A Daily Reference Crop Evapotranspiration Forecasting Model Based on Improved Informer DOI Creative Commons

Jiangjie Pan,

Long Yu, Bo Zhou

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(9), P. 933 - 933

Published: April 25, 2025

Daily reference crop evapotranspiration (ET0) is crucial for precision irrigation management, yet traditional prediction methods struggle to capture its dynamic variations due the complexity and nonlinearity of meteorological conditions. To address this, we propose an Improved Informer model enhance ET0 accuracy, providing a scientific basis agricultural water management. Using soil data from Yingde region, employed Maximal Information Coefficient (MIC) identify key influencing factors integrated Residual Cycle Forecasting (RCF), Star Aggregate Redistribute (STAR), Fully Adaptive Normalization (FAN) techniques into model. MIC analysis identified total shortwave radiation, sunshine duration, maximum temperature at 2 m, 28–100 cm depth, surface pressure as optimal features. Under five-feature scenario (S3), improved achieved superior performance compared Long Short-Term Memory (LSTM) original models, with MAE reduced 0.065 (LSTM: 0.637, Informer: 0.171) MSE 0.007 0.678, 0.060). The inference time was also by 31%, highlighting enhanced computational efficiency. effectively captures periodic nonlinear characteristics ET0, offering novel solution management significant practical implications.

Language: Английский

Estimation of CO2 Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach DOI Creative Commons
Seval Ene Yalçın

Systems, Journal Year: 2025, Volume and Issue: 13(3), P. 194 - 194

Published: March 11, 2025

This study focuses on estimating transportation system-related emissions in CO2 eq., considering several socioeconomic and energy- transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, deep learning algorithms. case of Turkey was considered as an example. Model performance evaluated using a dataset Turkey, future projections were made based scenario analysis compatible with Turkey’s climate change mitigation strategies. also adopted type-based analysis, exploring the role road, air, marine, rail systems. findings this indicate that aforementioned models can be effectively implemented to predict transport emissions, concluding they have valuable practical applications field.

Language: Английский

Citations

0
PRISMA-Guided Systematic Review on the Adoption of Artificial Intelligence and Embedded Systems for Smart Irrigation DOI
Nisrine Lachgar, Hajar Saikouk, Moad Essabbar

et al.

Pure and Applied Geophysics, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Language: Английский

Citations

0
A Daily Reference Crop Evapotranspiration Forecasting Model Based on Improved Informer DOI Creative Commons

Jiangjie Pan,

Long Yu, Bo Zhou

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(9), P. 933 - 933

Published: April 25, 2025

Daily reference crop evapotranspiration (ET0) is crucial for precision irrigation management, yet traditional prediction methods struggle to capture its dynamic variations due the complexity and nonlinearity of meteorological conditions. To address this, we propose an Improved Informer model enhance ET0 accuracy, providing a scientific basis agricultural water management. Using soil data from Yingde region, employed Maximal Information Coefficient (MIC) identify key influencing factors integrated Residual Cycle Forecasting (RCF), Star Aggregate Redistribute (STAR), Fully Adaptive Normalization (FAN) techniques into model. MIC analysis identified total shortwave radiation, sunshine duration, maximum temperature at 2 m, 28–100 cm depth, surface pressure as optimal features. Under five-feature scenario (S3), improved achieved superior performance compared Long Short-Term Memory (LSTM) original models, with MAE reduced 0.065 (LSTM: 0.637, Informer: 0.171) MSE 0.007 0.678, 0.060). The inference time was also by 31%, highlighting enhanced computational efficiency. effectively captures periodic nonlinear characteristics ET0, offering novel solution management significant practical implications.

Language: Английский

Citations

0