Calibration Information Research Articles

A time-calibrated phylogeny of the diversification of Holoadeninae frogs.

The phylogeny of the major lineages of Amphibia has received significant attention in recent years, although evolutionary relationships within families remain largely neglected. One such overlooked group is the subfamily Holoadeninae, comprising 73 species across nine genera and characterized by a disjunct geographical distribution. The lack of a fossil record for this subfamily hampers the formulation of a comprehensive evolutionary hypothesis for their diversification. Aiming to fill this gap, we inferred the phylogenetic relationships and divergence times for Holoadeninae using molecular data and calibration information derived from the fossil record of Neobatrachia. Our inferred phylogeny confirmed most genus-level associations, and molecular dating analysis placed the origin of Holoadeninae in the Eocene, with subsequent splits also occurring during this period. The climatic and geological events that occurred during the Oligocene-Miocene transition were crucial to the dynamic biogeographical history of the subfamily. However, the wide highest posterior density intervals in our divergence time estimates are primarily attributed to the absence of Holoadeninae fossil information and, secondarily, to the limited number of sampled nucleotide sites.

Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock.

Relaxing the molecular clock using models of how substitution rates change across lineages has become essential for addressing evolutionary problems. The diversity of rate evolution models and their implementations are substantial, and studies have demonstrated their impact on divergence time estimates can be as significant as that of calibration information. In this review, we trace the development of rate evolution models from the proposal of the molecular clock concept to the development of sophisticated Bayesian and non-Bayesian methods that handle rate variation in phylogenies. We discuss the various approaches to modeling rate evolution, provide a comprehensive list of available software, and examine the challenges and advancements of the prevalent Bayesian framework, contrasting them to faster non-Bayesian methods. Lastly, we offer insights into potential advancements in the field in the era of big data.

Design and implementation of an encoder calibration system for improved resolution and accuracy in IPMSM drive with embedded software

ABSTRACT This paper presents an encoder calibration system to enhance resolution and accuracy in an interior permanent magnet synchronous motor (IPMSM) drive, along with custom embedded software. The system implements closed-loop control with the IPMSM drive, adjusting the encoder resolution through gear ratio adjustments. Communication between the human-machine interface (HMI) and the DSP’s internal 28× CPU is achieved via digital communication RS-422 interface. The embedded software on the 28× CPU processes real-time data and displays calibration information on the HMI. Firmware integration includes external analog/digital converters, comparators, digital communication interfaces, high-accuracy encoders, original encoders, and additional gear ratio adjustments, with computed compensation values stored in EEPROM. The TMS-320F-28335 digital signal processor serves as the control core, enabling encoder signal processing, embedded software execution, HMI operation, and IPMSM drive control. The experimental results validate the practicality of the proposed digitization method, demonstrating its effective applicability in industrial control systems.

Model-free visual servoing based on active disturbance rejection control and adaptive estimator for robotic manipulation without calibration

PurposeVisual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.Design/methodology/approachThis paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.FindingsThe various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.Originality/valueCompared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

Accurate Pointing Determination of Space Targets under the Stars Background

Abstract. This study proposes a method for determining the direction of space targets using existing satellite constellations. Usually, when optical satellites point towards deep space for imaging, stars inevitably appear in the image. This study conducts all targets extraction, star identification, camera calibration. Ultimately obtaining high-precision space targets longitude and latitude information. One of the Jilin-1 GF06A series satellite was used to conduct observation experiments on space targets of this study. Each scene has an exposure time of 10 seconds, and a total of 90 seconds of observation data was obtained. This study analysed the results of star extraction and the identification result which indicate that GF06A can capture a maximum magnitude of 15.6. After using the extraction star coordinate information for calibration, the interior accuracy of camera was better than 0.48 arcseconds. Then, the precise longitude and latitude information of 5 space targets were obtained. Finally, cataloguing information of these stars is provided, which is beneficial for determining the orbit of the target in the future.

