Routing Model Research Articles

Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

Flood prediction systems need hierarchical atmospheric, hydrologic, and hydraulic models to predict rainfall, runoff, streamflow, and floodplain inundation. The accuracy of such systems depends on the error propagation through the modeling chain, sensitivity to input data, and choice of models. In this study, we used multiple precipitation forcings (hindcast and forecast) to drive hydrologic and hydrodynamic models to analyze the impacts of various drivers on the estimates of flood inundation depth and extent. We implement this framework to unravel the August 2021 extreme flooding event that occurred in Central Tennessee, USA. We used two radar-based quantitative precipitation estimates (STAGE4 and MRMS) as well as quantitative precipitation forecasts (QPF) from the National Weather Service Weather Prediction Center (WPC) to drive a series of models in the hierarchical framework, including the Variable Infiltration Capacity (VIC) land surface model, the Routing Application for Parallel Computation of Discharge (RAPID) river routing model, and the AutoRoute and TRITON inundation models. An evaluation with observed high-water marks demonstrates that the framework can reasonably simulate flood inundation. Despite the complex error propagation mechanism of the modeling chain, we show that inundation estimates are most sensitive to rainfall estimates. Most notably, QPF significantly underestimates flood magnitudes and inundations leading to unanticipated severe flooding for all stakeholders involved in the event. Finally, we discuss the implications of the hydrodynamic modeling framework for real-time flood forecasting.

A deep learning based latency aware predictive routing model for network‐on‐chip architectures

SummaryNetwork on Chip (NoC) is an evolving platform for communications related applications, which are executed on a single silicon chip. There are several routing models in NoC architectures, but the accuracy of these models is limited, and the existing models are degraded because of over and under fitting issues. This research introduces the new deep learning‐based latency aware predictive routing model for on‐chip networks to route packets with better performance and power efficiency. The deep learning model used in this research is a new convolutional residual gated recurrent unit (CRGRU) with queuing theory. Moreover, the source and channel queuing delay is comprised of features to learn spatial and sequential information that improves the overall prediction accuracy. This router is modified by the intrusion of the Router States Monitor unit and the CRGRU hardware engine. The work is executed using the Xilinx platform, and the performance measures like latency and throughput are obtained by varying the network size as , , and and also varying the buffer space and length as , , and , respectively. In addition, the squared correlation coefficient (SCC) and normalized root mean square error (NRMSE) are evaluated and compared with existing learning models to validate the proposed model.

Scheduling trucks and drones for cooperative deliveries

Truck and drone based cooperative delivery system is an emerging instant delivery mode for transporting packages in a more timely and efficient way than traditional delivery mode. This paper studies the scheduling of a fleet of truck groups, each of which can carry multiple drones. We formulate a mixed integer programming model for truck groups routes, and for the timing of the drones’ launching and return to their dedicated truck. The model objective is to minimize the total operational cost, including truck travel cost, drone travel cost, using truck groups fixed cost, and potential penalty for late delivery at customer locations. Column generation-based heuristic algorithm and some acceleration techniques are designed for solving the model efficiently. We carry out numerical experiments for validating the effectiveness of the mathematical model, algorithm, and acceleration techniques. In addition, sensitivity analyses are performed to derive some managerial implications.

A multi-stage stochastic programming approach for an inventory–routing problem considering life cycle

Food waste and proper methods to deal with it are one of the main challenges of supply chain network management. The majority of studies on how to use mathematical models in the supply chain have focused on goods that are at their peak of freshness as soon as they are produced and deteriorate over time. While some products experience an increase in value at the start of their life cycle, this value eventually reaches its maximum level, and after this point, these products experience a decline in value before being eliminated from the consumption cycle. The objective of this study is to develop a comprehensive inventory–routing model suitable for supply chain networks where products exhibit an increase and decrease in value over time. By considering the randomness and dynamic uncertainty of market demands and the fact that each period has effects on the next period, The proposed model employs a multi-stage stochastic programming (MSSP) approach. By doing so, the model ensures a balanced flow between different components of the network while considering nondeterministic demand under various scenarios that are shown in a tree of scenarios. The utilization of MSSP leads to more reliable solutions compared to deterministic models, making it possible for chain stores to make well-informed decisions in their inventory management and distribution strategies. Ultimately, this approach results in cost savings for chain stores handling such products. This research makes a significant contribution to the existing literature by demonstrating the effectiveness of the proposed model on actual data and highlighting the benefits of using stochastic programming in supply chain optimization.

