Operational Guidance Research Articles

Ensemble Precipitation Forecast Postprocessing with Ensemble Coalescence and Quantile Mapping

Abstract Ensemble forecast systems are an integral part of the National Weather Service’s (NWS) program for producing skillful, reliable deterministic and probabilistic forecasts, but statistical postprocessing is often required to address known deficiencies. Here, we test improvements to the ensemble postprocessing procedure currently implemented at the NWS Meteorological Development Laboratory (MDL) that generates a deterministic quantitative precipitation forecast from multimodel ensemble means. The existing approach uses recent forecast and analysis data to estimate cumulative distributions of precipitation and then applies a quantile mapping between the mean forecast and analysis. In the current work, we use a revised version of this approach that, in addition, utilizes ensemble mean inputs improved by a coalescence procedure based on the feature alignment technique (FAT). This is a variational technique for determining the displacements needed to adjust precipitation features of individual ensemble members toward their positions in the raw ensemble mean. The performance of the combined coalescence–quantile mapping algorithm was evaluated over a 2-yr period of forecasts from version 12 of the NCEP Global Ensemble Forecast System (GEFSv12) after extensive testing of configurable parameters to ensure the physical reasonableness of the precipitation fields. The coalescence procedure by itself was shown to correct some of the raw ensemble mean deficiencies related to position errors in the individual ensemble forecasts, especially as lead times increase. The combined approach resulted in improved forecasts as measured by fractional bias and equitable threat scores. Implementation of the techniques described here for operational guidance is being considered by the National Oceanic and Atmospheric Administration (NOAA) and is applicable to other forecast models. Significance Statement Ensemble weather prediction systems provide information about the uncertainty in weather forecasts. However, the raw output from these models is voluminous and suffers from biases. Here, we combine two statistical approaches to correct for the biases and create a single deterministic forecast that is more manageable to use by stakeholders. The two techniques—coalescence and then quantile mapping—are applied in sequence and the resulting model forecast is compared against observations. Our approaches improve the statistical performance with precipitation fields that have more realistic extrema and a smaller extent of small precipitation amounts. The techniques are being evaluated for operational implementation by the National Weather Service.

AI in airport operations: enhancing competitiveness and satisfaction

ABSTRACT During COVID-19, airports adopted biometric technologies, RFID for baggage tracking, and AI like smart check-in to minimise contact and enhance efficiency. AI also addresses infrastructure limitations and enhances competitiveness. Most studies focus on improving traveller experiences and operations, with limited research on gaps between traveller and operator preferences. This study bridges that gap using DEMATEL and VIKOR methods, revealing that travellers prioritise convenience and real-time information. By contrast, experts value predictive capabilities. Findings suggest that aligning AI-driven predictive technologies with traveller needs can boost satisfaction and efficiency, offering a win-win solution and strategic guidance for competitive airport operations.

Preoperative Ultrasound Correctly Localized Peripheral Nerve Abnormalities for Operative Guidance: A Retrospective Review.

Correct localization and characterization of nerve abnormality is of critical importance to appropriate intervention. Ultrasound (US) is known to be accurate in the diagnosis of peripheral neuropathy and in preoperative localization of nerve abnormalities and skin marking. We sought to investigate the utility of US-guided preoperative skin marking for the localization of peripheral nerve abnormality and to compare the US findings to electrodiagnostic (EDx) reports. Using the radiology information system at a single institution, we identified US examinations performed for preoperative localization of peripheral nerve abnormality from July 2016 to March 2023. Data collected included US characterization, surgical description, and EDx report of neuropathy. Search parameters identified 67 nerves in 55 patients treated surgically after US-guided localization of the nerve with skin marking. The EDx characterization was performed in 36 (54%) of these cases. The US diagnoses included neuroma, transection, perineural scarring, hardware impingement, and intraneural fascicular constriction. There was 100% accuracy of US findings as confirmed by operative notes. Skin marking by US guidance correlated to the sites of the nerve documented in operative reports of all 67 cases. In this single-institution retrospective review, US-guided preoperative skin marking of nerve abnormality was used to correctly localize peripheral nerve abnormality, and US diagnoses were corroborated by intraoperative findings. Further higher-level study is needed to support these findings suggesting the efficacy of US in mapping the course of peripheral nerves.

