Recent advances in deep learning models: a systematic literature review

R Malhotra, P Singh - Multimedia Tools and Applications, 2023 - Springer
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 …

Hybrid forecasting methods—a systematic review

LB Sina, CA Secco, M Blazevic, K Nazemi - Electronics, 2023 - mdpi.com
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 …

[HTML][HTML] An attention-aware LSTM model for soil moisture and soil temperature prediction

Q Li, Y Zhu, W Shangguan, X Wang, L Li, F Yu - Geoderma, 2022 - Elsevier
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 …

[HTML][HTML] Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning

Q Li, Z Wang, W Shangguan, L Li, Y Yao, F Yu - Journal of Hydrology, 2021 - Elsevier
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 …

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 …

[HTML][HTML] Improving soil moisture prediction using a novel encoder-decoder model with residual learning

Q Li, Z Li, W Shangguan, X Wang, L Li, F Yu - Computers and Electronics in …, 2022 - Elsevier
The skillful prediction of soil moisture can provide much help for many practical applications
including ecosystem management and precision agriculture. It presents great challenges …

Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches

M Bilgili, A Ilhan, Ş Ünal - Neural Computing and Applications, 2022 - Springer
Atmospheric pressure (AP), which is an indicator of weather events, plays an important role
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

Q Li, C Zhang, W Shangguan, Z Wei, H Yuan… - Expert Systems with …, 2024 - Elsevier
The advancements in deep learning methods have presented new opportunities and
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 …

[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 …