Machine learning-enabled calibration of river routing model parameters

Abstract Streamflow prediction of rivers is crucial for making decisions in watershed and inland waterways management. The US Army Corps of Engineers (USACE) uses a river routing model called RAPID to predict water discharges for thousands of rivers in the network for watershed and inland waterways management. However, the calibration of hydrological streamflow parameters in RAPID is time-consuming and requires streamflow measurement data which may not be available for some ungauged locations. In this study, we aim to address the calibration aspect of the RAPID model by exploring machine learning (ML)-based methods to facilitate efficient calibration of hydrological model parameters without the need for streamflow measurements. Various ML models are constructed and compared to learn a relationship between hydrological model parameters and various river parameters, such as length, slope, catchment size, percentage of vegetation, and elevation contours. The studied ML models include Gaussian process regression, Gaussian mixture copula, Random Forest, and XGBoost. This study has shown that ML models that are carefully constructed by considering causal and sensitive input features offer a potential approach that not only obtains calibrated hydrological model parameters with reasonable accuracy but also bypasses the current calibration challenges.

Social mobility and geo-context aware macroscopic routing scheme for mobile opportunistic network

Mobile opportunistic networks (MONs) are deliberated as important aspect to proliferate the wireless communications. These networks pose several challenges such as network lifetime, storage capacity, and forwarding capacity. End-to-End routing schemes are considered as promising technique solution to deal with these issues. In this domain, opportunistic routing has gained huge attention because it follows the broadcasting nature of wireless communication and focus on selection of relay node for packet transmission to ensure the better quality of service (QoS) and energy efficiency. This work focuses on optimizing the next hop selection process and introduced reinforcement learning approach which considers distance, energy and link connectivity to assign the reward for different actions to identify the suitable relay node. Moreover, geo-context and social behaviour based opportunistic routing models are used to increase the reliability of next hop selection. Similarly, social model considers social profiling, social connectivity, and social interaction model to identify the relay node. The outcome of proposed approach is compared with several existing approaches such as prophet, spray and wait, and epidemic routing in terms of packet delivery, and network overhead. The relative study shows that the proposed approach achieves the average packet delivery as 47.22% and minimizes the network overhead.

DFQIoV: Design of a Dynamic Fan-Shaped-Clustering Model for QoS-aware Routing in IoV Networks

Internet of Vehicles (IoV) is a steadily growing field of research that deals with highly ad-hoc wireless networks. These networks require design of high-speed & high-efficiency routing models, that can be applied to dynamically changing network scenarios. Existing models that perform this task are highly complex and require larger delays for estimation of dynamic routes. While, models that have faster performance, do not consider comprehensive parameters, which limits their applicability when used for large-scale network scenarios. To overcome these limitations, this text proposes design of a novel dynamic fan-shaped clustering model for QoS-aware routing in IoV networks. The model initially collects network information sets including node positions, & energy levels, and combines them with their temporal packet delivery & throughput performance levels. These aggregated information sets are processed via a hybrid bioinspired fan shaped clustering model, that aims at optimization of routing performance via deployment of dynamic clustering process. The model performs destination-aware routing process which assists in reducing communication redundances. This is done via a combination of Elephant Herding Optimization (EHO) with Particle Swarm Optimization (PSO), which integrates continuous learning for router level operations. The integrated model is able to reduce communication delays by 5.9%, while improving energy efficiency by 8.3%, throughput by 4.5%, and packet delivery performance by 1.4% under different network scenarios. Due to which the proposed model is capable of deployment for a wide variety of dynamic network scenarios.