Big Data Analytics and Machine Learning Framework for Optimizing Struvite Precipitation in Smart Wastewater Treatment Plants: A Decision Support System Approach

The paper presents a novel, information-based approach using machine learning and big data analytics to support struvite precipitation in the context of wastewater treatment plants. The framework is based on three interdependent functional stacks: a data ingestion stack that accounts for process instrumentation, process parameters, and surrounding environment monitors that feed data into a common integrated representation of the system; an analytics stack that employs ML algorithms to establish nonlinear mappings between parameters and predict healthy operating condition rods; and finally, a decision support interface that translates the analytics insights into actionable operating guidance. The data integration layer (at the bottom of the above) uses strong validation protocols to syncronize multiple streams of reliable data. The analytics layer not only grants predictive performance by creating machine learning models, the machine learning creates adaptive behavior across the system, enabling real-time functionality without compromising its predictive performance. These recommendations at the decision support interface level, on the other hand, are based on all this complexity, distilled into actionable insights, aligned with operational transparency and flexible control of the same. The architecture then proceeds to discuss high-level implementation considerations including infrastructure, operator training needs and system security procedures. Its modular structure offers flexibility, enables the relevance with existing operational constructs while also creating a pathway to innovations that will ultimately be the ‘tomorrow’ of the industry. By improving knowledge of reactor-associated microbial communities and dynamic processes, this guide aims to help achievement of increased phosphorus recovery efficiencies and further development of smart waste water treatment technologies.

Supplemental Operational Guidance for Minimizing Potential Inhalation Doses to Workers and Volunteers at Community Reception Centers and Public Shelters.

In the event of a nuclear explosion in an urban environment, contaminated persons may be directed to Community Reception Centers (CRC) and/or public shelters. This paper is a companion document to a previous paper that addresses the inhalation hazard to workers at a CRC from resuspension of fallout from the evacuees. To limit the inhalation hazard evacuees must be screened to prevent severely contaminated persons from entering a CRC. The suggested screening level is 10,000 dpm cm-2 and rapid methods of screening arriving evacuees are presented. Practical advice is provided on methods that can be used to limit contamination within a CRC. These methods include alterations to heating and cooling systems and the implementation of monitoring strategies to guard against unexpected increases in airborne activity levels.

Boosting nitrogen removal efficiency of wastewater treatment plant tailwater through iron-rich tidal flow constructed wetland: Insights on the microbial removal mechanism

Iron-rich tidal flow constructed wetlands (ITCW) have emerged as effective and eco-friendly systems for achieving high-efficiency total nitrogen (TN) removal in tailwater. However, the microbial removal mechanism in ITCW, particularly the microbial assembly processes, remains unclear, which limits understanding of the relationship between micro-level microbial activities and macro-level performance of ITCW. In this study, ITCW was constructed to investigate its performance, microbial responses, and community assembly mechanisms using metagenomics and the neutral community model (NCM). Results demonstrated that total nitrogen removal in ITCW increased by 52.53 ± 9.03 % and 21.20 ± 6.26 %, respectively, compared to conventional constructed wetlands and iron-based constructed wetlands. The combination of tidal flow and iron substrate established an active iron cycle in ITCW. The iron cycle improved electron transfer capacity and supported the enrichment of nitrifiers (e.g., Nitrospira) and denitrifiers (e.g., Hydrogenophaga, Thauera), enhancing nitrogen removal efficiency in ITCW. Metagenomic analysis confirmed an increased relative abundance of functional genes associated with nitrogen metabolism (e.g., amoA, nirSKABD) and the iron cycle (e.g., korB). NCM analysis indicated that stochastic processes influenced community assembly in all three CWs. Nonetheless, tidal flow operations in ITCW reduced stochastic influences, fostering niche specialization of functional nitrogen microorganisms, which supported their growth while minimizing functional redundancy, thereby enhancing nitrogen treatment performance. These findings offer significant insights into microbial mechanisms for nitrogen removal and provide theoretical guidance for ITCW design and operation.

