Deep learning model for predicting daily IGS zenith tropospheric delays in West Africa using TensorFlow and Keras

Osah, Samuel; Acheampong, Akwasi A.; Fosu, Collins; Dadzie, Isaac

doi:10.1016/j.asr.2021.04.039

Cited by 24 publications

(12 citation statements)

References 45 publications

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“…TensorFlow (Google Inc., Menlo Park, CA, USA) is the most extensively used and popular low-level framework [30]. Keras is a well-known advanced DL framework with TensorFlow as its backend engine [50]. Keras was written in Python in this study, and the sample data were randomly split into a training part and a testing part.…”

Section: Kerasmentioning

confidence: 99%

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

et al. 2022

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Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.

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Section: Kerasmentioning

confidence: 99%

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

et al. 2022

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“…TensorFlow is one of the world's most widely used machine learning and deep learning libraries, with a large and active community of developers and researchers contributing to its ongoing development and improvement. TensorFlow deep neural networks (TFDeepNN) have successfully been applied for analyzing and processing spatial data in various spatial domains, i.e., floods [36], climate forecasts [37], landslides [38], and forest fire detections [39]. A typical structure of the TensorFlow deep neural networks (TFDeepNN) is shown in Figure 1.…”

Section: Tensorflow Deep Neural Networkmentioning

confidence: 99%

A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas

Truong,

Nhu,

Phuong

et al. 2023

Remote Sensing

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Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models’ predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management.

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“…Compared to the Multilayer Perceptron (MLP) algorithm, one verified that CNN could extract more detailed spatial features in multivariate time series data. Osah et al [29] constructed a regional ZTD model based on the location and surface meteorological parameters (pressure, temperature, and water vapor pressure) of four International GNSS Service (IGS) stations in West Africa using the deep learning algorithm. Ding et al [30] verified the effectiveness of multi-parameters for constructing the ZWD model by the multilayer feedforward neural network.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered

Wu,

Huang,

Feng

et al. 2024

Atmosphere

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The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm.

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Deep learning model for predicting daily IGS zenith tropospheric delays in West Africa using TensorFlow and Keras

Cited by 24 publications

References 45 publications

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas

A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered

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