Investigation and Multivariate Models for Estimation of Surface Water Vapour Density in Ikeja, Nigeria

1*Zakariya Abdullahi, 2Davidson Odafe Akpootu, 2Musa Momoh, 3Lawal Sa’ad, 1Yusuf Ibrahim Bawa, 4Abubakar Muhammad Yabo, 5Ibrahim Nouhou, 2Emmanuel Oluwamayowa Agidi, 6Abdullahi Yusuf and 2Sunday Bongde Yohanna

1Department of Physics, Adamu Augie College of Education Argungu Kebbi State
2Deapartment of Physics, Usmanu Danfodiyo University Sokoto, Nigeria
3Department of Physics, Federal University Gusau
4College of Nursing Sciences Tambuwal
5Dėpartment de Physique, Facultė des Sciences et Techniques (FST), Universitė Dan Dicko Dankoulodo, Maradi Rėpublique du Niger
6Department of Science Education, Ibrahim Badamasi Babangida University, Lapai, Nigeria

*Corresponding author’s Email:,


Over a thirty-eight-year period (1979–2016), the study looked at the
relationships between monthly fluctuations in Surface Water Vapour Density (SWVD) and meteorological factors such temperature, relative humidity, surface pressure, cloud cover, and hours of sunshine in Ikeja on a monthly average. With two average months included in each season, the study examined the daily differences in the two different seasons’ surface water vapour densities in 2014. The results showed that during the course of the investigation, there were daily fluctuations in the density of surface water vapor. The highest and lowest SWVD values were found to be 20.9244 gm-3 and 16.0135 gm-3, respectively, in the months of June and January. The statistical indices of mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), Nash-Sutcliffe equation (NSE), coefficient of determination (R2), and index of agreement (IA) were used to compare the created SWVD-based models. To ascertain which of the three straightforward models of correlation using two variables was more appropriate for estimating surface water vapour density (SWVD) for the location being studied, the model that relates pressure and precipitable water vapour was adjudicated to be the most appropriate. The results obtained in this study will help hydrometeorologists to know the amount of SWVP in the studied location.


Statistical Indices,