Hybrid Numerical Weather Prediction: Downscaling GraphCast AI Forecasts for Downslope Windstorms

Abstract Preemptively managing electrical circuits during periods of elevated wildfire risk, such as downslope windstorms in complex terrain, necessitates accurate, high-resolution forecasts days in advance. Currently available high-resolution operational guidance is limited with respect to the forecasting period and/or spatial detail it can provide. As a consequence, many public utilities support their own NWP systems, primarily WRF-based, with initial conditions and boundary forcings supplied by operational global models such as the GFS. This study demonstrates pairing Google’s artificial intelligence (AI)-based GraphCast (GC) model, which operates on a 0.25° grid and produces global forecasts every 6 h, with a “last mile” WRF (WRF-LM) downscaling framework to refine GC’s spatial and temporal resolution for utility forecasting needs. We illustrate the potential of this system through a case study involving the December 2021 Marshall Fire windstorm in the Boulder, Colorado, area. Our results suggest that GC-initialized WRF forecasts are competitive with those guided by NCEP and ECMWF operational products, offering a promising hybrid approach for utility weather forecasting. Significance Statement Downslope windstorms are difficult to forecast with small errors potentially affecting the timing, magnitude, and location of concerning winds. Due to the elevated wildfire danger caused by these windstorms, accurate forecasts with lead times greater than 48 h are often needed to allow utilities time to prepare for the de-energization of transmission lines to reduce ignition risk. This paper examines methods of downscaling coarse synoptic-scale forecasts using the downslope windstorm associated with the 2021 Marshall Fire as our case study. A novel aspect of this work is the incorporation of machine learning weather prediction models and downscaling their forecasts using a high-resolution numerical model.

Robot teleoperation assisted by composite virtual fixtures in dynamic environment

Purpose This study aims to propose a virtual fixture (VF) – assisted robot teleoperation framework that modulates the generated trajectory from demonstrations to response varying obstacles in complex environment. Design/methodology/approach First, a single trajectory is learned from demonstration using dynamic movement primitives (DMP). Then, the classic DMP is improved by integrating adaptive terms and updating the radial basis function kernel weights, allowing the single-skill trajectory to alter to respond to obstacles dynamically. Finally, composite virtual forces are generated on the haptic device to enhance operational stability and prevents unintentional operations that could bring the robot into close proximity with obstacles. Findings The VF method can accomplish online obstacle avoidance and operation guidance. The experimental results show that the operation trajectory based on VFs is smoother compared to the operation without assistance. Moreover, the operational speed has increased by up to 44.37% compared to the demonstrations. Originality/value The proposed composite VF-based protection framework solves the problem that classical DMP method cannot dynamically avoid obstacles, and effectively improves the operational safety and efficiency.

Two-stage robust optimization for the integrated dynamic berth allocation and quay crane scheduling problem under uncertainty

A container port is an important cargo transit node that links the land and maritime regions, with berth allocation and quay crane scheduling tasks. Often, bad weather and delays in loading information make vessels’ navigation uncertain. Therefore, in contrast to the classical berth allocation and quay crane assignment and scheduling problem, which minimizes the makespan, this study proposes a novel two-stage robust mixed-integer linear programming model for the integrated dynamic berth allocation and time-variant quay crane scheduling problem to minimize the total departure time, under uncertainties of vessel arrival time and container demand. An extended column constraint generation algorithm with a variable reduction strategy is designed to solve the model. Extensive computational experiments are performed to verify the effectiveness of the proposed model and algorithm. The conclusions drawn from this study could provide operational guidance for container operators to deal with uncertainties.

Nature-based solutions as urban adaptation to climate risk: Framework for economic evaluation as decision support tool

Integration of Nature-based solution (NBS) as adaptation in planning and policy remains a challenge due to lack of adequate information on economic feasibility. This is mainly due to non-availability of economic evaluation framework for informed decision. Present study tries to address this by examining the status of evaluation frameworks through a systematic review of peer-reviewed articles published between 2015 and 2023. Based on the synthesis of the evidence a five-step framework, exclusively for economic evaluation of NBS as urban adaptation has been developed. Using this a novel, holistic, just, equitable and inclusive cyclical decision tool has been proposed. The review confirmed the lack of economic assessment and a holistic evaluation framework. The evaluation framework has been backed by operational guidance by providing comprehensive recommendations on the methodologies, tools and techniques and indicators and metrics that can be used for execution of each step. The proposed cyclical decision tool facilitates equity and justice by having provisions for ensuring equal participation of each stakeholder in decision making. It specifically ensures incorporation of plurality of knowledge and in particular value of the ecosystem services (ESs) from NBS. The tool has applicability across the urban spatial scale in cities of developed and developing economies.

