Article
Engineering, Aerospace
Sampad Kumar Panda, Haris Haralambous, Mefe Moses, J. R. K. Kumar Dabbakuti, Yekoye Asmare Tariku
Summary: The article presents a sensible assimilation method for peak parameters in the topside profiles and auto-scaled bottomside profiles, using FormoSat-3/COSMIC data and 39 global Digisonde data. The study finds that the overall ionospheric electron content estimates from the IRI model are relatively closer to the assimilated results compared to the NeQuick and IRI-Plas models, especially in terms of discrepancies between bottomside and topside electron content.
Article
Chemistry, Analytical
Yury V. V. Yasyukevich, Dmitry Zatolokin, Artem Padokhin, Ningbo Wang, Bruno Nava, Zishen Li, Yunbin Yuan, Anna Yasyukevich, Chuanfu Chen, Artem Vesnin
Summary: Global navigation satellite systems (GNSS) provide valuable data for testing ionosphere models. This study analyzed the performance of nine ionospheric models in terms of calculating total electron content (TEC) and improving single frequency positioning. The results showed that new-generation operational models have the potential to outperform or at least match classical empirical models. The findings contribute to the understanding and improvement of ionospheric modeling techniques.
Article
Engineering, Aerospace
Hany Mahbuby, Yazdan Amerian
Summary: Retrieved VTEC from dual-frequency GPS measurements is valuable for regional VTEC and IED modeling. However, 4D reconstruction of IED requires additional knowledge of electron density distribution, which is often obtained from global empirical models. Data assimilation, including the optimum interpolation method, plays a key role in IED modeling, especially in regions with sparse observations.
ADVANCES IN SPACE RESEARCH
(2023)
Article
Astronomy & Astrophysics
Pan Xiong, Dulin Zhai, Cheng Long, Huiyu Zhou, Xuemin Zhang, Xuhui Shen
Summary: A novel neural network model was proposed to predict ionospheric total electron content (TEC) by considering solar flux and geomagnetic activity data. The model outperformed other statistical models, particularly showing strong predictive performance across different geographical locations, seasons, and geomagnetic activities.
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
(2021)
Article
Engineering, Aerospace
Hossein Etemadfard, Masoud Mashhadi Hossainali
Summary: The study proposes a new method for simultaneous modeling of spatial gradients of ionosphere and VTECs in Iran using Vector Spherical Slepian (VSS) base functions. The results show that the optimum degree for the VSS model in Iran is 40. The proposed method improved the solution for long baselines by more than 12%, 18% and 10% in the east-west, north-south and up-down components, respectively.
ADVANCES IN SPACE RESEARCH
(2021)
Article
Chemistry, Analytical
Alina Barbulescu, Cristian Stefan Dumitriu
Summary: Experiments have demonstrated the presence of electrical signals in the ultrasonic cavitation field, and their properties have been studied. This research models the voltage collected in seawater during ultrasonic cavitation produced by a 20kHz frequency generator and compares the effectiveness of different modeling methods, with the hybrid Wavelet-ANN model proving to be the most accurate.
Article
Environmental Sciences
Alireza Atabati, Mahdi Alizadeh, Harald Schuh, Lung-Chih Tsai
Summary: Irregularities in electron density in the ionosphere can lead to radio signal fluctuations and ionospheric scintillations, which were predicted using a combination of artificial neural network (ANN) and genetic algorithm (GA) based on GNSS observations at GUAM station. The model showed an accuracy of about 81% for predicting ionospheric scintillations.
Article
Computer Science, Information Systems
Sadia Mostofa, Mardina Abdullah, Siti Aminah Bahari, Mohammad Tariqul Islam
Summary: This study investigates the impacts of severe geomagnetic storms in 2012 on large-scale ionospheric anomalies in the equatorial region of Malaysia. The research finds that the disturbances in the ionosphere caused by geomagnetic storms persist before and after the actual storm period. It highlights the importance of having a dedicated ionospheric weather index to evaluate and predict the impact of space weather storms, enhancing the precision and reliability of vulnerable radio systems.
Article
Astronomy & Astrophysics
Rong He, Min Li, Qiang Zhang, Qile Zhao
Summary: This study evaluates the performance of the Wuhan University GIM and the IRI-2020 models in the China region and finds that IRI-2020 has lower Total Electron Content (TEC), especially in high solar conditions and low-latitude regions. Model validation using GPS observations shows that WHU-GIM has higher accuracy, and both models can reproduce diurnal TEC variations, but IRI-2020 is more influenced by geomagnetic activities.
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
(2023)
Article
Engineering, Aerospace
Asfaw Merid, Melessew Nigussie, Atalay Ayele
Summary: Post-sunset ionospheric irregularities are common in the equatorial ionosphere and are believed to be triggered by atmospheric gravity waves. By decomposing fluctuating TEC into different oscillation modes, characteristics of atmospheric waves can be better understood and investigated.
