About this document

LSTM Model for PM2.5 Prediction in Nigeria by Damilola Oni is a document available to read on EtoBox.

This research paper presents the development of a Long Short-Term Memory (LSTM) model to predict PM2.5 concentrations in Nigeria, addressing the significant public health concerns related to air quality. The study highlights the effectiveness of LSTM in capturing complex temporal dependencies in air quality data, demonstrating improved prediction accuracy compared to traditional models. The methodology includes data collection from monitoring stations, preprocessing steps, and the training of the LSTM model

Author

Damilola Oni

Language

EN