Investigation of the propagation of meteorological to hydrological drought and water required to recover from drought over Ethiopian basins

Abstract This study investigates drought propagation from meteorological to hydrological and streamflow required to recover from drought in four sub-basins: Genale, Tekeze, Awash, and Baro basins of Ethiopia. Due to limited observed streamflow data, the soil moisture accounting and routing (SMAR) model was used to extend the streamflow data for each sub-basin from 1985 to 2017. Drought characteristics in terms of duration, severity, and onset/offset of drought and propagation time at different time scales are investigated using run theory and Pearson correlation, respectively. Two Archimedean copulas (Clayton and Gumbel) are used to identify the joint return period between drought duration and severity and the amount of streamflow required to recover from hydrological drought for each sub-basin. Our results revealed that drought frequency has increased over most sub-basins over the last two decades. The propagation time from meteorological drought to hydrological drought is shorter over the Tekeze sub-basin (1–3 months); however, Genale and Awash sub-basin show 3- to 6-month propagation time. The more extended propagation time is seen over the Baro sub-basin (6–9 months). The required amount of water for drought recovery estimation shows a linear relationship between the duration of the drought and the amount required.

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization.

The management of medical waste is a complex task that necessitates effective strategies to mitigate health risks, comply with regulations, and minimize environmental impact. In this study, a novel approach based on collaboration and technological advancements is proposed. By utilizing colored bags with identification tags, smart containers with sensors, object recognition sensors, air and soil control sensors, vehicles with Global Positioning System (GPS) and temperature humidity sensors, and outsourced waste treatment, the system optimizes waste sorting, storage, and treatment operations. Additionally, the incorporation of explainable artificial intelligence (XAI) technology, leveraging scikit-learn, xgboost, catboost, lightgbm, and skorch, provides real-time insights and data analytics, facilitating informed decision-making and process optimization. The integration of these cutting-edge technologies forms the foundation of an efficient and intelligent medical waste management system. Furthermore, the article highlights the use of genetic algorithms (GA) to solve vehicle routing models, optimizing waste collection routes and minimizing transportation time to treatment centers. Overall, the combination of advanced technologies, optimization algorithms, and XAI contributes to improved waste management practices, ultimately benefiting both public health and the environment.

New integrated routing and surveillance model with drones and charging station considerations

Cash/valuables routing is vulnerable to attacks and robberies, so it is essential to prioritize transport security. This research employs a surveillance drone approach to enhance the transport security of cash/valuables routing. Using drones to identify suspicious and high-risk situations is a secure way to transport cash/valuables. Compared to ground-based operators, drones provide superior advantages such as improved security, efficiency, and accuracy. To this end, an integrated routing and surveillance problem is proposed to secure cash/valuables transport. In this problem, ground vehicles (GVs) and surveillance drones are routed in coordination. GVs are responsible for transporting and delivering cash/valuables, while drones are tasked with monitoring the GVs’ routes. The drone surveillance is as follows: before GVs leave the customers’ points, drones must surveil the GV through the link ahead and ensure their security. Links are surveilled in specific time windows. In this problem, charging stations are considered to swap drones’ batteries. In addition, an improved iterated local search (ILS) algorithm is designed in this study to optimize the proposed problem. The performance of the proposed algorithm is then evaluated in small and large sizes. Furthermore, a sensitivity analysis is performed on some key parameters to assess the proposed model. Finally, the proposed approach is implemented in a real-world case study in Iran, with the results analyzed.