Wall-Proximity Effects on Five-Hole Probe Measurements

Wall proximity affects the accuracy of pressure probe measurements with a particularly strong impact on multi-hole probes. The wall-related evolution of the calibration of two hemispheric L-shaped 3D-printed five-hole probes was investigated in a low-speed wind tunnel. Pressure measurements and 2D particle image velocimetry were performed. The wall proximity causes the probe to measure a flow diverging from the wall, whereas the boundary layer causes the probe to measure a velocity directed towards the wall. Both angular calibration coefficients are affected in different manners. The error in angle measurement can reach 7°. These errors can be treated as calibration information. Acceleration caused by blockage is not the main reason for the errors. Methods to perform measurements closer to the wall are suggested.

Satellite-derived equilibrium shoreline modelling at a high-energy meso-macrotidal beach

Modelling and predicting the future of sandy shorelines is a key challenge in coastal research and is critical for sustainable coastal management. However, currently the most skillful shoreline models strongly rely on data to calibrate the free parameters, and are thus restricted to a few well monitored sites in the world. Here we address the challenges and opportunities offered by optical satellite imagery to provide useful information for equilibrium shoreline model calibration on cross-shore transport dominated sites. We focus on Truc Vert beach, southwest France, where previous work showed good equilibrium model skill to reproduce shoreline change from the time scales of hours (storms) to decades. Satellite derived waterlines are extracted over 11 years (2009–2020) and further transformed into satellite derived shorelines (SDS) with different water level corrections (e.g. tide and/or run up) and varying alongshore averaging lengths, and thus different uncertainties, in order to test model performance. Successively the timeseries duration and sampling frequency required for model calibration were also investigated. The model calibrated using the SDS data showed similar skill as the model calibrated using in-situ alongshore averaged shoreline positions, even for the uncorrected SDS dataset which Root Mean Square Error (RMSE) are approximately 30 m. Alongshore averaging was found to be the only necessary processing of the SDS data while any other site-specific corrections did not significantly improve model skill. Finally to further investigate the effect of sampling frequency and noise in the dataset we performed an analysis using a synthetic shoreline. Our results suggest that the effect of noise is negligible as long as the sampling frequency remains high (dt ≤ 30 days). Pending further validation, results show the strong potential of using uncorrected SDS dataset for shoreline model calibration at cross-shore transport dominated sandy coasts.

The Digitization of Photographic Spectra in the Dominion Astrophysical Observatory Plate Collection with Commercial Scanners: A Pilot Study

Commercial flatbed scanners have the potential to deliver a quick and efficient means of capturing the scientific content of spectra recorded on photographic plates. We discuss the digitization of selected spectra in the Dominion Astrophysical Observatory (DAO) photographic plate collection with commercial scanners. In this pilot study, emphasis is placed on assessing if the information on the plates can be recovered using Epson V800 and 12000XL scanners; the more complicated issues associated with the shortcomings of photographic materials, such as correcting for nonlinearity, are deferred to a future study. Spectra of Vega (α Lyr) that were recorded over ∼4 decades with the DAO 1.8 m telescope are examined. These spectra sample a range of photographic emulsions, plate preparation techniques, calibration information, observing techniques, and spectrograph configuration. A scanning density of 2400 elements per inch recovers information in the spectra. Differences in the modulation transfer function (MTF) of the two scanners are found, with the Epson 12000XL having a superior MTF. Comparisons with a CCD spectrum of Vega confirm that moderately weak features are faithfully recovered in photographic spectra that have been digitized with the 12000XL scanner. The importance of scanning the full plate to cover the light profile of the target and calibration information is emphasized. Lessons learned from these experiments are also presented.

The value of instream stable water isotope and nitrate concentration data for calibrating a travel time‐based water quality model

