Performance analysis of Neural Networks with IRI-2016 and IRI-2012 models over Indian low-latitude GPS stations

Mallika, Lakshmi; Ratnam, D. Venkata; Raman, Saravana; Sivavaraprasad, G.

doi:10.1007/s10509-020-03821-6

Cited by 15 publications

(2 citation statements)

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“…The lack of performance models for such countries has resulted in an inadequacy of information for proactive pavement maintenance planning. Most of the time, developing countries evaluate the current condition of pavement and employ this evaluation for reactive maintenance planning, such as in the cases of the evaluation of the PCI in Yaman [36], monitoring of pavement condition in Kabul, Afghanistan [37], assessing road roughness in India [38], and investigation of the IRI in New Mexico [39]. Nonetheless, few developing countries have attempted to develop performance models for their cities or countries.…”

Section: Pavement Performance Modeling Proceduresmentioning

confidence: 99%

Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data

Wasiq,

Golroo

2024

Infrastructures

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Road networks play a significant role in each country’s economy, especially in countries such as Afghanistan, which is strategically located in the international transit path from Europe to East Asia. In such a country, pavement performance models are fundamental for the pavement maintenance planning that provides high-quality infrastructure for transporting goods and travelers. However, due to the lack of a budget for pavement monitoring and maintenance in Afghanistan, transportation networks and pavement condition data have not been widely acquired for the development of a pavement performance model. The main aim of this study is to use a machine learning technique to, for the first time, develop a pavement performance model for Afghanistan that uses simple, cost-effective, and fairly accurate data—collected via smartphones—and that is based on a case study of over 550 km of Afghanistan’s highways. First, the current condition of Afghanistan’s road network is investigated using a smartphone. Then, collected data are prepared and analyzed so as to estimate the pavement condition index (PCI). Finally, a pavement performance model for PCI is developed using pavement age with an adequate coefficient of determination of 0.70 and successfully validated. It is concluded that the proposed approach is efficient and effective when developing a performance model in other developing countries encountering such data and budget limitations.

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Section: Pavement Performance Modeling Proceduresmentioning

confidence: 99%

Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data

Wasiq,

Golroo

2024

Infrastructures

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“…e solar wind parameters (SWp) and the TEC values of the IRI-2012, 2016 model are utilized in the ANNm that performs the backpropagation iteration of Rumelhart et al [49].…”

Section: Introductionmentioning

confidence: 99%

Discussing Total Electron Content over the Solar Wind Parameters

Eroğlu

2022

Mathematical Problems in Engineering

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Modeling and forecasting of Total Electron Content (TEC) values by an Artificial Neural Network model (ANNm) have high agreement on November 2003, 2004 superstorms. The work discusses Solar Wind Parameters (SWp) from OMNI (Operating Missions as a Node on the Internet) and TEC (TECU) data (International Reference Ionosphere) IRI-2012, IRI-2016 on November 20, 2003 (Dst = –422 nT) and on November 08, 2004 (Dst = –374 nT) Geomagnetic Storms (GSs). The paper commences with a 120-hour GS exhibition of SWp and proceeds with the correlation data of the variables, their hierarchical tracks, and inner dispersions. The ANNm with SWp as the input and TEC data as the output are introduced. The performance of the ANNm for 2003 and 2004 superstorms is adequate. The Correlation Coefficient (R) and Root Mean Square Error (RMSE) of the ANNm are 97.5%, 1.17 TECU (IRI-2012), and 97.9%, 1.09 TECU (IRI-2016) for the 2003 GS and 97.0%, 0.89 TECU (IRI-2012), and 98.0%, 1.61 TECU (IRI-2016) for 2004 GS. Parameters effect of the R constant of TEC data points out to the dynamic pressure (nPa), the magnetic field Bz component (nT), the flow speed (km/s), and the proton density (1/cm3). Besides, the absolute total error and the variance of the predicted TEC data for November 2003 and November 2004 GSs are 0.06 (0.30%) with 0.013 variance (IRI-2012), 0.09 (0.49%) with 0.016 variance (IRI-2016) for 2003 storm and 0.13 (0.73%) with 0.033 variance (IRI-2012), and 0.11 (1.06%) with 0.035 variance (IRI-2016) for 2004. It means that the paper models TEC data with considerable consistency over the SWp.

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Ionospheric Modelling and Forecasting

Elmunim

Abdullah

2021

Ionospheric Delay Investigation and Forecasting

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Performance analysis of Neural Networks with IRI-2016 and IRI-2012 models over Indian low-latitude GPS stations

Cited by 15 publications

References 35 publications

Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data

Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data

Discussing Total Electron Content over the Solar Wind Parameters

Ionospheric Modelling and Forecasting

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