Stream Density Research Articles

Toward the fabric of the Milky Way. I. The density of disk streams from a local 250^3 pc^3 volume

We studied 12 disk streams found in a 250^3 pc^3 volume in the solar neighborhood, which we define as coeval and comoving stellar structures with aspect ratios greater than 3:1. Using Gaia Data Release 3 data and the advanced clustering algorithms SigMA and Uncover we identified and characterized these streams beyond the search volume, doubling, on average, their known populations. We estimate the number density of disk streams to be ≈ 820 objects,/,kpc^3 (for $|Z| < 100$,pc), or surface densities of ≈ 160 objects,/,kpc^2. These estimates surpass N-body estimates by one to two orders of magnitude and challenge the prevailing understanding of their destruction mechanisms. Our analysis reveals that these 12 disk streams are dynamically cold with 3D velocity dispersions between 2 and 5,km,s^-1, exhibit narrow sequences in the Hertzsprung-Russell diagram, and are highly elongated with average aspect ratios of 7:1, extending up to several hundred parsecs. We find evidence suggesting that one of the disk streams, currently embedded in the Scorpius-Centaurus association, is experiencing disruption, likely due to the primordial gas mass of the association.

EXPERIMENTAL STUDIES OF PLASMA-SURFACE INTERACTIONS DURING INCLINED QSPA PLASMA IMPACTS ON Sn-FILLED CPS

A 3D-printed tungsten Sn Capillary Porous Structure (CPS) sample was exposed to oblique high-power plasma in the QSPA facility. The experiment aimed to analyze the damage to a liquid metal prototype, a potential component of the divertor in fusion tokamaks. Observations of plasma-surface interactions revealed particle ejection from the exposed target, which depended on the energy density of the incoming plasma stream. The leading edge of the CPS sample was identified as the primary source of the ejected particles. A reduction in mass loss rate of the plasma-treated sample over the course of the experimental series was demonstrated. The W substrate of the CPS target did not sustain significant damage. A comparative analysis of the damage to Sn-CPS and castellated W samples exposed to inclined and normal plasma streams under conditions simulating transients in a fusion reactor was also performed.

Features of Formation of Electromagnetic Fields of Industrial and Radio-Frequency Ranges in the Conditions of the Modern Residential Development of High Number of Storeys

The tool research of electromagnetic fields of industrial frequency and radio-frequency range in by Murino of the Leningrad Region is executed. Steady excesses of admissible level of tension on borders of a sanitary rupture of the high-voltage line of 330 kV are revealed. Excesses of admissible levels of magnetic induction and in the radio-frequency range – energy stream density are not revealed. It is established that the difference of values of electric field strength in 1.8 m from a surface and at the height of 72 m reaches 1–2 orders that can be explained only with the grounding Earth’s surface role. Considerable divergences of the settlement and measured values of tension and magnetic induction are revealed that means insufficiently correct account when calculating electrophysical properties of air. The detailed cards of density of a stream of energy showing a difficult, but steady picture of distribution of values are created. Some elements of territorial structure of electromagnetic fields of radio-frequency range – fragments of rings around base stations of cellular communication, and the auras dated for certain houses are revealed.

Assessing Hydrological Response and Resilience of Watersheds as Strategy for Climatic Change Adaptation in Neotropical Region

This study aimed to assess the hydrological response and resilience of watersheds in a neotropical region to identify regions sensitive to climate variations, enabling the development of adaptive strategies in response to global environmental changes. This study applied Budyko’s framework using Fuh’s hydrological model rewritten by Zhou to estimate hydrological response and Budyko’s metrics (deviation and elasticity) to estimate hydrological resilience to climatic changes in 26 watersheds in southeastern Brazil. The proposed modeling was able to capture the differences among the watersheds, with “m” values ranging from 1.79 to 3.63. It was possible to rank the hydrological resilience from low to high across watersheds using Budyko’s metrics, where the highest values of elasticity were found in watersheds with a higher percentage of forest cover. The sensitive analyses showed that watersheds with higher “m” values are more sensitive to changes in precipitation and potential evapotranspiration. The results also demonstrate that mean elevation and stream density were two key variables that influence the “m” value; these physiographic characteristics may alter the water and energy balance of the watershed affecting the water yield. A relationship between watershed’s hydrological response and resilience was proposed to identify critical areas for the stability of water yield in the watersheds, providing a guide for public policy and suggesting ways to help the management of water resources in watersheds.

Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence Analysis.

An event camera is a neuromimetic sensor inspired by the human retinal imaging principle, which has the advantages of high dynamic range, high temporal resolution, and low power consumption. Due to the interference of hardware and software and other factors, the event stream output from the event camera usually contains a large amount of noise, and traditional denoising algorithms cannot be applied to the event stream. To better deal with different kinds of noise and enhance the robustness of the denoising algorithm, based on the spatio-temporal distribution characteristics of effective events and noise, an event stream noise reduction and visualization algorithm is proposed. The event stream enters fine filtering after filtering the BA noise based on spatio-temporal density. The fine filtering performs time sequence analysis on the event pixels and the neighboring pixels to filter out hot noise. The proposed visualization algorithm adaptively overlaps the events of the previous frame according to the event density difference to obtain clear and coherent event frames. We conducted denoising and visualization experiments on real scenes and public datasets, respectively, and the experiments show that our algorithm is effective in filtering noise and obtaining clear and coherent event frames under different event stream densities and noise backgrounds.

Evaluating habitat use and relative abundance of Iowa's river otter with harvest data

AbstractThe North American river otter (Lontra canadensis) was extirpated from much of the United States in the early 20th century due to habitat loss, pollution of waterways, and overharvesting. The Iowa Department of Natural Resources began a river otter reintroduction effort in 1985, which placed otters in 14 sites across the state. Otters have since been known to occur in every county in Iowa and appear to have successfully repopulated their former range throughout the state. Our objective was to relate land cover characteristics and otter abundance using harvest data. We used data collected by agency staff to map the locations of otter harvest in Iowa from 2006 to 2016. We mapped otter harvest locations at the subwatershed level (also called 12‐digit Hydrologic Unit Code or HUC‐12). We related otter harvest to land cover variables and predicted otter abundance by land cover type. We found that roads, forests, larger waterways, and Ictaluridae (catfish) presence were negatively correlated with otter harvest. Variables positively correlated with otter harvest were areas with greater land cover diversity, wetland patch density, average stream density, and waterway and wetland areas. The land cover model predicted otters in equal or greater numbers than the harvest data in 62.8% of HUC‐12s. The areas of greatest otter abundance estimates were located near recreation areas and urban areas, indicating the underutilization of these heavy‐trafficked areas by trappers. Areas of fewer predicted otters were not concentrated in a single area of the state but occurred along the Interstate 80 corridor.

Investigation in the bag breakup of water droplet in airflow with different temperatures and densities

This paper tries to clarify the respective significance of thermal effects and airflow density on the influence of bag breakup of water droplets in hot airflow. The bag breakup processes of water droplets in the airflow temperature range of 293 K (ρg=1.205 kg/m3) to 493 K (ρg=0.716 kg/m3) were investigated. Meanwhile, room-temperature gas mixtures with corresponding densities were prepared using room-temperature helium blended with room-temperature air to explore the bag breakup processes of water droplets. The results show that although the critical conventional Weber number (Wecon) of spherical droplets for bag breakup varies significantly in both streams, the critical initiation Weber number (Weini) of disk-shaped droplets for bag breakup remains almost consistent. Different deformation processes from Wecon to Weini result in different Wecon. In the gas mixtures with room temperature, the critical Wecon for bag breakup increases from 9.47 to 12.12 as the stream density decreases from 1.205 to 0.716 kg/m3. However, the critical Wecon for bag breakup decreases from 9.47 to 7.80 in the hot airflow at identical airflow density variations.

