Wavelet decomposition and deep learning of altimetry waveform retracking for Lake Urmia water level survey

Sorkhabi, Omid Memarian; Asgari, J.; Amiri-Simkooei, A. R.

doi:10.1080/1064119x.2021.1899348

Cited by 14 publications

(3 citation statements)

References 38 publications

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“…The effectiveness of WT also has been combined with the powerful performance of the deep learning approach. Wavelet decomposition and CNNs were used to analyze returned waveform data for the prediction of water level [ 20 ]. In the image processing area, deep learning networks were applied to the wavelet transformed image for image inpainting [ 21 ], super-resolution [ 22 ], and brain tumor detection [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

Sensor Data Prediction in Missile Flight Tests

Ryu

Jeong

Shim

2022

Sensors

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Sensor data from missile flights are highly valuable, as a test requires considerable resources, but some sensors may be detached or fail to collect data. Remotely acquired missile sensor data are incomplete, and the correlations between the missile data are complex, which results in the prediction of sensor data being difficult. This article proposes a deep learning-based prediction network combined with the wavelet analysis method. The proposed network includes an imputer network and a prediction network. In the imputer network, the data are decomposed using wavelet transform, and the generative adversarial networks assist the decomposed data in reproducing the detailed information. The prediction network consists of long short-term memory with an attention and dilation network for accurate prediction. In the test, the actual sensor data from missile flights were used. For the performance evaluation, the test was conducted from the data with no missing values to the data with five different missing rates. The test results showed that the proposed system predicts the missile sensor most accurately in all cases. In the frequency analysis, the proposed system has similar frequency responses to the actual sensors and showed that the proposed system accurately predicted the sensors in both tendency and frequency aspects.

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Section: Related Workmentioning

confidence: 99%

Sensor Data Prediction in Missile Flight Tests

Ryu

Jeong

Shim

2022

Sensors

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“…Additionally, from the review of Zhu, Lu, et al (2020), we can also find that deep learning models have rarely been employed in lake water‐level forecasting even though deep learning has already proved its efficiency in other subjects of hydrology (Shen, 2018; Sit et al, 2020; Tesch et al, 2021). Available studies include Liang et al (2018), Hrnjica and Bonacci (2019), Zhu, Hrnjica, et al (2020), Sorkhabi et al (2021), and Barzegar et al (2021), which used the traditional long short‐term memory (LSTM) model in lake water‐level forecasting. In recent years, deep learning models have been improved, and attention mechanism has been coupled with deep learning models (e.g., LSTM), which has been widely used in other regions (Aguirre et al, 2021; Xie et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Daily water‐level forecasting for multiple polish lakes using multiple data‐driven models

Ptak

et al. 2022

Geographical Journal

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Water level in lakes fluctuates frequently due to the impact of natural and anthropogenic forcing. Frequent fluctuations of water level will impact lake ecosystems, and it is thus of great significance to have a good knowledge of water-level dynamics in lakes. However, forecasting daily water-level fluctuation in lake systems remains a tough task due to its non-linearity and complexity. In this study, two deep data-driven models, including gated recurrent unit (GRU) and long short-term memory (LSTM), were coupled with attention mechanism for the forecasting of daily water level in lakes for the first time. Daily water-level times series in five lowland lakes in Poland were used to evaluate the models. Root mean squared error (RMSE) and mean average error (MAE) were used for the evaluation of model performance. The modelling results were compared with the traditional feed-forward neural networks (FFNN), GRU, LSTM, and zero-order forecast. The modelling results showed that sequential deep learning models do not outperform feed-forward models in all cases. In most cases, LSTM with attention mechanism (average RMSE = 0.88 cm, average MAE = 0.69 cm) outperforms GRU with attention mechanism (average RMSE = 1.00 cm, average MAE = 0.81 cm). However, attention mechanism did not help to improve the accuracy of the GRU and LSTM for most cases. Based on the average performance in different lakes, GRU performs the best among the deep learning models (average RMSE = 0.84 cm, average MAE = 0.66 cm).Zero-order forecast models perform better than deep learning models for predicting tomorrow (average RMSE = 0.71 cm, average MAE = 0.39 cm), while deep learning models perform better as the horizon of prediction increases.

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“…Successful applications range from improved snow water equivalent products over British Columbia, Canada [22] to the identification of potential Antarctic meteorite sites [23]. Further studies of particular relevance are the regression-based neural network solution for the estimation of snow depth on Arctic sea ice using multi-band observations from the Copernicus Imaging Microwave Radiometer [24] and the enhanced waveform retracking of lake surface elevation using deep learning [25].…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry

Horton¹,

Ewart²,

Gourmelen

et al. 2022

Remote Sensing

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Satellite and airborne observations of surface elevation are critical in understanding climatic and glaciological processes and quantifying their impact on changes in ice masses and sea level contribution. With the growing number of dedicated airborne campaigns and experimental and operational satellite missions, the science community has access to unprecedented and ever-increasing data. Combining elevation datasets allows potentially greater spatial-temporal coverage and improved accuracy; however, combining data from different sensor types and acquisition modes is difficult by differences in intrinsic sensor properties and processing methods. This study focuses on the combination of elevation measurements derived from ICESat-2 and Operation IceBridge LIDAR instruments and from CryoSat-2’s novel interferometric radar altimeter over Greenland. We develop a deep neural network based on sub-waveform information from CryoSat-2, elevation differences between radar and LIDAR, and additional inputs representing local geophysical information. A time series of maps are created showing observed LIDAR-radar differences and neural network model predictions. Mean LIDAR vs. interferometric radar adjustments and the broad spatial and temporal trends thereof are recreated by the neural network. The neural network also predicts radar-LIDAR differences with respect to waveform parameters better than a simple linear model; however, point level adjustments and the magnitudes of the spatial and temporal trends are underestimated.

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Wavelet decomposition and deep learning of altimetry waveform retracking for Lake Urmia water level survey

Cited by 14 publications

References 38 publications

Sensor Data Prediction in Missile Flight Tests

Sensor Data Prediction in Missile Flight Tests

Daily water‐level forecasting for multiple polish lakes using multiple data‐driven models

Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry

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