[HTML][HTML] PERSIANN-CNN: Precipitation estimation from remotely sensed information using artificial neural networks–convolutional neural networks
M Sadeghi, AA Asanjan, M Faridzad… - Journal of …, 2019 - journals.ametsoc.org
Accurate and timely precipitation estimates are critical for monitoring and forecasting natural
disasters such as floods. Despite having high-resolution satellite information, precipitation …
disasters such as floods. Despite having high-resolution satellite information, precipitation …
Deeprain: Convlstm network for precipitation prediction using multichannel radar data
Accurate rainfall forecasting is critical because it has a great impact on people's social and
economic activities. Recent trends on various literatures show that Deep Learning (Neural …
economic activities. Recent trends on various literatures show that Deep Learning (Neural …
A spatiotemporal deep learning model ST-LSTM-SA for hourly rainfall forecasting using radar echo images
Accurate and timely short-term forecasting services of precipitation variable are significant
for people's lives and property security. The data-driven approaches demonstrate promising …
for people's lives and property security. The data-driven approaches demonstrate promising …
Rainfall prediction using machine learning models: literature survey
Research on rainfall prediction contributes to different fields that have a huge impact on our
daily life. With the advancement of computer technology, machine learning has been …
daily life. With the advancement of computer technology, machine learning has been …
A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars
In recent years, the number of weather-related disasters significantly increases across the
world. As a typical example, short-range extreme precipitation can cause severe flooding …
world. As a typical example, short-range extreme precipitation can cause severe flooding …
CEMA-LSTM: Enhancing contextual feature correlation for radar extrapolation using fine-grained echo datasets
Accurate precipitation nowcasting can provide great convenience to the public so they can
conduct corresponding arrangements in advance to deal with the possible impact of …
conduct corresponding arrangements in advance to deal with the possible impact of …
Microgrid-level energy management approach based on short-term forecasting of wind speed and solar irradiance
M Alhussein, SI Haider, K Aurangzeb - Energies, 2019 - mdpi.com
Background: The Distributed Energy Resources (DERs) are beneficial in reducing the
electricity bills of the end customers in a smart community by enabling them to generate …
electricity bills of the end customers in a smart community by enabling them to generate …
Context-aware attention LSTM network for flood prediction
Z Liu, W Xu, J Feng, S Palaiahnakote… - 2018 24th international …, 2018 - ieeexplore.ieee.org
To minimize the negative impacts brought by floods, researchers from pattern recognition
community utilize artificial intelligence based methods to solve the problem of flood …
community utilize artificial intelligence based methods to solve the problem of flood …
Monthly extended ocean predictions based on a convolutional neural network via the transfer learning method
Y Miao, X Zhang, Y Li, L Zhang… - Frontiers in Marine Science, 2023 - frontiersin.org
Sea surface temperature anomalies (SSTAs) and sea surface height anomalies (SSHAs) are
indispensable parts of scientific research, such as mesoscale eddy, current, ocean …
indispensable parts of scientific research, such as mesoscale eddy, current, ocean …
Location-Refining neural network: A new deep learning-based framework for Heavy Rainfall Forecast
Precipitation nowcasting aims to predict the rainfall distribution within a short-term period.
However, it pays the same attention to all locations instead of emphasizing those regions …
However, it pays the same attention to all locations instead of emphasizing those regions …