Research of a flow-based model of secure Traffic Engineering routing in an infocommunication network with normalized link-blocking conditions

The work improves the flow-based model of secure routing with load balancing in accordance with the Traffic Engineering concept by considering the network security parameters in an information and communication network (ICN). Within the framework of the proposed model, the solution to the technological problem of secure routing with load balancing in an ICN was reduced to solving an optimization problem of linear programming, which guarantees the predictable computational complexity of solutions and low requirements for the computing power of devices responsible for solving routing problems – routers, route servers, controllers, etc. The novelty of the proposed model is the modification of the exponential model of blocking communication links, taking into account normalized conditions to prevent a situation where even the most secure links will be blocked, leading to inefficient use of the link resource. The results of the study of secure routing processes with load balancing in ICN confirmed the model’s effectiveness in considering the network state: its topology, flow characteristics, bandwidth, and congestion of communication links, as well as the probability of their compromise. This made it possible to orient the resulting routing solutions to reduce the congestion of communication links that have a high compromise probability by redistributing traffic to more secure links. In the course of the study, a comparative analysis of the effectiveness of using secure and TE routing models was carried out by a number of indicators. It has been established that the use of SecTE and NormSecTE secure routing models focused on finding a compromise between the Quality of Service and network security indicators. At the same time, the proposed improved model of secure TE routing NormSecTE, based on a more accurate consideration of the probability of compromising communication links, allowed, compared to the SecTE model, to improve the level of network security (packet compromise probability), but with a certain decrease in the level of Quality of Service (average end-to-end packet delay) in the ICN.

Study of reliable routing process to ensure the communication network resilience

The article is devoted to a relevant scientific and applied problem related to improving potential solutions to increase network resilience by means of reliable routing. The study of several routing models is presented, taking into account different types of metrics, in particular, the metric based on the availability indicator. The technological task of routing is formulated in an optimization form. Attention is focused on the flow-based model of reliable routing, and its effectiveness is proved through modeling and comparative analysis with existing approaches. Also, single-path and multipath routing strategies were analyzed in terms of their effectiveness in increasing the level of network resilience. It is demonstrated that the multipath approach to reliable routing is necessary at high loads and contributes to an increase in the overall multi-path availability. At the same time, the implementation of a single-path routing strategy at high loads leads to routing solutions with a low availability indicator and limited use of the available network resource – bandwidth. The numerical research results have confirmed the effectiveness of the proposed multipath routing solution with the availability metric and allowed the formulation of general recommendations related to the implementation of reliable routing in practice. Thus, it is proposed to use a multipath routing strategy to balance the load in the network, together with consideration of availability metrics in calculating multipath for the data flow transmission. Further improvement of the reliable routing model is envisaged, considering the Quality of Service indicators when calculating reliable and productive multipath.

A Framework Designing of Routing Model for Path Planning of Vehicles Using IoT

A Framework Designing of Routing Model for Path Planning of Vehicles Using IoT

An Empirical Study on Impact of Project Management Constraints in Agile Software Development

Goal: The Study aims to investigate the impact of project management constraints in fixed-cost and fixed-scheduled Agile software development contracts using multigroup analysis (MGA) to perform repeated comparisons of parameters across groups of Scrum or Kanban methodologies using structural equation modelling (SEM) Design/Methodology/Approach: A web-based survey is used to collect responses to a questionnaire based on project management constraints for Kanban and Scrum method-based Agile software development projects from people working on projects fixed-cost and fixed-schedule contracts and then analyzed with the help of multi-group analysis using SmartPLS 4.0 Results: Risk management has a mediating effect between project scope, resources, and delivery quality. MGA implied that the Kanban method is better at managing resources and will have a higher impact on the quality of deliverables than the Scrum method. Limitations of the investigation: Evaluating the suitability of various multi-group analysis approaches requires more than just our empirical example using satisfactory data and other project management constraints. Practical implications: The findings of the study suggest that hypothesis testing should be carried out for a distinct model parameter between study groups, whenever they are comparing more than two group. Originality/Value: This study helps in contributing to exploring PLS route modelling by the introduction of the original non-parametric confidence set approach based on a comparison of parameter estimations and bootstrap confidence intervals. The study investigates the impact of several project management constraints on to fixed-cost Agile software development projects.