AbstractTransit time‐based water quality models using StorAge Selection (SAS) functions are crucial for nitrate (NO3−) management. However, relying solely on instream NO3− concentration for model calibration can result in poor parameter identifiability. This is due to the interaction, or correlation, between transport parameters, such as SAS function parameters, and denitrification rate, which challenges accurate parameters identification and description of catchment‐scale hydrological processes. To tackle this issue, we conducted three Monte‐Carlo experiments for a German mesoscale catchment by calibrating a SAS‐based model with daily instream NO3− concentrations (Experiment 1), monthly instream stable water isotopes (e.g. δ18O) (Experiment 2) and both datasets (Experiment 3). Our findings revealed comparable ranges of SAS transport parameters and median water transit times (TT50) across the experiments. This suggests that, despite their distinct reactive or conservative nature, and sampling strategies, the NO3− and δ18O time series offer similar information for calibration. However, the absolute values of transport parameters and TT50 time series, as well as the degree of parameter interaction differed. Experiment 1 showed greater interaction between certain transport parameters and denitrification rate, leading to greater equifinality. Conversely, Experiment 3 yielded reduced parameters interaction, which enhanced transport parameters identifiability and decreased uncertainty in TT50 time series. Hence, even a modest effort to incorporate only monthly δ18O values in model calibration for highly frequent NO3−, improved the description of hydrological transport. This study showcased the value of combining NO3− and δ18O model results to improve transport parameter identifiability and model robustness, which ultimately enhances NO3− management strategies.

Target location method of intelligent deicing robot based on nonlinear auto disturbance rejection neural network

A visual navigation scheme for intermittent walking deicing robots was designed to address the issues of multiple iterations, long computation time, and poor real-time performance in the nonlinear optimization of the original SLAM system. A computer image acquisition unit, a computer image processing module, and a visual navigation parameter extraction algorithm were also designed. Implemented a visual navigation system for deicing robots based on image processing. It can meet the navigation requirements of the deicing robot. Simulation and experiments have shown that the method proposed in this paper can quickly and accurately identify abnormal point clouds. The visual servo control scheme constructed in this paper is aimed at robot task operations with unknown system calibration and target depth information. By calibrating the robot vision system, the conversion between camera pixel coordinates and robot base coordinates is achieved, with a transmission frame rate of 53.65 per second; The maximum error in positioning accuracy in space is 10.6 mm. The feature trajectory of visual space is smooth and stable within the camera's field of view, and the end movement of Cartesian space robots is stable without rebound, resulting in high grasping and positioning accuracy.

A Method for Analyzing the Operating Data of Electric Energy Meters Based on Data Mining Analysis

Aiming at the problem of error estimation of smart meters in distribution network, a method of error estimation of smart meters based on particle swarm optimization convolutional neural network is proposed. This method establishes an intelligent energy meter error estimation model through data collection, data prediction, and preprocessing. To address the convergence issue in training, the interlayer distribution of weights is adjusted to improve training quality. This method fully utilizes template calibration information to transform indicator detection under complex conditions into simple and effective isometric segmentation, transforming label recognition from complex text detection and recognition tasks to simple and efficient binary detection tasks, with better robustness. The effectiveness and high robustness of the proposed method have been demonstrated through experimental verification.

A review on personal calibration issues for video-oculographic-based gaze tracking.

Personal calibration is a process of obtaining personal gaze-related information by focusing on some calibration benchmarks when the user initially uses a gaze tracking system. It not only provides conditions for gaze estimation, but also improves gaze tracking performance. Existing eye-tracking products often require users to conduct explicit personal calibration first, thereby tracking and interacting based on their gaze. This calibration mode has certain limitations, and there is still a significant gap between theoretical personal calibration methods and their practicality. Therefore, this paper reviews the issues of personal calibration for video-oculographic-based gaze tracking. The personal calibration information in typical gaze tracking methods is first summarized, and then some main settings in existing personal calibration processes are analyzed. Several personal calibration modes are discussed and compared subsequently. The performance of typical personal calibration methods for 2D and 3D gaze tracking is quantitatively compared through simulation experiments, highlighting the characteristics of different personal calibration settings. On this basis, we discuss several key issues in designing personal calibration. To the best of our knowledge, this is the first review on personal calibration issues for video-oculographic-based gaze tracking. It aims to provide a comprehensive overview of the research status of personal calibration, explore its main directions for further studies, and provide guidance for seeking personal calibration modes that conform to natural human-computer interaction and promoting the widespread application of eye-movement interaction.

