IRI Model Research Articles

Construction of global IGS-3D electron density (Ne) model by deep learning

In this study, we construct a global IGS-3D Ne model that generates global 3-D electron density (Ne) from International Global Navigation Satellite Systems (GNSS) Service (IGS) total electron content (TEC) data through deep learning. As a first step towards this, we make a model to generate a vertical electron density profile from a TEC value using Multi-Layer Perceptron (MLP). In this process, we use the vertical electron density profiles and the corresponding TEC values of the IRI-2016 model from 2001 to 2008 for training, 2009 and 2014 for validation, and 2010 to 2013 for a test. The next step is to generate global IGS electron density profiles using the global IGS TECs as input data for the model, which is called the global IGS-3D Ne model. We evaluate the IGS-3D Ne model by comparing the electron density profiles from the incoherent scatter radars (ISRs) at three stations with the IGS-3D Ne model from 2010 to 2013. The evaluation shows that the electron density profiles from the IGS-3D Ne model are closer to the ISR data than those of the IRI model, especially at high latitudes. The IGS-3D Ne model shows that the averaged root mean square error (RMSE) values between IGS and ISR electron density profiles are 0.37 log(m−3), 0.22 log(m−3), and 0.34 log(m−3) for all test datasets at Jicamarca, Millstone Hill, and EISCAT stations, respectively. These results suggest that our method has sufficient potential to enhance the ability to predict global electron density profiles.

Speed and accuracy investigations of neural network algorithms for ionospheric modelling at an equatorial region

Neural networks are very efficient tools for modeling, including ionospheric modeling. The training algorithm is important for achieving the optimum performance of the trained network. This research is therefore meant to evaluate and compare the performances of ten neural network training algorithms based on their prediction accuracies, and the duration/times taken by each of the algorithms to establish the optimum result. The neural networks were trained using electron density measurements by Radio Occultation (RO) technique from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites. Data for the period 2006 through 2021 was used. The networks were trained using about 2.9 million data points collected from the Kano region, Nigeria (5-degree rectangular region around geographic: 12.00° N, 8.59° E) after performing data quality control. The training algorithms considered in the work include: Bayesian Regularization (BR); Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFG); Conjugate Gradient with Powell/Beale (CGB); Fletcher-Reeves Conjugate Gradient (CGF); Gradient descent with momentum and adaptive learning rate (GDX); Levenberg Marquardt (LM); One Step Secant (OSS); Polak-Ribiére Conjugate Gradient (CGP); Resilient Backpropagation (RP); and Scaled Conjugate Gradient (SCG). The results showed that the BR and the LM algorithms gave the best performances in minimizing the errors of prediction (the mean RMSEs are respectively 112 and 114 ×103 electrons/cm3), but the RP algorithm, which came third in terms of accuracy, was significantly faster than both the LM and BR algorithms. The worst-performing algorithm in terms of accuracy was the GDX algorithm, although it was the fastest algorithm. The BFG algorithm was the worst-performing algorithm in terms of a combination of speed and accuracy. The developed neural network model was validated using ionosonde electron density measurements obtained from Ilorin, Nigeria (geographic: 8.5° N, 4.5° E; geomagnetic: 1.8° S). A comparison of the neural network, the NeQuick, and the IRI model predictions relative to the ionosonde measurements indicate that the neural network model was the best-performing model; the NN model predictions minimized the mean absolute errors (MAEs) in ∼44% of 399 ionosonde profiles investigated, the IRI model did so in ∼32%, and the NeQuick did so in ∼24%. The MAEs of the NeQuick however exhibited the best (least) variance. In overall, the NN model gave the least (best) mean of the MAEs (∼73 × 103 cm−3), compared to ∼82 × 103 cm−3 given by both the NeQuick and the IRI models, further supporting the idea that neural networks are excellent for present-day ionospheric modeling.

Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density

The ionosphere is one of the important error sources that affect the communication of radio signals. The international reference ionosphere (IRI) model is a commonly used model to describe ionospheric parameters. The driving parameter IG12 of the IRI-2016 model was optimally updated based on GNSS-TEC data from 2015 and 2019. The electron density profiles and NmF2 calculated by the IRI-2016 model (upda-IRI-2016) driven by the updated IG12 value (IG-up) were evaluated for their accuracy using ionosonde observations and COSMIC inversion data. The experiments show that both the electron density profiles and NmF2 calculated by upda-IRI-2016 driven by IG-up show significant optimization effects, compared to the IRI-2016 model driven by IG12. For electron density, the precision improvement (PI) for both MAE and RMSE at the Beijing station exceed 31.2% in January 2015 and 16.0% in January 2019. While the PI of MAE and RMSE at the Wuhan station, which is located at a lower latitude, both exceed 32.5% in January 2015, both exceed 42.1% in January 2019, which is significantly higher than that of the Beijing station. In 2015, the PI of MAE and RMSE compared with COSMIC are both higher than 20%. For NmF2, the PI is greater for low solar activity years and low latitude stations, with the Wuhan station showing a PI of more than 11.7% in January 2019 compared to January 2015. The PI compared to COSMIC was higher than 17.2% in 2015.

