyage. This paper discusses the adoption of controllable-pitch propellers, which adjust their pitch to adapt to varying navigational environments, thereby optimizing energy efficiency. We developed an optimization framework to determine the ideal pitch angle and rotation speed (RPM) under different sailing conditions. The energy performance model for inland ships was enhanced to account for the open-water efficiency of CPPs across various pitch angles and RPMs, considering the impacts of current and shallow water, among other factors. The optimization approach was refined by incorporating an improved genetic algorithm with an annealing algorithm to enhance the initial population, applying the K-means clustering algorithm for population segmentation, and using multi-parent crossover from diverse clusters. The efficacy of the optimization method for CPP operations was validated by analyzing three operational scenarios of a Yangtze sailing ship. Additionally, key components of the ship performance model were calibrated through experimental tests, demonstrating an anticipated fuel consumption reduction of approximately 5% compared to conventional fixed-pitch propellers. Full article