[HTML][HTML] A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …
establishing new infrastructure in the power generation network. To deliver a high-quality …
Combining ERA5 data and CYGNSS observations for the joint retrieval of global significant wave height of ocean swell and wind wave: a deep convolutional neural …
As an emerging remote sensing technology, GNSS reflectometry (GNSS-R) has been widely
investigated for retrieving ocean parameters including ocean significant wave height (SWH) …
investigated for retrieving ocean parameters including ocean significant wave height (SWH) …
Glows-net: A deep learning framework for retrieving global sea surface wind speed using spaceborne gnss-r data
Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote
sensing technology that uses GNSS signals reflected from the Earth's surface to estimate …
sensing technology that uses GNSS signals reflected from the Earth's surface to estimate …
[HTML][HTML] Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods
Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth,
climate change, natural disasters such as forest fires, and drought prediction. Spaceborne …
climate change, natural disasters such as forest fires, and drought prediction. Spaceborne …
Deep Learning in Spaceborne GNSS Reflectometry: Correcting Precipitation Effects on Wind Speed Products
Deep learning techniques have shown the capability in GNSS reflectometry (GNSS-R) for
retrieving geographical parameters based on GNSS-R observations. Recent studies have …
retrieving geographical parameters based on GNSS-R observations. Recent studies have …
A bagged-tree machine learning model for high and low wind speed ocean wind retrieval from CYGNSS measurements
This article presents two empirical models, the low wind bagged trees (LWBT) and high wind
bagged trees (HWBT) ensemble models to estimate ocean surface wind speed using …
bagged trees (HWBT) ensemble models to estimate ocean surface wind speed using …
Significant wave height retrieval based on multivariable regression models developed with CYGNSS data
This study utilizes L1B level data from reflected global navigation satellite system (GNSS)
signals from the Cyclone GNSS (CYGNSS) mission to estimate sea surface significant wave …
signals from the Cyclone GNSS (CYGNSS) mission to estimate sea surface significant wave …
MF-ANN: A Novel Artificial Neural Network based Method for Ocean Wind Speed Retrieval on Spaceborne GNSS-R Signal
H **e, X Cheng, S He, Y Li, J Pang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
An artificial neural network (ANN) model-based method is proposed to retrieve the ocean
surface wind speed from cyclone global navigation satellite system (CYGNSS) L1 …
surface wind speed from cyclone global navigation satellite system (CYGNSS) L1 …
A novel dual-branch neural network model for flood monitoring in south Asia based on CYGNSS data
D Song, Q Zhang, B Wang, C Yin, J **a - Remote Sensing, 2022 - mdpi.com
Microwave remote sensing is widely applied in flood monitoring due to its independence
from severe weather conditions, which usually restrict the usage of optical sensors …
from severe weather conditions, which usually restrict the usage of optical sensors …
Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval
Global navigation satellite system reflectometry (GNSS-R) delay-Doppler map (DDM)
measures the sea surface roughness, which has recently been applied to retrieve sea …
measures the sea surface roughness, which has recently been applied to retrieve sea …