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Reservoir production capacity prediction of Zananor field based on LSTM neural network
This paper aims to explore the application of artificial intelligence in the petroleum industry, with a specific focus on oil well production...
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Motion simulation of moorings using optimized LSTM neural network
Mooring arrays have been widely deployed in sustained ocean observation in high resolution to measure finer dynamic features of marine phenomena....
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On the use of VMD-LSTM neural network for approximate earthquake prediction
Earthquake prediction has been widely studied in many fields using various technologies, including machine learning, which is able to explore the...
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Sensitivity analysis of regional rainfall-induced landslide based on UAV photogrammetry and LSTM neural network
Rainfall stands out as a critical trigger for landslides, particularly given the intense summer rainfall experienced in Zheduotang, a transitional...
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Ultra-short-term prediction of LOD using LSTM neural networks
Earth orientation parameters (EOPs) are essential in geodesy, linking the terrestrial and celestial reference frames. Due to the time needed for data...
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Seismic velocity inversion based on CNN-LSTM fusion deep neural network
Based on the CNN-LSTM fusion deep neural network, this paper proposes a seismic velocity model building method that can simultaneously estimate the...
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Research on modeling and predicting of BDS-3 satellite clock bias using the LSTM neural network model
In the Global Navigation Satellite System (GNSS), the satellite clock bias (SCB) is one of the sources of ranging error, and its ability to predict...
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Efficient prediction of runway visual range by using a hybrid CNN-LSTM network architecture for aviation services
Visibility is the primary criterion for the landing and takeoff of an aircraft. At all major airports, a procedure called the low visibility...
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Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches
Because hurricanes, droughts, floods, and heat waves are all important factors in measuring environmental changes, they can all result from changes...
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New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast
Global navigation satellite system (GNSS) signals are significantly affected by the ionosphere. An efficient way to assess the ionospheric effects on...
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Effects of Different Spatial Resolutions on Prediction Accuracy of Thunnus alalunga Fishing Ground in Waters Near the Cook Islands Based on Long Short-Term Memory (LSTM) Neural Network Model
Albacore tuna (Thunnus alalunga) is one of the target species of tuna longline fishing, and waters near the Cook Islands are a vital albacore tuna...
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Inversion of 1-D magnetotelluric data using CNN-LSTM hybrid network
The magnetotelluric (MT) inversion is nonlinear and ill-posed, which poses great challenges for accurate model reconstruction. To tackle these...
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Enhancing satellite clock bias prediction in BDS with LSTM-attention model
Satellite clock bias (SCB) is a critical factor influencing the accuracy of real-time precise point positioning. Nevertheless, the utilization of...
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Daily air temperature forecasting using LSTM-CNN and GRU-CNN models
Today, air temperature (AT) is the most critical climatic indicator. This indicator accurately defines global warming and climate change, despite the...
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Comparison of LSTM network, neural network and support vector regression coupled with wavelet decomposition for drought forecasting in the western area of the DPRK
Drought forecasting is very important in reducing the drought damage and optimizing water resources. This paper focuses on confirming the advantage...
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Prediction of PM2.5 concentration based on the weighted RF-LSTM model
Accurate prediction of PM2.5 concentrations can provide a solid foundation for preventing and controlling air pollution. When the Long Short-Term...
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Hybrid neural network wind speed prediction based on two-level decomposition and weighted averaging
The randomicity and fluctuation of the wind speed will influence the precision of the forecast. This paper presents a new method of combined wind...
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Landslide displacement prediction based on the ICEEMDAN, ApEn and the CNN-LSTM models
Landslide deformation is affected by its geological conditions and many environmental factors. So it has the characteristics of dynamic, nonlinear...
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Detection and mitigation of time synchronization attacks based on long short-term memory neural network
Due to its wide-area and high-precision advantages, Global Navigation Satellite System (GNSS) timing is widely employed in critical infrastructures...
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Reservoir characterization reimagined: a hybrid neural network approach for direct three-dimensional petrophysical property characterization
Reservoir characterization, crucial for oilfield development, aims to unravel intricate non-linear relationships within real-world data. Conventional...