ADVANCES IN SPACE RESEARCH
(2021)
Article
Multidisciplinary Sciences
Pantea Davoudifar, Keihanak Rowshan Tabari, Amir Abbas Eslami Shafigh, Ali Ajabshirizadeh, Zahra Bagheri, Fakhredin Akbarian Tork Abad, Milad Shayan
Summary: The study used TEC data to investigate ionospheric variations and developed a semi-empirical model to predict its mean values, enabling researchers to monitor irregular effects induced by solar events. The results showed that strong flares can cause TEC extent variations of over 20%.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Artificial Intelligence
Jing-Wei Liu, Fang-Ling Zuo, Ying-Xiao Guo, Tian-Yue Li, Jia-Ming Chen
Summary: The study proposed wavelet convolutional neural network (wCNN) and wavelet convolutional wavelet neural network (wCwNN), and found that these improved methods increased the complexity of the algorithm while improving both precision and accuracy in image classification experiments.
APPLIED INTELLIGENCE
(2021)
Article
Astronomy & Astrophysics
Rasim Shahzad, Munawar Shah, Arslan Ahmed
Summary: The study compares measured and modelled VTEC in the mid latitude Sukkur region of Pakistan during the final phase of solar cycle 24 (2019-2020), finding variations in diurnal, monthly, and seasonal analyses, with higher correlation between measured and modelled VTEC on days of low solar activity.
ASTROPHYSICS AND SPACE SCIENCE
(2021)
Article
Engineering, Aerospace
Chunyuan Zhou, Ling Yang, Xiaoning Su, Bofeng Li
Summary: This paper explores the potential advantages of neural networks in ionosphere modeling and predicting. It utilizes Radial Basis Function Neural Network (RBF-NN) for modeling and Long- and Short-Term Memory Neural Network (LSTM-NN) for predicting. The results show that RBF-NN outperforms traditional models, and LSTM-NN has better short-term performance. The predicted products also improve precise point positioning with reduced convergence time and improved horizontal positioning performance.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Engineering, Aerospace
Kenneth Iluore, Jianyong Lu
Summary: This paper explores the application of deep learning models, such as LSTM and GRU, in forecasting ionospheric GPS_VTEC. It compares the performance of these models with MLP neural networks, GIM_TEC, and the IRI-Plas 2017 models. The study finds that the GRU unit achieves the highest correlation coefficient and the lowest prediction error, indicating its superior prediction accuracy compared to other models.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Remote Sensing
Mir-Reza Ghaffari Razin, Behzad Voosoghi
Article
Engineering, Aerospace
Mir Reza Ghaffari Razin, Amirreza Moradi
Summary: A new method for temporal extrapolation of ionosphere total electron content (TEC) is proposed in this paper using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm. The efficiency of the proposed model is evaluated on observations from Tehran GNSS station in different solar activity periods, showing higher accuracy compared to traditional ionosphere models.
ADVANCES IN SPACE RESEARCH
(2021)
Article
Remote Sensing
Mir Reza Ghaffari Razin, Amir Reza Moradi, Samed Inyurt
Summary: A new method using support vector machine (SVM) for spatio-temporal modeling of ionospheric total electron content (TEC) during severe solar activity periods is proposed. The model is evaluated using observations from the Iranian permanent GPS network (IPGN), showing high accuracy in modeling the temporal and spatial variations of ionospheric TEC.
Article
Engineering, Aerospace
Parviz Nematipour, Mehdi Raoofian-Naeeni, Mir Reza Ghaffari Razin
Summary: In this paper, a novel approach based on C-1 finite element interpolation is used to construct a regional ionospheric grid model with high spatio-temporal resolution over Europe. The new model is evaluated using observations from 38 GPS stations and shows higher accuracy compared to existing global ionosphere mapping models. The results suggest that the FE model has a very high capability and accuracy in regional VTEC modeling in high and low geomagnetic conditions.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Engineering, Aerospace
Mir-Reza Ghaffari Razin, Behzad Voosoghi
Summary: This study applies two machine learning methods to model precipitable water vapor (PWV) based on GPS observations in the northwest region of Iran. The results show that the Support Vector Machine (SVM) has a higher accuracy compared to the Artificial Neural Network (ANN) and Voxel-based Tomography (VBT) models.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Remote Sensing
Mir-Reza Ghaffari Razin, Samed Inyurt
Summary: A new method based on ANFIS is proposed for spatiotemporal modeling of precipitable water vapor (PWV). The method involves calculating the tropospheric zenith wet delay using GPS observations, converting it to PWV, and training an ANFIS network using the back-propagation algorithm. The results show that the proposed model has high accuracy and precision in determining the temporal and spatial variations of PWV.