Assessing flash flood erosion following storm Daniel in Libya

The eastern Mediterranean basin is witnessing increased storm activity impacting populous urban coastal areas that historically were not prone to catastrophic flooding. In the fall of 2023, Storm Daniel struck the eastern coast of Libya, causing unprecedented flash floods with a tragic death toll and large-scale infrastructure damages. We use Sentinel-1A C-band SAR images to characterize the resulting flash flood erosion and sediment load dynamics across the watersheds and to map damages within coastal cities at their outlets. Our results suggest that sediment loading, resulting from surface erosion, increased the density of turbid streams. The above exacerbated the catastrophic impact of the flash floods in the coastal cities of Derna and Susah, where 66% and 48% of their respective urban surface have experienced moderate-to-high damages. Our findings highlight the increased vulnerability of coastal watersheds in arid areas within the eastern Mediterranean basin due to the forecasted increase in hydroclimatic extremes and call for a transformative coastal management approach to urgently implement nature-based solutions and land-use changes to mitigate these rising risks.

A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan

The study examined three machine learning algorithms (MLAs): random forest (RF), support vector machine (SVM), and artificial neural networks (ANN) for generating flood susceptibility maps in two watersheds in Jordan. Both were selected because they represent two climatic regimes: desert and mountainous areas. Because of a shortage of past floods location, a physical model was utilized to generate them based on simulations of 100-year rainfall. 10,000 of them were selected randomly and used for MLAs training and testing. During training, thirteen flood influential factors were identified. Out of them, the distance to stream, elevation, and topographic wetness index have shown an overwhelming effect in Zarqa Ma’in watershed (they gained 50% of IGR), while the distance to stream, stream density, and elevation have an overwhelming effect in Al-Buaida watershed (they gained 44% of IGR). For flood susceptibility mapping, RF outperformed the other two algorithms for both watersheds and was thus selected for susceptibility mapping. The maps were classified into five classes, and 11% of Al-Buaida watershed fell into high to very high classes, while 5.2% of Zarqa Ma’in watershed fell within these classes. In conclusion, MLAs were able to produce susceptibility maps efficiently, and they can form an alternative to physical modeling.

Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) > XGBoost-GWO (AUC: 0.961) > SVR-GWO (AUC: 0.960) > RNN (AUC: 0.956) > XGBoost (AUC: 0.953) > SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors.

Multi-Parameter Approach to Determine the Floods Causes in North Luwu, South Sulawesi.

Abstract Floods occur annually in North Luwu, South Sulawesi Province, Indonesia. The exact cause of the flooding is yet to be known; thus, this study aimed to find the main factors causing floods in North Luwu using a multi-parameter approach. The methods applied are hydrological, flood susceptibility, and land use changes in the analysis. The return period and intensity duration frequency of rain (1983-2021) was used for hydrological analysis. Flood susceptibility is obtained from the processing of slope, elevation, rainfall, stream density, soil type, and land use parameters. Land use change was analyzed using the Climate Change Initiative and Land Cover (CCI LC) data in 2000 and 2015. The results of the 1-year rainfall return period in North Luwu were relatively high, namely 83 mm/day, the 2-year return period of 119 mm/day, 5 years of 142 mm/day, 10 years of 155 mm/day, and 100 years 184 mm/day. The results of the IDF analysis obtained that the early rainy hours showed an intensity of 41-46 mm/hour with a total of 259 mm/day, which was considered extreme. There is unity between the results of the correlation of flood events in 2021 with recorded rainfall of 64-153 mm/day. Flood susceptibility analysis shows the dominant high vulnerability in the downstream area of 101,337 Ha, medium vulnerability (208,545 Ha) in the middle stream, and low vulnerability of 57,719 Ha upstream. The high flood susceptibility map results followed the flood events distribution from 2017-2021, while there is a lack of change in the land use analysis.

Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53km-2, 0.13km-2, 0.25km-2, 0.30km-2, and 0.28km-2 and drainage densities of 0.63km-1, 0.16km-1, 0.29km-1, 0.35km-1, and 0.33km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

Spatial landslide risk assessment in a highly populated Rohingya refugee settlement area of Cox’s Bazar, Bangladesh

ABSTRACT In recent times, landslides have become a major concern in the southeast part of Bangladesh. This study aims to develop comprehensive landslide risk mapping by applying the analytical hierarchy process (AHP) and geospatial techniques in Ukhiya and Teknaf Upazilas, a highly populated Rohingya Refugee Settlement area. To assess the landslide risk, we selected 12 influencing criteria of hazard, vulnerability and exposure under the relevant components of risk, such as precipitation intensity, landslide inventory, distance to fault line, stream density, distance to stream network, elevation, aspect, slope, geology, normalized difference vegetation index (NDVI), land use and land cover (LULC), and population density. These spatial criteria were weighted using the AHP model. The weighted overlay techniques were then used to produce the landslide risk map. The findings demonstrate that 2.19% of the total area is classified as a very high risk zone, and 12.74% is categorized as a high risk zone. Moderate risk areas cover 23.08% of the total area. The risk map is validated by the landslides inventory. The outcomes can be used by the concerned authorities to take the necessary steps to reduce the impact of landslides.

