Assessing the Impact of Vegetation Cover on Total Column Ozone Over West Africa

Ogunjo, Samuel; Fuwape, I. A.; Rabiu, Babatunde; Oluyamo, S. S.; Owoola, Eunice Oluwabunmi

doi:10.1007/978-3-030-01440-7_54

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dust system, driven by large scale oscillations reaches the southern parts of the country (Anuforom, 2007 ) and transported as far as the Amazon basin in South America (Koren et al., 2006 ). The weather dynamics of the southern part is driven largely by the Atlantic ocean (Ogunjo et al., 2019 ). This is largely responsible for the low temperature range throughout the year (Eludoyin et al., 2014 ).…”

Section: Methodsmentioning

confidence: 99%

Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach

2022

View full text Add to dashboard Cite

The dynamical nature of COVID‐19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID‐19 cases based on past infections, (b) predict current COVID‐19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K‐nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k‐nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID‐19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID‐19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID‐19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control.

show abstract

Section: Methodsmentioning

confidence: 99%

Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach

2022

View full text Add to dashboard Cite

show abstract

“…Ogunjo (2021) showed that droughts in specific locations within Nigeria are multi‐fractal. Furthermore, it has been found that large‐scale oscillations also drive droughts within West Africa (Ogunjo et al ., 2019b). Indices for estimating hydrological droughts are used to quantify the degree and severity of these particular drought events.…”

Section: Introductionmentioning

confidence: 99%

Multidecadal trend analysis of hydrological drought along River Niger using the Streamflow Drought Index

Ogunjo

Olusola

Durowoju

2023

Singap J Trop Geogr

View full text Add to dashboard Cite

Droughts affect human well‐being and the economy of countries across the world. Understanding the long‐term evolution of droughts within a particular region will help in drought mitigation and adaptation plans, thereby reducing drought impact on the environment. This study examined the multidecadal trends in hydrological droughts at two stations along River Niger using 3‐month, 6‐month, and annual time scales. Hydrological drought events were identified using the streamflow drought index (SDI) between 1915 and 1990 based on the Theil‐Sen slope and Mann‐Kendall approaches. Across the timescales, extreme and severe droughts occurred in 1982/84 and 1983/84 with ‐2 ≤ Sd < ‐1.5. On an annual scale, the results from the annual SDI further showed that the 7th and 8th decades (1971−1990) recorded more drought events of varying degrees ranging from mild to extreme drought in both stations than in other decades. The last two decades (7th and 8th) further revealed the most extended hydrological drought duration from 1974/75 to 1988/89 for Baro and from 1979/80 to 1988/89 for Lokoja. The highest severity recorded at Baro was ‐15.56 and ‐14.26 at Lokoja. The prolonged duration of drought events across the stations and their associated yearly intensities suggests that more proactive measures are needed to ameliorate the hydrological drought impact in the study area.

show abstract