Article
Environmental Studies
Navid Hooshangi, Navid Mahdizadeh Gharakhanlou, Seyyed Reza Ghaffari-Razin
Summary: The paper presents an innovative approach for determining the initial number of rescuers in earthquake emergencies by integrating queuing theory and GIS analysis. The results show that at least 2,300 rescue teams are required to achieve the calculated survival rate.
INTERNATIONAL JOURNAL OF DISASTER RESILIENCE IN THE BUILT ENVIRONMENT
(2022)
Article
Engineering, Aerospace
Seyyed Reza Ghaffari-Razin, Amir Reza Moradi, Navid Hooshangi
Summary: A new method for spatio-temporal modeling of ionosphere total electron content (TEC) using least squares support vector machine (LS-SVM) is proposed. The method reduces computational complexity, improves convergence speed and accuracy, and shows better performance in seasonal error analysis.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Engineering, Aerospace
Seyyed Reza Ghaffari-Razin, Reza Davari Majd, Navid Hooshangi
Summary: This study proposes a new model for spatio-temporal modeling and forecasting of precipitable water vapor (PWV) using the least square support vector regression (LS-SVR) method. The LS-SVR method simplifies the computational algorithm, resulting in increased convergence speed and accuracy of the results. The evaluation of the new model is conducted using GPS networks in north-west and central Alborz in Iran, and the results demonstrate its effectiveness and accuracy.
ADVANCES IN SPACE RESEARCH
(2023)
Article
Remote Sensing
Seyyed Reza Ghaffari-Razin, Asghar Rastbood, Navid Hooshangi
Summary: The generalized regression neural network (GRNN) method is proposed for spatio-temporal modeling of ionosphere total electron content (TEC). The new model shows higher accuracy and convergence speed compared to other machine learning models, and has been evaluated using observations from 30 global navigation satellite system (GNSS) stations in central Europe at 2015. The results indicate that the GRNN model outperforms other models in both high and low geomagnetic and solar activities, and can be considered as an alternative to global and empirical ionosphere models.
Article
Remote Sensing
Seyyed Reza Ghaffari-Razin, Asghar Rastbood, Navid Hooshangi
Summary: Surface displacement measurements of the earth's crust using GNSS observations are discrete and cannot be studied as a continuous field. The proposal of using an adaptive neuro-fuzzy inference system (ANFIS) model overcomes this problem by inputting geodetic coordinates of GPS stations and outputting displacement field components. In this study, the ANFIS model is compared with other interpolation processes and shows higher efficiency in analyzing crustal deformation.
Article
Green & Sustainable Science & Technology
Navid Hooshangi, Navid Mahdizadeh Gharakhanlou, Seyyed Reza Ghaffari Razin
Summary: Iran's energy consumption is increasing rapidly. This paper used a GIS-based FF-TOPSIS approach to prioritize potential sites for solar power plants, considering economic, geographic, climatic, infrastructural, and demographic criteria. Approximately 13.81% of Iran's land has the potential for solar power plants, with the provinces of Sistan and Baluchestan, Hormozgan, Fars, and Khuzestan having the highest potential.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Environmental Sciences
Parisa Agar, Shirzad Roohi, Behzad Voosoghi, Arash Amini, Davod Poreh
Summary: Coastal zones pose challenges for satellite altimeters due to the presence of non-water surfaces and calm sea surfaces, which lead to erroneous powers in return waveforms. Selecting an optimized retracker for post-processing waveforms is crucial for accurate water level estimation. Two approaches, waveform decontamination and waveform modification, were introduced to minimize the impact of erroneous powers. The performance of these approaches was evaluated against tidal gauges and compared with external datasets. The results showed that the first meaningful sub-waveform of the decontaminated waveforms had the best performance. The ALES database had worse performance compared to our sub-waveform retracking scenarios.
Article
Environmental Sciences
Mohammad Amin Khalili, Behzad Voosoghi, Luigi Guerriero, Saeid Haji-Aghajany, Domenico Calcaterra, Diego Di Martire
Summary: This paper aims to determine the best interval for interpreting long-term deformation processes and identifying displacement patterns by using three unsupervised clustering algorithms and implementing the advanced integration method for atmospheric phase screen correction. Through the comparison of signals corrected by the AIM and the GPS station, as well as similarity measures and Davies-Bouldin index scores, the SBAS technique with the unsupervised K-medians method has been chosen as the accurate and reliable interval.
Article
Geochemistry & Geophysics
Mohammad Amin Khalili, Behzad Voosoghi
Summary: This paper proposes a new Gaussian Radial Basis Functions (GRBF) for the interpolation and differentiation of scattered data in vertical deformation analysis. By comparing different methods in simulated and actual data, it was found that the LGRBF and PGRBF methods significantly improve the surface interpolation accuracy compared to traditional methods.
GEODESY AND GEODYNAMICS
(2021)