Lake-TopoCat: a global lake drainage topology and catchment database

Abstract. Lakes and reservoirs are ubiquitous across global landscapes, functioning as the largest repository of liquid surface freshwater, hotspots of carbon cycling, and sentinels of climate change. Although typically considered lentic (hydrologically stationary) environments, lakes are an integral part of global drainage networks. Through perennial and intermittent hydrological connections, lakes often interact with each other, and these connections actively affect water mass, quality, and energy balances in both lacustrine and fluvial systems. Deciphering how global lakes are hydrologically interconnected (or the so-called “lake drainage topology”) is not only important for lake change attribution but also increasingly critical for discharge, sediment, and carbon modeling. Despite the proliferation of river hydrography data, lakes remain poorly represented in routing models, partially because there has been no global-scale hydrography dataset tailored to lake drainage basins and networks. Here, we introduce the global Lake drainage Topology and Catchment database (Lake-TopoCat), which reveals detailed lake hydrography information with careful consideration of possible multifurcation. Lake-TopoCat contains the outlet(s) and catchment(s) of each lake; the interconnecting reaches among lakes; and a wide suite of attributes depicting lake drainage topology such as upstream and downstream relationship, drainage distance between lakes, and a priori drainage type and connectivity with river networks. Using the HydroLAKES v1.0 (Messager et al., 2016) global lake mask, Lake-TopoCat identifies ∼ 1.46 million outlets for ∼ 1.43 million lakes larger than 10 ha and delineates 77.5×106 km2 of lake catchments covering 57 % of the Earth's landmass except Antarctica. The global lakes are interconnected by ∼ 3 million reaches, derived from MERIT Hydro v1.0.1 (Yamazaki et al., 2019), stretching a total distance of ∼10×106 km, of which ∼ 80 % are shorter than 10 km. With such unprecedented lake hydrography details, Lake-TopoCat contributes towards a globally coupled lake–river routing model. It may also facilitate a variety of limnological applications such as attributing water quality from lake scale to basin scale, tracing inter-lake fish migration due to changing climate, monitoring fluvial–lacustrine connectivity, and improving estimates of terrestrial carbon fluxes. Lake-TopoCat is freely accessible at https://doi.org/10.5281/zenodo.7916729 (Sikder et al., 2023).

Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches

Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches

An Integrative Framework for Assessment of Urban Flood Response to Changing Climate

AbstractIncreasing frequency of extreme rainfall induced catastrophic urban flood events in recent decades demands proactive efforts to assess flood risk and vulnerability. Here, we develop an integrative framework for fine spatiotemporal scale assessment of urban flood response to historical and future projected changes in extreme precipitation. The framework includes three main components—nonstationary modeling of historical extreme precipitation, modeling of future precipitation, and urban flood simulations. It also provides robust estimates of uncertainty in design precipitation from statistical modeling, multiple climate models and stochastic uncertainty, estimated using machine learning techniques. We demonstrate the proposed framework for White Oak Bayou watershed in Houston, Texas, US. Two‐dimensional hydrologic‐hydraulic Interconnected Channel and Pond Routing model is used to simulate flood response from design precipitation for historical (1986–2020) and two future (2021–2050 and 2071–2100) periods in three (SSP1‐2.5, SSP2‐4.5, and SSP5‐8.5) future climate scenarios. Results show that nonstationary design estimates for historical precipitation are 14%–25% higher than the stationary estimates for 100‐year event of 1‐ to 24‐hr duration. Contrary to general global trends, we found a significant reduction in future design precipitation in all three emission scenarios. Additionally, stochastic uncertainty in future design precipitations is found to be larger than the modeling uncertainty in historical estimates and climate model uncertainty in future estimates. Flood response in terms of peak flood and total flood volume suggests that the difference in stationary versus nonstationary historical and future design precipitation are substantial to cause considerable change in simulated flood.

