Crop yield prediction integrating genotype and weather variables using deep learning
Accurate prediction of crop yield supported by scientific and domain-relevant insights, is
useful to improve agricultural breeding, provide monitoring across diverse climatic …
useful to improve agricultural breeding, provide monitoring across diverse climatic …
A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction
Accurate air pollutant prediction allows effective environment management to reduce the
impact of pollution and prevent pollution incidents. Existing studies of air pollutant prediction …
impact of pollution and prevent pollution incidents. Existing studies of air pollutant prediction …
Spatiotemporal attention for multivariate time series prediction and interpretation
Multivariate time series modeling and prediction problems are abundant in many machine
learning application domains. Accurate interpretation of the prediction outcomes from the …
learning application domains. Accurate interpretation of the prediction outcomes from the …
Land subsidence prediction using recurrent neural networks
In an environment, one of the natural geological hazards is land surface subsidence.
Underground mining and subsurface coal fires are primarily responsible for subsidence of …
Underground mining and subsurface coal fires are primarily responsible for subsidence of …
Lower limb kinematics trajectory prediction using long short-term memory neural networks
This study determined whether the kinematics of lower limb trajectories during walking could
be extrapolated using long short-term memory (LSTM) neural networks. It was hypothesised …
be extrapolated using long short-term memory (LSTM) neural networks. It was hypothesised …
An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth
Multi-step-ahead prediction is considered of major significance for time series analysis in
many real life problems. Existing methods mainly focus on one-step-ahead forecasting …
many real life problems. Existing methods mainly focus on one-step-ahead forecasting …
A hybrid deep learning-based approach for optimal genotype by environment selection
Z Khalilzadeh, M Kashanian, S Khaki… - Frontiers in Artificial …, 2024 - frontiersin.org
The ability to accurately predict the yields of different crop genotypes in response to weather
variability is crucial for developing climate resilient crop cultivars. Genotype-environment …
variability is crucial for developing climate resilient crop cultivars. Genotype-environment …
Velocity prediction based on map data for optimal control of electrified vehicles using recurrent neural networks (lstm)
F Deufel, P Jhaveri, M Harter, M Gießler, F Gauterin - Vehicles, 2022 - mdpi.com
In order to improve the efficiency of electrified vehicle drives, various predictive energy
management strategies (driving strategies) have been developed. This article presents the …
management strategies (driving strategies) have been developed. This article presents the …
Machine learning for accurate methane concentration predictions: Short-term training, long-term results
R Luo, J Wang, I Gates - Environmental Research …, 2023 - iopscience.iop.org
Although methane emissions from Alberta's oil and gas sector have decreased in recent
years, monitoring these emissions using Continuous Emission Monitoring Systems (CEMS) …
years, monitoring these emissions using Continuous Emission Monitoring Systems (CEMS) …
[PDF][PDF] The hidden-layers topology analysis of deep learning models in survey for forecasting and generation of the wind power and photovoltaic energy
D Xu, H Shao, X Deng, X Wang - CMES—Computer Modeling in …, 2022 - cdn.techscience.cn
As wind and photovoltaic energy become more prevalent, the optimization of power systems
is becoming increasingly crucial. The current state of research in renewable generation and …
is becoming increasingly crucial. The current state of research in renewable generation and …