An enhanced state-aware model learning approach for security analysis in lightweight protocol implementations

Owing to the emergence and rapid advances of new-generation information and digitalization technologies, the concept of model-driven digital twin has received widespread attentions and is developing vigorously. Driven by data and simulators, the digital twin can create the virtual twins of physical objects to perform monitoring, simulation, prediction, optimization, and so on. Hence, the application of digital twin can increase efficiency and security of systems by providing reliable model and decision supports. In this paper, we propose a state-aware model learning method to simulate and analyze the lightweight protocol implementations in edge/cloud environments. We introduce the data flow of program execution and network interaction inputs/outputs (I/O) into the extended finite state machine (EFSM) to expand the modeling scope and insight. We aim to calibrate the states and construct an accurate state-machine model using a digital twin based layered approach to reasonably reflect the correlation of a device’s external behavior and internal data. This, in turn, improves our ability to verify the logic and evaluate the security for protocol implementations. This method firstly involves instrumenting the target device to monitor variable activity during its execution. We then employ learning algorithms to produce multiple rounds of message queries. Both the I/O data corresponding to these query sequences and the state calibration information derived from filtered memory variables are obtained through the mapper and execution monitor, respectively. These two aspects of information are combined to dynamically and incrementally construct the protocol’s state machine. We apply this method to develop SALearn and evaluate the effectiveness of SALearn on two lightweight protocol implementations. Our experimental results indicate that SALearn outperforms existing protocol model learning tools, achieving higher learning efficiency and uncovering more interesting states and security issues. In total, we identified two violation scenarios of rekey logic. These situations also reflect the differences in details between different implementations.

AVID: A speech database for machine learning studies on vocal intensity

Vocal intensity, which is quantified typically with the sound pressure level (SPL), is a key feature of speech. To measure SPL from speech recordings, a standard calibration tone (with a reference SPL of 94 dB or 114 dB) needs to be recorded together with speech. However, most of the popular databases that are used in areas such as speech and speaker recognition have been recorded without calibration information by expressing speech on arbitrary amplitude scales. Therefore, information about vocal intensity of the recorded speech, including SPL, is lost. In the current study, we introduce a new open and calibrated speech/electroglottography (EGG) database named Aalto Vocal Intensity Database (AVID). AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labelled sentence-wise using a total of 19 labels that support the utilisation of the data in machine learning (ML) -based studies of vocal intensity based on supervised learning. In order to demonstrate how the AVID data can be used to study vocal intensity, we investigated one multi-class classification task (classification of speech into soft, normal, loud and very loud intensity classes) and one regression task (prediction of SPL of speech). In both tasks, we deliberately warped the level of the input speech by normalising the signal to have its maximum amplitude equal to 1.0, that is, we simulated a scenario that is prevalent in current speech databases. The results show that using the spectrogram feature with the support vector machine classifier gave an accuracy of 82% in the multi-class classification of the vocal intensity category. In the prediction of SPL, using the spectrogram feature with the support vector regressor gave an mean absolute error of about 2 dB and a coefficient of determination of 92%. We welcome researchers interested in classification and regression problems to utilise AVID in the study of vocal intensity, and we hope that the current results could serve as baselines for future ML studies on the topic.

Global sensitivity analysis and uncertainty quantification for a mathematical model of dry anaerobic digestion in plug-flow reactors

<p>In many applications, complex biological phenomena can be reproduced via structured mathematical models, which depend on numerous biotic and abiotic input parameters, whose effect on model outputs can be of paramount importance. The calibration of model parameters is crucial to obtain the best fit between simulated and experimental data. Sensitivity analysis and uncertainty quantification constitute essential tools in the field of biological systems modeling. Despite the significant number of applications of sensitivity analysis in wet anaerobic digestion, there are no examples of global sensitivity analysis for mathematical models describing the dry anaerobic digestion in plug-flow reactors. For the first time, the present study explores the global sensitivity analysis and uncertainty quantification for a plug-flow reactor model. The investigated model accounts for the mass$ / $volume variation that takes place in these systems as a result of solid waste conversion in gaseous value-added compounds. A preliminary screening based on the Morris' method allowed for the definition of three different groups of parameters. A surrogate model was constructed to investigate the relation between input and output parameters without running demanding simulations from scratch. The obtained Sobol' indices allowed to perform the quantitative global sensitivity analysis. Finally, the uncertainty quantification results led to the definition of the probability density function related to the investigated quantity of interest. The study showed that the net methane production is mostly sensitive to the values of the conversion parameter related to the particulate biodegradable volatile solids in acetic acid $ k_1 $ and to the kinetic parameter describing the acetic acid uptake $ k_2 $. The application of these techniques led to helpful information for model calibration and validation.</p>

