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Recent advances in deep learning models: a systematic literature review
In recent years, deep learning has evolved as a rapidly growing and stimulating field of
machine learning and has redefined state-of-the-art performances in a variety of …
machine learning and has redefined state-of-the-art performances in a variety of …
Hybrid forecasting methods—a systematic review
Time series forecasting has been performed for decades in both science and industry. The
forecasting models have evolved steadily over time. Statistical methods have been used for …
forecasting models have evolved steadily over time. Statistical methods have been used for …
[HTML][HTML] An attention-aware LSTM model for soil moisture and soil temperature prediction
Accurate prediction of soil moisture (SM) and soil temperature (ST) plays an important role in
Earth system science, hel** to forecast and understand ecosystem changes. They present …
Earth system science, hel** to forecast and understand ecosystem changes. They present …
[HTML][HTML] Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning
The skillful soil moisture (SM) for the Soil Moisture Active Passive (SMAP) L4 product can
provide substantial value for many practical applications including ecosystem management …
provide substantial value for many practical applications including ecosystem management …
Occupant-centric HVAC and window control: A reinforcement learning model for enhancing indoor thermal comfort and energy efficiency
X Liu, Z Gou - Building and Environment, 2024 - Elsevier
Occupant behavior plays a crucial role in enhancing indoor thermal comfort and achieving
energy efficiency by influencing the operational modes of Heating, Ventilation, and Air …
energy efficiency by influencing the operational modes of Heating, Ventilation, and Air …
[HTML][HTML] Improving soil moisture prediction using a novel encoder-decoder model with residual learning
The skillful prediction of soil moisture can provide much help for many practical applications
including ecosystem management and precision agriculture. It presents great challenges …
including ecosystem management and precision agriculture. It presents great challenges …
Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches
Atmospheric pressure (AP), which is an indicator of weather events, plays an important role
in climatology, agriculture, meteorology, atmospheric and environmental science, human …
in climatology, agriculture, meteorology, atmospheric and environmental science, human …
[HTML][HTML] LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction
The advancements in deep learning methods have presented new opportunities and
challenges for predicting land surface variables (LSVs) due to their similarity with computer …
challenges for predicting land surface variables (LSVs) due to their similarity with computer …
Prediction of mechanical behavior of rocks with strong strain-softening effects by a deep-learning approach
LL Shi, J Zhang, QZ Zhu, HH Sun - Computers and Geotechnics, 2022 - Elsevier
Rock materials exhibit various mechanical characteristics, and it is difficult to describe the
strain–stress relation with strong strain-softening behavior by a single constitutive law. In the …
strain–stress relation with strong strain-softening behavior by a single constitutive law. In the …
[HTML][HTML] Enhancing soil moisture forecasting accuracy with REDF-LSTM: Integrating residual en-decoding and feature attention mechanisms
X Li, Z Zhang, Q Li, J Zhu - Water, 2024 - mdpi.com
This study introduces an innovative deep learning model, Residual-EnDecode-Feedforward
Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the …
Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the …