Asymptotic results for recursive multivariate associated-kernel estimators of the probability density mass function of a data stream

. In this article, our central focus is to investigate the non parametric estimator of the probability density or mass function of a data stream by using the general family of kernels, also called multivariate associated kernels. Being able to estimate categorial, count, discrete, and (semi)continuous distributions, these multivariate associated kernel estimators unify several particular cases. Within this framework, we first introduce a recursive estimator for non classical associated kernels. Under reasonable assumptions, we subsequently exhibit their asymptotic results, such as the local uniform L q -consistency, the pointwise asymptotic normality and finally the strong global consistency. Illustrative examples of associated kernels are revisited.

Interaction of drought-influencing factors for drought mitigation strategies in Lam Ta Kong Watershed, Khorat Plateau

Generally, drought is influenced by both spatial characteristics and anthropogenic activities within an area. Drought vulnerability assessment is a critical tool that can be effectively used to develop proper drought mitigation strategies to prevent avoidable losses. To develop suitable drought mitigation strategies, the overall drought vulnerability must be assessed, and the interaction among drought-influencing factors in the area should be considered. Consequently, this study aimed to investigate the interactions among critical drought-influencing factors and drought vulnerability in the Lam Ta Kong Watershed via spatial analysis with the analytical hierarchy process (AHP) and geographical information system (GIS) technology. Ten drought-influencing factors were considered in the vulnerability assessment: slope, elevation, soil texture, soil fertility, stream density, precipitation, temperature, precipitation days, evaporation, and land use. The results indicated that the critical drought-influencing factors were precipitation, precipitation days, and land use, resulting in most of the watershed experiencing high drought vulnerability (35.1% of the watershed or 1810.83 km2). Moreover, this research highlighted the interactions among the critical drought-influencing factors. Precipitation interacted with precipitation days to cause drought vulnerability across the watershed, with a p-value <0.05. Similarly, the interactions between precipitation and land use and between precipitation days and land use, with p-values <0.05, showed that they were associated with and influenced by drought in the Lam Ta Kong Watershed. This study further indicated that appropriate drought mitigation strategies for this watershed must consider the interactions among these drought-influencing factors, as well as their specific interactions across the watershed.

Unveiling novel APLIS model for identifying groundwater recharge zones in semi-arid regions: A case from Lebanon

The Bekaa Plain in Lebanon, is a major agricultural region known for its fertile soil and extensive crops cultivation using groundwater resources which is recently became under stress due to population growth, urbanization, and climate change. The main aim of this study is to investigate the rate of groundwater recharge (GwR) into this region using APLIS Model which principally depends on altitude, slope, lithology, infiltration, and soil type. Using GIS-based for a multicriteria mapping approach, in this study additional factors were added to the model, by incorporating rainfall rates, stream density, and fracture density. Results revealed various recharge potential area in the Bekaa Plain, with 52 % classified as high to very high GwR zones, equivalent to approximately 42.5 % of precipitated water, which is a testament to the exceptional capacity of the region to replenish groundwater aquifers. The geographic distribution of GwR categories primarily depends on altitude and lithological characteristics. The exceptional GwR potential of the Bekaa Plain holds immense promise for mitigating water scarcity, fostering agricultural productivity, and ensuring the socioeconomic well-being of communities reliant on groundwater resources.