Modeled Forest Conversion Influences Humid Tropical Watershed Hydrology More than Projected Climate Change

In the humid tropics, forest conversion and climate change threaten the hydrological function and stationarity of watersheds, particularly in steep terrain. As climate change intensifies, shifting precipitation patterns and expanding agricultural and pastoral land use may effectively reduce the resilience of headwater catchments. Compounding this problem is the limited long-term monitoring in developing countries for planning in an uncertain future. In this study, we asked which change, climate or land use, more greatly affects stream discharge in humid tropical mountain watersheds? To answer this question, we used the process-based, spatially distributed Soil Moisture Routing model. After first evaluating model performance (Ns = 0.73), we conducted a global sensitivity analysis to identify the model parameters that most strongly influence simulated watershed discharge. In particular, peak flows are most influenced by input model parameters that represent shallow subsurface soil pathways and saturation-excess runoff while low flows are most sensitive to macropore hydraulic conductivity, soil depth and porosity parameters. We then simulated a range of land use and climate scenarios in three mountain watersheds of central Costa Rica. Our results show that deforestation influences streamflow more than altered precipitation and temperature patterns through changes in first-order hydrologic hillslope processes. However, forest conversion coupled with intensifying precipitation events amplifies hydrological extremes, reducing the hydrological resilience to predicted climate shifts in mountain watersheds of the humid tropics. This finding suggests that reforestation can help mitigate the effects of climate change on streamflow dynamics in the tropics including impacts to water availability, flood pulses, channel geomorphology and aquatic habitat associated with altered flow regimes.

An investigation of operational management solutions and challenges for electric taxiing of aircraft

Taxiing aircraft using electric towing vehicles (ETVs) is expected to significantly contribute to the objective of climate-neutral aviation by 2050. This study reviews existing work on operational aspects of electric towing of aircraft, and discusses management solutions. We first discuss the varying electric taxi systems currently under development, and their implementation progress at airports. We outline the current specifications of ETVs and the procedures needed to perform electric taxiing movements. We next discuss the management needs for implementing ETVs at an airport, by reviewing existing mathematical models for ETV fleet management: dedicated vehicle routing models, ETV to flight assignment models, fleet sizing models and battery charging optimisation models. Last, we identify remaining research challenges. For instance, a main challenge is to increase the robustness of ETV routing and towing scheduling against disruptions due to flight delay. This paper summarizes the main research directions needed to support large-scale ETV implementation in the next few decades.

Isolation and Virulence Evaluation of Currently Circulating Avibacterium paragallinarum in Poultry

Avibacterium paragallinarum is a Gram-negative bacterium that causes infectious coryza (IC), a respiratory disease that is highly contagious among chickens. Through PCR testing and sequence analysis of the FlfA gene, a total of 10 Av. paragallinarum field isolates were identified, accounting for 5.7% of the samples. In this study, we assessed the virulence of field isolates by utilizing a rapid artificial intrasinus injection route model. Upon observation, we have found that all 10 field isolates from Egypt that were identified as type B strains possess virulent properties and have the ability to cause Infectious Coryza (IC) disease in chickens. The current study aimed to sequence the FlfA gene and conduct phylogenetic analyses to better understand the Avibacterium paragallinarum field isolates that have been circulating in Egypt in recent years. This research will provide valuable insights into the genetic makeup and evolution of this pathogen, which can inform future efforts to control and prevent its spread. The amino acid sequences of Avibacterium paragallinarum from Egypt and the reference sequence exhibit a remarkable degree of homology, ranging from 97.96% to 100%. This finding underscores the close relationship between the two sequences and highlights the potential for further research into the genetic makeup of this pathogen. Furthermore, the discovery of a fimbrial cluster that is identical to the Galli bacterium FlfA in the Av. paragallinarum genome indicates that these two species may have recently exchanged this fimbriae. This exchange could have occurred during the natural co-colonization of the chicken's upper respiratory tract. It is possible that this exchange could have implications for the pathogenicity and virulence of these bacteria. The FlfA protein has unique characteristics and was subjected to bioinformatics analysis to predict its properties and protein interactions. The FlfA protein structure is stable with many coiled regions. ..