Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network

Zhao, Yihao; Wang, Maofa; Xue, Huanhuan; Gong, Youping; Qiu, Baochun

doi:10.3390/app11114896

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…In this way, each layer of the network is trained independently and greedily. Hidden variables are used as observable variables to learn each layer of deep structure (Zhao et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Sorkhabi

Milani

Alizadeh

2022

Earth and Space Science

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Observations and processing of Iran's national geodynamic network, global positioning systems (GPSs) track and record signals received from GPS satellites 24 hr a day. The information collected at each station is received in the form of observation and navigation files, meteorological data and tilt meters in the main processing centers, using the telephone line and modem. These centers are located in Tehran and the surveying managements of the surveying organization in the cities of Mashhad, Tabriz, Hamedan and Ahvaz, which receive this information daily. In these data centers, after performing quality control and format conversion, the received data are ready to enter the GPS processing software. Processing in these centers is done on a daily and final basis, the results of which are presented in the form of stations exact coordinates in the international terrestrial reference frame (ITRF) coordinate system, coordinate time series and velocity vectors along with the error ellipses of each station. The results of processing global geodynamic networks are in the form of each station time series, displacement and velocity vectors, and exact ITRF coordinate system and their conversion in the world geodetic system 1984 (WGS84) coordinate system along with their accuracy. These results, as well as observation and navigation files, are stored in data centers (Djamour et al., 2010).Artificial neural network (ANN) has been successfully used in various fields of geodesy. Coordinate transformation with ANN was found to be a practical tool, effective and feasible (Cakir and Konakoglu. 2019). Geoid undulation determination with ANN has root mean square error of 10.81 cm (Konakoglu et al., 2022). Ionospheric variations with ANN has an R value greater than 0.74 (Inyurt and Sekertekin. 2019).Estimation of geodetic velocity is of special importance for geodynamic studies especially fault displacement. GV is determined based on GPS and global navigation satellite system (GNSS) observations at different time intervals and can be used to measure tectonic movements (Sorkhabi, Alizadeh, et al., 2022). GNSS stations, due to their high cost and difficulty in measuring and maintaining them, need methods that can accurately estimate GV in gap areas. Memarin Sorkhabi and Djamour (2015) in northwestern Iran estimated geodetic velocity (GV) with an ANN reaching root mean square error (RMSE) less than 3.5 mm. In 2021, Konakoglu estimated the GV with the multi-layer perceptron neural network method, which was more accurate than classical mathematical methods. MariaMrówczyńska et al. (2020) used ANN and principal component analysis (PCA) to estimate GV, which performs both compression and reconstruction methods with high accuracy. Back propagation artificial neural network with 42 GPS stations has an RMSE of less than 2 mm in determining the GV of northwestern Iran (Sorkhabi, Alizadeh, et al., 2022).Various deep learning methods have been developed that can be used according to the type of problem. The comparison of this research with the pr...

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“…In this way, each layer of the network is trained independently and greedily. Hidden variables are used as observable variables to learn each layer of deep structure (Zhao et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Sorkhabi

Milani

Alizadeh

2022

Earth and Space Science

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“…Machine learning based acoustic TL predictions have been explored in air [11], [12], and both Bayesian [13] and ML [14] methods have been applied to predict TL in underwater environments. Similar to this work, Yihao et al [13] This work takes a sequential approach to determining the ability of machine learning algorithms to learn specific underwater acoustic phenomena and apply those algorithms to directly predict TL in such environments.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for point-to-point transmission loss estimates in ocean acoustic waveguides

Kelly

O'Donnell

Sabra

et al. 2022

The Journal of the Acoustical Society of America

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Underwater acoustic system performance depends on several complex and dynamic environmental parameters, and simulating such performance is vital to the success of development and implementation of these systems. Because of the complexity of the environment and governing physical equations, realistic simulations can become computationally prohibitive. This is especially true of for large environments with many active systems being assessed. By utilizing convolutional neural networks (CNNs) trained on data generated by well-established physics based models (such as BELLHOP’s ray tracing algorithm), network predictions can be used lieu of physics-based models to significantly reduce the computational burden in the loop for system performance simulations. In this paper, the usefulness and limitations of using CNNs to estimate transmission loss (TL), which is a key element in determining system performance, is explored. Using BELLHOP’s ray tracing algorithm as a baseline, CNN’s were able to produce TL results with significantly lower errors than those estimates made using other estimation methods such as spherical spreading and K-nearest neighbors. This indicates that the computational costs of large underwater acoustic simulations may be shifted from inside the simulation to network training, thus allowing for more efficient traditional and Monte Carlo style simulations.

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Deep neural network regression for estimating land surface temperature at 10 meter spatial resolution using Landsat-8 and Sentinel-2 data

Jamaluddin

Dang

Mahendra

et al. 2021

Seventh Geoinformation Science Symposium 2021

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Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network

Cited by 5 publications

References 17 publications

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Machine learning for point-to-point transmission loss estimates in ocean acoustic waveguides

Deep neural network regression for estimating land surface temperature at 10 meter spatial resolution using Landsat-8 and Sentinel-2 data

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