High-resolution 3-D imaging of electron density perturbations using ultra-dense GNSS observation networks in Japan: an example of medium-scale traveling ionospheric disturbances

For the first time using computerized ionospheric tomography (CIT) and leveraging ultra-dense slant total electron content (STEC) measurements derived from two ground-based Global Navigation Satellite System (GNSS) receiver networks in Japan, we have reconstructed the 3-D field-aligned structure of nighttime medium-scale traveling ionospheric disturbances (MSTIDs) with high spatiotemporal resolution. The CIT algorithm focuses on electron density perturbation components, allowing for the imaging of disturbances with small amplitudes and scales. Slant TECs used for CIT are setup to consist of two components: the background derived from IRI-2016 model and TEC perturbations obtained by subtracting a 30-min running average from observations. The resolution is set to 0.25º in latitude and longitude, 10 km in altitude, 30 s in time. Simulations were conducted to assess the performance of the CIT algorithm, revealing that this technique has good fidelity by accurately reconstructing more than 80% of the electron density perturbations. The focus is on the nighttime event of July 4, 2022, when data were accessible. The reconstruction results show that the MSTIDs initially form at lower altitudes and subsequently develop to exhibit large amplitudes and scales that extend to higher altitudes, characterized by a well-defined frontal structure with electrodynamic signatures. These results are consistent with theories and snippets of observational evidence regarding electromagnetic-influenced MSTIDs, hence affirming the effectiveness of the developed CIT technique in probing of the variations in the 3-D structure of ionospheric electron density. This is expected to contribute to a compressive understanding of the underlying mechanisms of ionospheric inhomogeneities.Graphical

VALIDATION OF THE NEQUICK AND IRI MODELS BASED ON DATA FROM IONOSONDES

<p class="ABSTRACT">This study compares the NeQuick and IRI ionospheric models using data from ionosondes as reference. Validation based on data obtained by direct measurement using ionosondes helps to refine empirical models and improves their reliability. In this way, it is possible to identify the differences and limitations of the model and contribute to its improvement. As the NeQuick and IRI models provide tools for the study of ionospheric variability and forecasts of the ionospheric TEC coefficient, their versions are constantly being improved and need to be validated accordingly. Therefore, this research is based on the comparison of the TEC coefficient for the validation of two different ionosphere models based on data from three different regions of Europe.</p>

Effects of local time on the variations of the total electron contents at an American and Asian longitudes and their comparison with IRI-2016, IRI-Plas2017 and NeQuick-2 models during solar cycle 24

Ionospheric modelling is one of the major tools to study the behavior of the ionosphere. Ionospheric models have been useful in predicting the true state of the ionosphere particularly in regions where Global Positioning System (GPS) are not readily available. This research paper aims to study the longitudinal variations and the effects of local time on the total electron content (TEC) recorded in two different sectors (Asia and America) during the ascending, maximum and descending phases of solar cycle 24 (2011–2017) and also to compare its values to IRI-2016, IRI-Plas2017 and NeQuick-2 models in order to evaluate their performances. An hourly interval profile computed on seasonal basis were used to study the behaviors of TEC diurnally and seasonally. A monthly interval error profile plotted on annual basis was also used to investigate the deviations of the models from the GPS values. Our results showed that the peak values of TEC in the Asian and American sectors were recorded around the dawn,06:00UT (13:00LT) and dusk, 18:00UT (15:00LT) respectively. We also affirmed from our results that seasonal/winter anomalies were recorded in all the phases of the solar cycle in both sectors. Equinoctial Asymmetry was also observed to be predominant during different phases of the solar cycle in both sectors except during ascending and descending phases in the Asian and American sectors respectively. Out of the 168 months of data collated for this study, only 162 months of data were available. The IRI-2016, IRI-Plas2017 and NeQuick-2 models have 11.7%, 23.5% and 64.8% better performance in all the months under consideration. Therefore, the NeQuick-2 model had the best performance in both the Asian and American sectors. Finally, from the results of our statistical analysis, Mean Absolute Error (MAE) has ∼3 TECU lower than the Root Mean Square Error (RMSE) values in both sectors and in all the solar cycle phase. Hence, MAE can evaluate the performance of ionospheric models better than RMSE.