Impact of cutting parameters on surface roughness in aluminum alloys machining: a review of machine learning models for key parameter identification

This paper provides a comprehensive review of research on the influence of machining parameters—depth of cut, feed rate, and cutting speed—on surface roughness, with a focus on aluminum alloys. Surface quality in CNC machining is significantly affected by these parameters, with numerous studies highlighting their impact on achieving desired surface roughness. The review analyzes findings from ten studies, emphasizing that feed rate is generally identified as the most influential parameter for surface roughness. While feed rate shows a dominant effect, cutting speed and depth of cut also contribute, though to a lesser extent. The research includes a discussion of various methodologies, including ANOVA, the Taguchi method, and more simple machine learning regression models, which demonstrate strong alignment with experimental results and highlight the effectiveness of advanced regression-based models in predicting surface roughness. The study concludes that optimizing feed rate is crucial for achieving high surface quality values, while cutting speed and depth of cut should be managed appropriately. The findings underscore the importance of machine learning tools in analyzing and optimizing machining parameters, offering practical guidance for CNC machine operators and engineers.

Methane emissions at pressure-regulating stations in China: A comparative analysis of various quantitative methods.

As a key component of the distribution system, metering and pressure-regulating (M&R) stations provide an opportunity for effective mitigation of methane emissions. Given that these stations are readily accessible, above-ground facilities, routine methane emission monitoring can identify issues and with repair or upgrades this can lead to reduced methane emissions. This practice has become an important measure for addressing climate change. China aims to actively promote the monitoring, evaluation, and inventory improvement of distribution systems. In this study, methane detection and analysis were conducted at 11 pressure regulation-metering stations operated by a company in North China and Central China. Methane emissions were estimated using the dynamic flux chamber method, the inverse Gaussian plume modeling method (OTM33A), and the stochastic Lagrangian (LS) inverse modeling method. The results of the dynamic flux chamber method indicate that flanges, instrumentation and meters, and connectors are the primary sources of methane emissions at pressure-regulating stations, with emission factors of 0.0034kg/h (CI: 0.0030-0.0038kg/h), 0.0022kg/h (CI: 0.0018-0.0025kg/h), and 7.1e-04 (CI: 6.3e-04-8.0e-04) kg/h, respectively. The correlation between detected concentration and emission rate was weak. The cumulative fluxes calculated by the three methods were 0.109kg/h (CI: 0.094-0.125kg/h), 0.143kg/h (CI: 0.015-0.337kg/h), and 0.125kg/h (CI: 0.035-0.218kg/h). The results of controlled tests and field measurements suggest that the LS method offers a more reliable and physically accurate analysis of methane fluxes for pressure regulating-metering stations with limited measurement distances (typically less than 30m) and small continuous emissions. The emission factor for the pressure-regulating stations in this study is 0.013kg/h (CI: 0.008-0.023kg/h) using the bootstrap Monte Carlo method. This research provides guidance for operators to implement fixed monitoring, which is crucial for building emission inventories and ensuring production safety.

Dynamic characteristics of wave entry for a deep-sea mining vehicle: Numerical and experimental studies

The deep-sea mining vehicle (DSMV) experiences complex impacts upon the wave entry, which directly affects the safety and reliability of the whole deployment system. In this paper, a computational fluid dynamics (CFD) method is proposed to investigate hydrodynamic properties and wave-entry effects of a DSMV with varying laying speeds. Firstly, a fifth-order Stokes surface gravity wave simulating Sea State 4 conditions is constructed based on the volume of fluid (VOF) method. Then, the overset mesh scheme is utilized to establish the multiple degree-of-freedom (DOF) model of DSMV which bears the traction loads of laying cable. Laboratory prototype tests are conducted, and the captured results of cavitation evolution and motion properties corroborate the present model. Numerical results indicate that the DSMV entering the wave danger point will experience hydrodynamic forces several to dozens of times greater, severely threatening structural safety. During the wave entry process, two phases of nonlinear deceleration occur, posing a risk to the umbilical cable. Surge and pitch motions caused by waves and structural asymmetry may lead to deployment position deviation. Based on the trends observed in the simulation data, guidance for deployment operations has been proposed.

Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method

In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R2 values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations.

Operationalizing implementation science frameworks to plan a hybrid effectiveness-implementation study of a digital health intervention.

Evaluating implementation of digital health interventions (DHIs) in practice settings is complex, involving diverse users and multistep processes. Proactive planning can ensure desired implementation determinants and outcomes are captured for hybrid studies, but operational guidance for DHI studies is limited. We planned a cluster randomized, type II hybrid effectiveness-implementation trial testing PositiveLinks, a smartphone application for HIV care, compared to usual care (n = 6 sites per arm), among HIV outpatient sites in the DC Cohort Longitudinal HIV Study in Washington, DC. Our process included: 1) Defining components of the DHI and associated implementation strategy, 2) Selecting implementation science frameworks to accomplish evaluation aims, 3) Mapping framework dimensions, domains, and constructs to implementation strategy steps, 4) Modifying/creating instruments to collect data for implementation outcome measures and determinants and 5) Developing a compatible implementation science data collection and management plan. Specification of components of the DHI and implementation strategy identified relevant platform usage data and necessary implementer actions, toward planning measurement of program reach and adoption. A priori mapping of implementation strategy steps to the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework identified how data would be collected for each step/corresponding outcome measure. The multi-site cohort study provided infrastructure for prospective, scaled implementation research, including site research assistants (RAs) coordinating implementation. Existing cohort tools (periodic site assessments, patient consent logs) were adapted for the evaluation to further capture representativeness and reach/adoption 'denominators.' New survey instruments allowed for framework-guided evaluation of provider adaptations/dose/fidelity to planned implementation across a large number of participants and multiple timepoints. Some aspects of real-world implementation were challenging to mirror within the planned hybrid trial (e.g. RAs selected as de facto site implementation leads) or were modified to preserve internal validity of effectiveness measurement (e.g. PositiveLinks 'community of practice'). Challenges and opportunities arose in planning the implementation evaluation for PositiveLinks within a hybrid trial in a cohort population. Prospective hybrid trial planning must balance generalizability of implementation processes to 'real world' conditions, with rigorous trial procedures to measure intervention effectiveness. Rapid, scalable tools require further study to enable evaluations within large, multi-site hybrid studies.

How to Evaluate the Operating Performance of Mid-Deep Geothermal Heat Pump Systems (MD-GHPs): A Study on a Multistage Evaluation Index System

Mid-deep geothermal heat pump systems (MD-GHPs) use mid-deep borehole heat exchangers (MDBHEs) to extract heat from the geothermal energy at a depth of 2–3 km, and have been used for space heating in China over the last decade. This paper proposes a comprehensive and multilevel evaluation-index system to analyze and evaluate the energy performance of MD-GHPs. The multilevel evaluation index system consists of a target layer, a criterion layer, and an index layer, where the criterion layer is subdivided into six aspects and the index layer includes 26 specific indices, reflecting the geothermal resources, heat transfer performance of the MDBHEs, energy efficiency of the heat pump systems, building space heating demand, grid dynamic response capability, and energy-saving and economic benefits. Then, based on both expert survey results and case study data, the entropy weight method and the analytic hierarchy process are integrated to determine indicator weight coefficients among the multilevel evaluation indices, comprehensively considering both subjective and objective analyses. Furthermore, a fuzzy comprehensive evaluation model is conducted to integrate these weighted indices into a multi-criteria evaluation of MD-GHP performance. Finally, the proposed method was applied to evaluate the practical performance of four projects, returning scores of 61.56, 58.33, 72.73, and 78.41. These evaluations enable an overall assessment of the energy performance of MD-GHPs, reflecting the technical weaknesses and offering optimization guidance for system design and operation.