Sharing calibration information among laser-induced breakdown spectroscopy instruments using spectral line binning and calibration transfer

The ability of a publicly accessible preexisting laser-induced breakdown spectroscopy (LIBS) database to train multivariate models to predict rock and mineral compositions from other laboratories or from remotely collected spectra is evaluated using LIBS spectra collected on >2500 unique geological targets using three different instruments and a range of collection protocols. Datasets collected under increasingly disparate conditions are utilized, including a single instrument with different resolution settings; two different instruments with very similar ablation and collection optics; and a benchtop instrument and portable instrument with different collection protocols, resolutions, and plasma conditions. Cross-calibration among datasets is performed for a range of scenarios designed to test the efficacy of post-processing techniques.Major element predictions are most accurate when instrument parameters match among training and test spectra. Use of a piecewise direct standardization-partial least squares (PDS-PLS) calibration transfer algorithm reduces major element prediction uncertainties when the resolution of training and test spectra do not match. Even when training and test spectra are collected on different instruments, reasonable predictions can be derived by binning peak areas prior to training calibration models. Finally, incorporating a large, preexisting database into a smaller dataset collected under test conditions has the potential to greatly improve the reliability of predicted compositions. With careful consideration to match plasma conditions and utilize post-processing, existing large-scale LIBS calibration databases like the one used in this study can be used to boost LIBS accuracy and expand the potential of LIBS as a widely-applicable quantitative geochemical tool.

PENGARUH EVALUASI PENGGUNA APLIKASI SIMPEL TERHADAP KINERJA LAYANAN MENGGUNAKAN METODE REGRESI LINEAR BERGANDA

Information Systems have become a crucial component in the digital era of today's world. The use of information systems is widely employed in government agencies to facilitate work processes and provide the best services. The Directorate of Metrology, in its effort to enhance services, has developed a new information system called the Legal Metrology Calibration and Recalibration Management Information System (SIMPEL). However, in its implementation at the Integrated Trade Services Unit IV (UPTP IV), numerous challenges have been identified that result in a decrease in Non-TaX State Revenue and an increase in the number of complaints. PNBP serves as a performance indicator for UPTP IV services. Therefore, an evaluation study of SIMPEL is needed to increase the PNBP figures. Based on several references, the variables of effectiveness and technical capability are considered for evaluating SIMPEL. The research methodology employed is the multiple linear regression method. The research findings indicate that partially, from the customer perspective, the evaluation of effectiveness significantly affects the performance of UPTP IV services. Partially, from the employee perspective, the evaluation of technical capability significantly influences the performance of UPTP IV services. Simultaneously, the evaluation of effectiveness and technical capability significantly affects the performance of UPTP IV services.

Longwall Mining Automation—The Shearer Positioning Methods between the Longwall Automation Steering Committee and China University of Mining and Technology

The shearer positioning method is of great significance to the automation of longwall mining. The research teams in the Longwall Automation Steering Committee (LASC) of Australia and China University of Mining and Technology (CUMT) have focused on shearer positioning and identified the shearer inertial navigation system, the measurement of longwall retreat and creep displacement, and the backward calibration of the shearer trajectory as three key technologies to obtain accurate shearer positioning information. In underground environments without GPS, due to the characteristics of inertial navigation system (INS) autonomous full-parameter navigation, shearer positioning based on INS is adopted by the LASC and CUMT, and error reduction algorithms are proposed to inhibit the rapid error accumulation of INS. In order to obtain the periodic calibration information when the shearer reaches the end of the longwall face, it is necessary to measure the retreat and creep displacements in order to back-correct the shearer trajectory. Finding a suitable measurement method for this task is challenging, especially in the presence of dust and moisture. The LASC used a scanning laser and FMR 250 microwave radar to measure these two displacements, while CUMT adopted an ultra-wideband (UWB) radar. In terms of the backward calibration method, minimum-variance fixed-interval smoothing (MFS) proposed by LASC and the global optimization model (GOM) for the shearer trajectory from CUMT are described in detail. The experiment demonstrates that the GOM outperforms MFS in terms of accuracy but requires more computational resources. Therefore, our next research objective is to develop an efficient and accurate algorithm for performing backward calibration on the shearer trajectory.