Forecasting ionospheric TEC using least squares support vector machine and moth-flame optimization methods in China

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models.

On the variations in equatorial and low-latitude GPS-TEC and assessment of NeQuick-2, IRI-2016 and IRI-2020 models in the African longitude during solar cycle 24–25

Ionospheric models play a crucial role in understanding, prediction, and mitigation of the effects of ionospheric variability on a wide range of technological and scientific applications relying on space-based services. Conversely, the models need to be routinely updated with newer datasets and specifications to account for the regional discrepancies in the changing ionospheric conditions due to various dominant localized physical and chemical processes. Although there have been ongoing improvements to the extensively utilized empirical model known as the International Reference Ionosphere (IRI), the newly emerged version (IRI-2020) needs to undergo global testing. In this research, we carried out diurnal and seasonal variations in GPS-TEC and the assessment of some ionospheric models such as IRI-2016 and its recently updated version (IRI-2020), alongside the NeQuick-2 model at 2 stations each in the East, West and South in the equatorial and low-latitude African longitudes during different phases of solar cycles 24–25 (2016 – 2021). Also, we carried out statistical analysis between GPS-TEC and the ionospheric models using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), in order to show the model with the best forecasting capability in the African region. Diurnal, seasonal and solar cycle variations in GPS-TEC, NeQuick-2, IRI-2016 and IRI-2020 were observed, showing higher magnitudes in the West, followed by the East in close range and least in the Southern sector of the African longitudes. TEC Variations in some sectors in the African longitudes show a consistent trend in this research. More importantly, there are observed regional differences within the African longitudes owing to the wider coverage of landmass in the equatorial and low latitudes. However, TEC variations in the Northern sector of Africa are not included in the present research. From our observation, NeQuick-2 and IRI-2016 models either underestimate or overestimate GPS-TEC during different phases of the solar cycles at the three sectors in the African longitudes, whereas IRI-2020 shows mostly underestimating characteristics at the three sectors irrespective of solar activity conditions during the study period. Nevertheless, the underestimation or overestimation of NeQuick-2 and IRI-2016, and the underestimation of IRI-2020 are reflected in the RMSE and MAE values. Regrettably, the predictions from IRI-2020 model are not satisfactory at any of the three sectors in the African longitudes and prompt attention of the modeling community for further investigations towards possible refinements in the model specifications.

Performance of IRI 2016 model in predicting total electron content (TEC) compared with GPS-TEC over East Africa during 2019–2021

This study evaluated the applicability of IRI-2016 model in predicting GPS TEC using the monthly means of the five (5) quiet days for equinoxes and solstices months. GPS-derived TEC data were obtained from the IGS network of ground based dual frequency GPS receivers from three stations [(KYN3 0.53° S, 38.53° E; Geom. Lat. 3.91.63° S), (MBAR 0.60° S, 30.74° E; Geom. Lat. 2.76° S) and HOID 1.45° S, 31.34° E; Geom. Lat. 3.71° S]. All the three options for topside Ne of IRI-2016 model and ABT-2009 for bottomside thickness have been used to compute the IRI TEC. The results were compared with the GPS TEC measurements. Correlation Coefficients between the two sets of data, the Root-Mean Square Errors of the IRI-TEC from the GPS-TEC, and the percentage RMSE of the IRI-TEC from the GPS-TEC have been computed. In general, the IRI-2016 model underestimated GPS-TEC during the nighttime, whereas the model overestimated GPS-TEC values during the daytime. At most of the stations and during all seasons where data were available, correlation coefficient was above 0.9, which is quite strong. The variation of O/N2 ratio may potentially be the cause of the IRI TEC deviation from the GPS TEC. This variation arises from lower thermosphere plasma drift that moves upward.