AT-DBN: A Novel Deep Learning Approach for Accurate Flue Gas Pressure Forecasting in Power Plants

Accurately predicting and controlling the flue gas pressure at the induced draft fan inlet is crucial for environmental protection and boiler efficiency. This study designed a model of Attention-enhanced Deep Belief Network (AT-DBN) to predict the flue gas pressure parameters at the induced draft fan inlet, based on key improvements to the Deep Belief Network (DBN). The main improvement involves introducing an attention mechanism to enhance the prediction accuracy of the model for the flue gas pressure parameters at the induced draft fan inlet. The study begins with meticulous preprocessing of historical operational data, including identifying and removing outliers to ensure data quality for subsequent analysis. Next, Pearson correlation analysis is employed to systematically evaluate the correlation between various variables and the flue gas pressure, thereby identifying key variables that significantly affect prediction performance. Finally, an attention mechanism is integrated into the traditional DBN architecture, aiming to enhance the network’s ability to perceive important information within the input features, thereby improving prediction accuracy and model generalization. The results demonstrate that the proposed AT-DBN model can accurately predict the flue gas pressure at the induced draft fan inlet across multiple test sets, providing valuable guidance for power plant production operations.

Attitudes, perceptions, and intentions toward sports betting legalization: a cluster analysis of California residents

ABSTRACT In the United States, the potential legalization of sports betting in non legal states presents a complex landscape of opportunities and challenges. This study examines California voters’ attitudes toward sports betting, the perceived impacts of its legalization, and the factors influencing their voting intentions. Utilizing the Theory of Reasoned Action (TRA), the research explores how attitudes and subjective norms shape voter behavior. A K-Means clustering analysis identified three distinct voter segments: supporters, neutrals, and opponents. The analysis revealed significant differences in voting intentions, attitudes, and perceived impacts across these clusters. These findings highlight the importance of robust policy and tailored marketing strategies that address the specific concerns and motivations of each voter segment. By providing insights into voter preferences and behaviors, this research offers valuable guidance for policymakers, operators, and stakeholders involved in the sports betting industry, supporting informed decision-making and responsible policy and messaging strategy.

Continual Learning and Adaptation In Resource-Constrained Environments (CLAIRE)

As climate-related extreme events continue to increase and impact the world, of particular importance are the threats posed to food, agriculture, and water (FAW) systems. Deep learning could benefit FAW systems for classification of threats and for forecasting potential future events given historical patterns. However, many FAW systems are faced with operational environments that are resource-constrained, which could present challenges in deploying deep learning models. Continual learning offers a way to overcome certain deployment challenges by enabling deep learning models that are more robust to data distribution changes, without the need for GPUs or off-line training. We describe a continual learning approach to forecasting extreme air quality events developed for the National Oceanic and Atmospheric Administration to provide operational air quality guidance to the Continental United States. We describe how this deep learning model is resilient to future data distribution changes by performing curriculum learning, and how it can be deployed as a continual learner, offering better predictive performance for resource-constrained environments.

Movement Characteristics of Droplet Deposition in Flat Spray Nozzle for Agricultural UAVs

At present, research on aerial spraying operations with UAVs mainly focuses on the deposition outcomes of droplets, with insufficient depth in the exploration of the movement process of droplet deposition. The movement characteristics of droplet deposition as the most fundamental factors affecting the effectiveness of pesticide application by UAVs are of great significance for improving droplet deposition. This study takes flat spray nozzles as the research object, uses the Particle Image Velocimetry (PIV) technique to obtain movement data of water droplet deposition under the influence of rotor flow fields, and investigates the variation characteristics of droplet deposition speed under different influencing factors. The results show that the deposition speed and the distribution area of high-speed (>12 m/s) particles increase with the increase of rotor speed, spraying pressure, and nozzle size. When the rotor speed increases from 0 r/min to 1800 r/min, the average increase in maximum droplet deposition speed for nozzle models LU120-02, LU120-03 and LU120-04 is 33.26%, 19.02%, and 7.62%, respectively. The rotor flow field significantly increases the number of high-speed droplets, making the dispersed droplet velocity distribution more concentrated. When the rotor speed is 0, 1000, 1500, and 1800 r/min, the average decay rates of droplet deposition speed are 36.72%, 20.00%, 15.47%, and 13.21%, respectively, indicating that the rotor flow field helps to reduce the decrease in droplet deposition speed, enabling droplets to deposit on the target area at a higher speed, reducing drift risk and evaporation loss. This study’s results are beneficial for revealing the mechanism of droplet deposition movement in aerial spraying by plant protection UAVs, improving the understanding of droplet movement, and providing data support and guidance for precise spraying operations.