Influence of the laser-beam intensity distribution on the performance of directed energy deposition of an axially fed metal powder

This paper investigates the influence of laser-beam intensity distribution (LBID) on the performance of the annular laser-beam directed energy deposition (DED) process with axial powder delivery. Three different LBIDs: Gaussian-like (G-LBID), top-hat-like (TH-LBID) and ring (R-LBID) at two LBID diameters were used. The process performance was characterised qualitatively in terms of the melt-pool shape and the process stability and quantitatively by powder-catchment efficiency, selected geometrical and metallurgical properties of the clad. The observed influence of LBID on process performance, as determined by the relationship between LBID and powder stream density distribution (PSDD), decreased with increasing mean surface-energy density and was more significant at larger LBID diameter. The highest powder-catchment efficiencies (90% and 87%) were achieved with the G-LBID and TH-LBID, whose high-intensity centre is aligned with the peak of the Gaussian-like PSDD. A lower powder-catchment efficiency of 77% was achieved with the R-LBID, whose high-intensity region is located at the edge of the melt pool with minimum powder density. However, this also results in the highest and most uniform dilution, the highest metallurgical bond ratio and the lowest lack of fusion porosity at the clad-substrate interface. In addition, the process was stable at the lower values of mean surface-energy density with R-LBID, while balling instability was observed with G-LBID and TH-LBID. It can be concluded that the use of R-LBID at lower values of mean surface-energy density improves the performance of the DED process with axial powder feed in terms of process stability and metallurgical properties of the clad.

Flash flood susceptibility mapping of north-east depression of Bangladesh using different GIS based bivariate statistical models

Flash flood causes severe damage to the environment and human life across the world, no exception is Bangladesh. Severe flash floods affect the northeastern portion of Bangladesh in the early monsoon and pose a serious threat to every aspect of socioeconomic development and environmental sustainability. To manage the threat and reduce flood loss, the map of flash flood susceptible zones plays a key role. Thus, the aim of this research is to map the flash flood-susceptible areas of the northeastern haor areas of Bangladesh utilizing GIS-based bivariate statistical models. The models utilized are frequency ratio (FR), weights of evidence (WoE), certainty factor (CF), Shanon’s entropy (SE) and information value (IV). Among the 250 identified flash flood locations, 80 % data was used for training purposes and 20 % data for testing purposes. Eleven selected conditioning factors of flash flood include elevation, slope, aspect, curvature, TWI, TRI, SPI, distance to stream, stream density, rainfall and physiography. The calculated weights are assigned to the conditioning factors using ArcGIS environment to prepare the final flash flood maps. Results of AUC of ROC indicate WoE (success rate = 0.833 and prediction rate = 0.925) is the best model for flash flood susceptibility mapping followed by FR (success rate = 0.828 and prediction rate = 0.928) and SE (success rate = 0.827 and prediction rate = 0.923). According to the models, topographic (flat area) and hydrologic factors significantly control flash flood occurrence in the study area. The prepared flash flood susceptibility maps will be helpful for disaster managers and haor master planners of the study area.

Continuous manufacturing of pharmaceutical products: A density-insensitive near infrared method for the in-line monitoring of continuous powder streams

Near infrared (NIR) spectroscopy is a valuable analytical technique for monitoring chemical composition of powder blends in continuous pharmaceutical processes. However, the variation in density captured by NIR during spectral collection of dynamic powder streams at different flow rates often reduces the performance and robustness of NIR models. To overcome this challenge, quantitative NIR measurements are commonly collected across all potential manufacturing conditions, including multiple flow rates to account for the physical variations. The utility of this approach is limited by the considerable quantity of resources required to run and analyze an extensive calibration design at variable flow rates in a continuous manufacturing (CM) process. It is hypothesized that the primary variation introduced to NIR spectra from changing flow rates is a change in the density of the powder from which NIR spectra are collected. In this work, powder stream density was used as an efficient surrogate for flow rate in developing a quantitative NIR method with enhanced robustness against process rate variation. A density design space of two process parameters was generated to determine the conditions required to encompass the apparent density and spectral variance from increases in process rate. This apparent density variance was included in calibration at a constant low flow rate to enable the development of a density-insensitive NIR quantitative model with limited consumption of materials. The density-insensitive NIR model demonstrated comparable prediction performance and flow rate robustness to a traditional NIR model including flow rate variation (“gold standard” model) when applied to monitoring drug content in continuous runs at varying flow rates. The proposed platform for the development of in-line density-insensitive NIR methods is expected to facilitate robust analytical model performance across variable continuous manufacturing production scales while improving the material efficiency over traditional robust modeling approaches for calibration development.