Back to Search
Start Over
An enhanced loss function in deep learning model to predict PM2.5 in India.
- Source :
- Intelligent Decision Technologies; 2023, Vol. 17 Issue 2, p363-376, 14p
- Publication Year :
- 2023
-
Abstract
- Fine particulate matter (PM 2.5) is one of the major air pollutants and is an important parameter for measuring air quality levels. High concentrations of PM 2.5 show its impact on human health, the environment, and climate change. An accurate prediction of fine particulate matter (PM 2.5) is significant to air pollution detection, environmental management, human health, and social development. The primary approach is to boost the forecast performance by reducing the error in the deep learning model. So, there is a need to propose an enhanced loss function (ELF) to decrease the error and improve the accurate prediction of daily PM 2.5 concentrations. This paper proposes the ELF in CTLSTM (Chi-Square test Long Short Term Memory) to improve the PM 2.5 forecast. The ELF in the CTLSTM model gives more accurate results than the standard forecast models and other state-of-the-art deep learning techniques. The proposed ELFCTLSTM reduces the prediction error of by a maximum of 10 to 25 percent than the state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
LONG-term memory
SHORT-term memory
AIR pollutants
PARTICULATE matter
Subjects
Details
- Language :
- English
- ISSN :
- 18724981
- Volume :
- 17
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Intelligent Decision Technologies
- Publication Type :
- Academic Journal
- Accession number :
- 164007700
- Full Text :
- https://doi.org/10.3233/IDT-220111