Dependence of annual asymmetry in <i>NmF2</i> on local time

Based on the global empirical model of the F2 layer critical frequency median (Satellite and Digisonde Data Model of the F2 layer, SDMF2), an analysis was made of the properties of diurnal variations in the annual asymmetry in the concentration of the F2 layer maximum NmF2 at different values of the solar activity index F. The AI index, which characterizes the relative difference in NmF2 averaged over all longitudes and latitudes between January and July at a given local time, was used as a parameter of this asymmetry. It was found that the diurnal variations of the AI index are dominated by a semidiurnal mode with maxima in the daytime and at night. The daytime maximum of the AI index is almost independent of the level of solar activity. The nighttime AI maximum decreases with increasing solar activity. For low solar activity, the daytime and nighttime AI maxima almost coincide in amplitude when AI = 16—17%. The difference in the solar radio flux between January and July due to the ellipticity of the Earth’s orbit relative to the Sun makes a significant contribution to the AI index at all hours of the day. On average, it is 3—4% and can reach 5% with low solar activity at night. The difference in the AI index for low and high activity according to the IRI model (with URSI and, especially, CCIR coefficients) is overestimated relative to the SDMF2 model at almost all hours of the day, apparently due to the limited number of experimental data when obtaining the CCIR and URSI coefficients especially over the oceans

Assessment of the accuracy of IRI-2016 model at the equatorial-, low-, mid-, and high-latitudes ionosphere along 30–45oE meridian lines during minimum (2009) and maximum (2013) phases of solar cycle 24

This study assesses the accuracy of the International Reference Ionosphere IRI-2016 model prediction at the equatorial-, low-, mid-, and high-latitude ionosphere along 30–45oE meridian during minimum (2009) and maximum (2013) phases of solar cycle 24. We used raw TEC derived from GPS observation data from GPS receivers within ∼±70o latitudinal band, along the African/European longitudinal sector. We compared the GNSS-TEC data after the Plasmaspheric Electron Content (PEC) has been excluded with those derived from the two components of the IRI-2016 model. The percentage contributions of PEC to GNSS-TEC was minimum in June solstice, and maximum in December solstice with the highest values at the high-latitude regions, but with a clear hemispherical asymmetry; manifesting the winter anomaly effect. Higher TEC values were recorded in the equatorial/low-latitude regions than in the high- and mid-latitude regions. Overall, in both hemispheres, the accuracy of prediction of the IRI-2016 model was higher in the high- and mid-latitude regions than in the equatorial/low-latitude regions. The accuracy of the model was higher during solar minimum than during solar maximum. Both options of IRI-2016 performed better at nighttime than during daytime. IRI-NeQuick option generally performed better than the IRI-Corr option. TEC values were higher in the southern hemisphere than in the northern hemisphere.

The capability of Artificial Neural Networks as a Model for Predicting Total Electron Content (TEC): A Review

The results of investigations from a complete analysis of ANN application on Total Electron Content (TEC) prediction are presented in this paper. TEC is important in defining the ionosphere and has many everyday applications, for example, satellite navigation, time delay and range error corrections for single frequency Global Positioning System (GPS) satellite signal receivers. The total electron content (TEC) in the ionosphere has been measured using GPS. GPS are not installed in every point on the earth to make global TEC measurements possible. As a result, it is crucial to have certain models that can aid to get data from places where there is not any in order to comprehend the global behavior of TEC. Neural Network (NN) models have been shown to accurately anticipate data patterns, including TEC. The capacity of neural networks to represent both linear and nonlinear relationships directly from the data being modeled is what makes them so powerful. The survey from literature reveals that, Levenberg-Marquardt algorithm is preferred and used mostly because of its speed and efficiency during learning process, and that ANN showed a good prediction of TEC compared to the IRI model. As a result, NNs are suitable for forecasting GPS TEC values at various locations if the model's input parameters are well specified.

Performance evaluation of ionospheric models over equatorial Indian region

An extensive year-long evaluation of Total Electron Content (TEC) obtained from NCAR Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM) simulations along with TEC obtained from International Reference Ionosphere (IRI-2020) is carried out with respect to observations made by a GPS TEC receiver over the dip equatorial location of Thiruvananthapuram (8.5° N, 77° E, 0.5° magnetic), in the Indian region during solar minimum period of 2005–2006. The TIE-GCM model and IRI model were run for the period of 2005 (November December) and till October 2006 for each day, and the hourly values of TEC were compared to GPS TEC observations. Overall the temporal and seasonal characteristics of TEC over Thiruvananthapuram were simulated reasonably well by the TIE-GCM model and IRI model. The skill scores of normalized Root Mean Square Error (nRMSE), Prediction Efficiency (P.E), and Ratio between maximum and minimum values were estimated for both models to evaluate their performance. The mean percentage difference between the measured and the simulated TEC was found to be around −25% for IRI, indicating underestimation of the observations, and between −40% and −50% for TIE-GCM again indicating underestimation. A comprehensive analysis revealed that there is good conformity between the modeled and measured values for the whole observation period, with a correlation coefficient R-value of 0.8 and 0.9 for the TIE-GCM and IRI models respectively. This study brings out the competence of the two models in simulating the temporal and seasonal pattern of variability of ionospheric TEC over the equatorial Indian region.

