Advancing water demand management: predictive analytics using convolutional neural networks and developed maritime search and rescue algorithm based on the shared socioeconomic pathways DOI Creative Commons

Yiheng Lan,

Wenhao Luo, Manli Yang

et al.

International Journal of Low-Carbon Technologies, Journal Year: 2025, Volume and Issue: 20, P. 724 - 734

Published: Jan. 1, 2025

Abstract Efficient management of water resources is crucial based on the idea developing socioeconomic conditions. To achieve this, it essential to forecast demand accurately. This investigation introduces a predictive framework that utilizes convolutional neural network-based Xception model, which has been optimized through developed maritime search and rescue algorithm increase accuracy in forecasting future trends under shared pathway scenarios. The enhanced model uses pathways evaluate potential effects growth domestic industry demand. Policymakers managers can benefit from findings this investigation, as provides insights into needs. information help making informed decisions planning for sustainable resource management, even presence uncertainty variability. study’s results enable better understanding patterns.

Language: Английский

Capsule neural network and adapted golden search optimizer based forest fire and smoke detection DOI Creative Commons

Luling Liu,

Li Chen, Mehdi Asadi

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 4, 2025

Forest fires represent a major risk to both ecosystems and human health that rising frequency of it exacerbates global warming. This study introduces an innovative methodology for detecting forest smoke using enhanced capsule neural network (CNN) together with adapted golden search optimizer (AGSO). By advanced deep learning optimization strategies, the method effectively identifies complex patterns linked wildfires. Testing this model on wildfire imagery BowFire dataset reveals proposed outperformed traditional feature selection classification methods. The integration modified CNN AGSO facilitated rapid response mitigation efforts, enhancing accuracy dependability fire identification. research highlights importance computational techniques in reducing risks, ensuring safety, progressing automatic detection systems. combination networks illustrates potential merging cutting-edge technologies tackle intricate environmental issues efficiently.

Language: Английский

Citations

0
Advancing water demand management: predictive analytics using convolutional neural networks and developed maritime search and rescue algorithm based on the shared socioeconomic pathways DOI Creative Commons

Yiheng Lan,

Wenhao Luo, Manli Yang

et al.

International Journal of Low-Carbon Technologies, Journal Year: 2025, Volume and Issue: 20, P. 724 - 734

Published: Jan. 1, 2025

Abstract Efficient management of water resources is crucial based on the idea developing socioeconomic conditions. To achieve this, it essential to forecast demand accurately. This investigation introduces a predictive framework that utilizes convolutional neural network-based Xception model, which has been optimized through developed maritime search and rescue algorithm increase accuracy in forecasting future trends under shared pathway scenarios. The enhanced model uses pathways evaluate potential effects growth domestic industry demand. Policymakers managers can benefit from findings this investigation, as provides insights into needs. information help making informed decisions planning for sustainable resource management, even presence uncertainty variability. study’s results enable better understanding patterns.

Language: Английский

Citations

0