This study trained six machine learning models to predict meteorological variables at a tropical location. The models used are: Multiple linear regression, Decision tree, Random forest, Support vector machine, Extreme gradient boosting and Multilayer perceptron. This was with the...
Open access
Research Article10.9734/ijecc/2024/v14i13829
In this study, an Eddy Covariance (EC) system was deployed to measure the turbulent heat fluxes (sensible and latent heat fluxes) at a farmland located at Obafemi Awolowo University, Ile-Ife, Nigeria. The period of measurement was between 2016 and 2019. The surface of the measure...
Open access
Research Article10.9734/ijecc/2023/v13i81989
This study observed six meteorological variables at a tropical location from 2016 to 2019. The measurement was performed at the meteorological station (7.55 0N; 4.56 0E) of Obafemi Awolowo University, Ile-Ife, Nigeria. The variables that were measured are: incoming solar radiatio...
Open access
Research Article10.9734/air/2022/v23i430340
The accurate estimation of sensible heat flux (Hs) is imperative in the exchange of energy and mass between the earth’s systems. However, the process of acquiring the sensible heat flux data is challenging due to the complexity in its direct measurement hence, not readily availab...
Open access
Research Article10.9734/air/2022/v23i430338
This study estimated the levels of atmospheric turbidity in Ile-Ife, a tropical location in the Southwest of Nigeria, from November, 2017 to March, 2019. This was with the aim to quantify the degree of atmospheric cleanliness of the study location. The methods of estimation used...
Open access
Research Article10.9734/psij/2021/v25i730270
Aim: To quantify the atmospheric aerosol loading in order to predict the severity and accompanying consequences of aerosols at a tropical location in Ile-Ife, southwest Nigeria. Place and Duration of Study: Department of Physics and Engineering Physics, Obafemi Awolowo University...
Open access
Research Article10.9734/ijecc/2021/v11i630431