Performance Analysis of NeQuick-G, IRI-2016, IRI-Plas 2017 and AfriTEC Models over the African Region during the Geomagnetic Storm of March 2015

Performance Analysis of NeQuick-G, IRI-2016, IRI-Plas 2017 and AfriTEC Models over the African Region during the Geomagnetic Storm of March 2015

Preparation of functionalized polymer nanomaterials and application in renal injury

Renal injury, which is a prevalent condition, significantly impacts the health of competitive wrestlers. However, there is a limited number of therapeutic methods and drugs available for its treatment. To tackle this issue, a renal targeted drug based on functionalized polymer nanoparticles (FPNPs) is being studied. The primary methods in the preparation of FPNPs involve utilizing the thin film hydration technique to create nanoparticles and encapsulating the drug TP within them as a treatment for renal injury, thus forming TP-FPNPs. Subsequently, the renal targeting of TP-FPNPs is analyzed using in vitro organ fluorescence imaging techniques. Furthermore, a renal IRI model is constructed to assess the pharmacokinetics of TP-FPNPs using the application method. The preparation results of TP-FPNPs reveal a hydrated particle size of approximately 90 nm, which remains in the renal region for over 48 hours. Additionally, the concentration content surpasses that of other organs by more than threefold after 12 hours. Moreover, in the application results, TP-FPNPs continue to possess a concentration of 302.5 ng/g even after 48 hours post-injection. Based on the aforementioned results, it can be concluded that TP-FPNPs exhibit specific targeting, biocompatibility, and an extended retention time. Thus, this study is expected to effectively achieve the effect of treating renal injury in martial arts athletes, establishing an experimental foundation for its clinical treatment.

Bridging the evidence gap in spatial planning

The article focuses on how the use of evidence in spatial planning could bridge the gap between vision and reality through the continuous evaluation of the spatial impacts of the proposed interventions. The introduction sets the theoretical, institutional, and practical context on how evidence is used to assess these impacts in relation to both the expected outcomes and the pursued policy priorities. The research section addresses these issues based on the empirical and methodological background derived from a series of successive studies carried out between 1999 and 2014. These studies are related to the establishment and operation of the spatial impact observatory of the Egnatia motorway, a major European transport infrastructure project in northern Greece. The results section introduces a methodological approach, succinctly referred to as the IRIS model in which spatial planning is conceived as an adaptive process, and the use of evidence aims to enhance its flexibility and preparedness in dealing with the uncertainties that arise from dynamic conditions, rather than relying solely on predetermined solutions. It comprises three key components: a theoretical model that simulates the relationship between transport infrastructure and spatial development, an intermediate data model in which raw data were constructed as evidence indicators, and a combination of inductive and deductive paths in which evidence is used to assess the anticipated impact of spatial plans and to evaluate the actual spatial outcomes after their implementation. Finally, the conclusions underline the value added of the IRIS approach as a comprehensive and integrative methodology that aims to improve the efficacy of spatial planning by establishing a link between theoretical models, policy objectives, and evidence-based decision-making.

A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer

AbstractTo achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.

Impact of geomagnetic storms on ionospheric TEC at high latitude stations: a comparative analysis of GPS observations and the IRI-2016 model

Impact of geomagnetic storms on ionospheric TEC at high latitude stations: a comparative analysis of GPS observations and the IRI-2016 model

Day to night shift in reflection height of VLF radio waves derived from IRI model electron density models

Day to night shift in reflection height of VLF radio waves derived from IRI model electron density models

An Approach to Incorporate Amplitudes and Wavelengths of Transverse Crack for Predicting IRI

A methodology was proposed that can improve pavement smoothness prediction. As an initial attempt, smoothness due to solely transverse cracking was investigated. The new model incorporated amplitudes and wavelengths of transverse crack which are directly related to IRI computation. The crack amplitude and wavelength were measured by automated profiler from the MnRoad test roads and formulized with mathematical form expressed with respect to various crack depths and widths, respectively. Instead of conventional severity criteria, crack severities could be determined by crack depth and width in the mathematical form. Utilizing digital signal processing techniques, cracked surfaces with various severities were successfully prepared. The computed IRI values were found to be significantly related to crack severities expressed by width and depth. Then, an IRI prediction model was derived containing the simulated various crack severities and crack occurrences. Ultimately, the IRI model was validated using transverse crack performance data from MnRoad. The model incorporated spectral information that could fundamentally and accurately predict IRI increment by crack. This confirmed the necessity of incorporation of amplitude and wavelength information in predicting IRI.