Stellar variability in Gaia DR3

Context. The unparalleled characteristics of Gaia photometry in terms of calibration, stability, time span, dynamic range, full-sky coverage, and complementary information make it an excellent choice to study stellar variability. Aims. We aim to measure the photometric dispersion in the G, GBP, and GRP bands of the 145 677 450 third Gaia data release (DR3) five-parameter sources with G ≤ 17 mag and GBP – GRP between −1.0 and 8.0 mag. We will use that unbiased sample to analyze stellar variability in the Milky Way (MW), Large Magellanic Cloud (LMC), and Small Magellanic Cloud (SMC). Methods. For each band we convert from magnitude uncertainties to observed photometric dispersions, calculate the instrumental component as a function of apparent magnitude and color, and use it to transform the observed dispersions into the astrophysical ones: sG, SGBP, and SGRP. We give variability indices in the three bands for the whole sample indicating whether the objects are non-variable, marginally variable, or clearly so. We use the subsample established by Rimoldini and collaborators with light curves and variability types to calibrate our results and establish their limitations. Results. The position of an object in the dispersion-dispersion planes can be used to constrain its variability type, a direct application of these results. We use information from the MW, LMC, and SMC color-absolute magnitude diagrams (CAMDs) to discuss variability across the Hertzsprung-Russell diagram. White dwarfs and B-type subdwarfs are more variable than main sequence (MS) or red clump (RC) stars, with a flat distribution in sG up to 10 mmag and with variability decreasing for the former with age. The MS region in the Gaia CAMD includes a mixture of populations from the MS itself and from other evolutionary phases. Its sG distribution peaks at low values (~1–2 mmag) but it has a large tail dominated by eclipsing binaries, RR Lyrae stars, and young stellar objects. RC stars are characterized by little variability, with their sG distribution peaking at 1 mmag or less. The stars in the pre-main-sequence (PMS) region are highly variable, with a power law distribution in sG with slope 2.75 and a cutoff for values lower than 7 mmag. The luminous red stars region of the Gaia CAMD has the highest variability, with its extreme dominated by AGB stars and with a power law in sG with slope ~2.2 that extends from there to a cutoff of 7 mmag. We show that our method can be used to search for LMC Cepheids. We analyze four stellar clusters with O stars (Villafranca O-016, O-021, O-024, and O-026) and detect a strong difference in sG between stars that are already in the MS and those that are still in the PMS.

Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed

Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an alternative to the physically based Soil and Water Assessment Tool (SWAT) for predicting streamflow in the Rio Grande Headwaters near Del Norte, a snowmelt-dominated mountainous watershed of the Upper Rio Grande Basin. Remotely sensed data were used for the random forest machine learning analysis (RFML) and RStudio for data processing and synthesizing. The RFML model outperformed the SWAT model in accuracy and demonstrated its capability in predicting streamflow in this region. We implemented a customized approach to the RFR model to assess the model’s performance for three training periods, across 1991–2010, 1996–2010, and 2001–2010; the results indicated that the model’s accuracy improved with longer training periods, implying that the model trained on a more extended period is better able to capture the parameters’ variability and reproduce streamflow data more accurately. The variable importance (i.e., IncNodePurity) measure of the RFML model revealed that the snow depth and the minimum temperature were consistently the top two predictors across all training periods. The paper also evaluated how well the SWAT model performs in reproducing streamflow data of the watershed with a conventional approach. The SWAT model needed more time and data to set up and calibrate, delivering acceptable performance in annual mean streamflow simulation, with satisfactory index of agreement (d), coefficient of determination (R2), and percent bias (PBIAS) values, but monthly simulation warrants further exploration and model adjustments. The study recommends exploring snowmelt runoff hydrologic processes, dust-driven sublimation effects, and more detailed topographic input parameters to update the SWAT snowmelt routine for better monthly flow estimation. The results provide a critical analysis for enhancing streamflow prediction, which is valuable for further research and water resource management, including snowmelt-driven semi-arid regions.