ASSESSMENT OF THE RELATIONSHIP BETWEEN URBAN GROWTH AND SURFACE TEMPERATURE IN ABUJA MUNICIPAL AREA COUNCIL
Keywords:
Land Surface Temperature, Land Use Land Cover, Relationship, Satellite Imageries, Urban GrowthAbstract
AMAC experienced growth in built-up areas during the past 30 years. The influx of people brought modification in Land Use Land Cover leading to expansion in urban area and conversion from one LULC to another. This has implications on climate of the area. This study assessed urban growth effects on Land Surface Temperature (LST) of AMAC from 1986 to 2016, using remote sensing and Geographic Information System (GIS).ERDAS Imagine 2014 and ArcGIS 10.4.1 softwares were used for processing and classification of the multi-date (1986, 2001 and 2016) satellite imageries. It also used LST data derived from Landsat imageries as well as rate of change in LULC. Simple linear regression was used to establish relationship between urban growth and LST. Results show that the period between 1986 and 2016 witnessed changes in LULC as bare surface increased by 24.28%, built-up areas by 16.43%. However, vegetation decreased by 40.46%, water body by 0.06 % and rocks by 0.19%. The implication of urban growth on LST is an increase in mean LST of built-up areas to27oC, 33oC and 36oC for 1986, 2001 and 2016 respectively with the highest value at city centre due to sparse vegetal cover. LST also increased across different LULC during the three epoch years. In the relationship between LST and urban growth, LST and NDBI revealed strong relationship with coefficient of determination (R²) of 0.9610 for 1986; 0.9576 for 2001and 0.9732 for 2016. These results call for implementation of policies to control rapid urban growth and preserve vegetal covers
References
Al-Barrak, M. A., & Al-Razgan, M. S. (2015). Predicting Students' performance Through Classification: A Case Study. Journal of Theoretical & Applied Information Technology, 75(2).
Aliyu, S., Musa, H., & Jauro, F. (2018). Performance comparison of two Decision tree algorithms based on splitting criteria for predicting child birth delivery type. Paper presented at the 1st International Conference on Education and Development, Baze University, Abuja-Nigeria.
Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Zeducational data to predict Student’s academic performance using ensemble methods. International Journal of Database Theory and Application, 9(8), 119-136.
Anuradha, C., & Velmurugan, T. (2015). A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian Journal of Science and Technology, 8(15).
Araque, F., Roldán, C., & Salguero, A. (2009). Factors influencing university drop out rates. Computers & Education, 53(3), 563-574.
Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education (IJAIED), 13, 159-172.
Cole, J., & Foster, H. (2007). Using Moodle: Teaching with the popular open source course management system: " O'Reilly Media, Inc.".
Considine, G., & Zappalà, G. (2002). The influence of social and economic disadvantage in the academic performance of school students in Australia. Journal of sociology, 38(2), 129-148.
ElGamal, A. (2013). An educational data mining model for predicting student performance in programming course. International Journal of Computer Applications, 70(17), 22-28.
Kuznar, D., & Gams, M. (2016). Metis: system for early detection and prevention of student failure. Paper presented at the 6thInternational Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016).
Pandey, U. K., & Pal, S. (2011). Data Mining: A prediction of performer or underperformer using classification. arXiv preprint arXiv:1104.4163.
Patidar, P., Dangra, J., & Rawar, M. (2015). Decision tree C4. 5 algorithm and its enhanced approach for educational data mining. Engineering Universe for Scientific Research and Management, 7(2).
Prasetyawan, P., & Abadi, F. (2017). Application Development of Student's Graduation Classification Model based on The First 2 Years Performance using K-Nearest Neighbor. Paper presented at the International Conference on Engineering and Technology Development (ICETD).
Quinlan, J. R. (1979). Induction over Large Data Bases. Retrieved from
Raga Jr, R. C., & Raga, J. D. (2017). Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data. International Journal of Computing Sciences Research, 1(3), 1-16.
Raschka, S. (2014). Naive bayes and text classification i-introduction and theory. arXiv preprint arXiv:1410.5329.
Rice, W. (2006). Moodle e-learning course development. a complete guide to successful learning using moodleQ3. Packt Publ, 13.
Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.
Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.
Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.
Tair, M. M. A., & El-Halees, A. M. (2012). Mining educational data to improve students' performance: a case study. International Journal of Information, 2(2), 140-146.
Wang, W., Li, Y., Wang, X., Liu, J., & Zhang, X. (2018). Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers. Future Generation Computer Systems, 78, 987-994.
Yadav, S. K., & Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. arXiv preprint arXiv:1203.3832.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences