[
Abbas, M.Z., Abu Baker, K., Ayaz, M., Mohamed, H., Tariq, M., Ahmed, A. and Faheem, M. (2018). Key factors involved in pipeline monitoring techniques using robots and WSNs: Comprehensive survey. Journal of Pipeline Systems Engineering and Practice, 9(2), p.04018001.
]Search in Google Scholar
[
Adegboye, M.A., Fung, W.K. and Karnik, A. (2019). Recent advances in pipeline monitoring and oil leakage detection technologies: Principles and approaches. Sensors, 19(11), p.2548.
]Search in Google Scholar
[
Ali, H. and Choi, J.H. (2019). A review of underground pipeline leakage and sinkhole monitoring methods based on wireless sensor networking. Sustainability, 11(15), p.4007.
]Search in Google Scholar
[
Breiman, L. (2001). Random forests. Machine learning, 45(1), pp.5-32.
]Search in Google Scholar
[
Chai, T. and Draxler, R.R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), pp.1247-1250.
]Search in Google Scholar
[
Chijioke, A.N, (2016). NetNaiga. Retrieved September 12, 2016, from https://www.netnaija.com/forum/general/news/25353-buhari-set-visit-niger-delta-june-2-flag-clean-ogoni-land
]Search in Google Scholar
[
Fawagreh, K., Gaber, M.M. and Elyan, E. (2014). Random forests: from early developments to recent advancements. Systems Science & Control Engineering: An Open Access Journal, 2(1), pp.602-609.
]Search in Google Scholar
[
Ishwaran, H., Kogalur, U.B., Blackstone, E.H. and Lauer, M.S. (2008). Random survival forests. The annals of applied statistics, 2(3), pp.841-860.
]Search in Google Scholar
[
Kamel, M.A., Yu, X. and Zhang, Y. (2020). Formation control and coordination of multiple unmanned ground vehicles in normal and faulty situations: A review. Annual reviews in control, 49, pp.128-144.
]Search in Google Scholar
[
Kharrat, M., Ichchou, M.N., Bareille, O. and Zhou, W. (2014). Pipeline inspection using a torsional guided-waves inspection system. Part 2: Defect sizing by the wave finite element method. International Journal of Applied Mechanics, 6(04), p.1450035.
]Search in Google Scholar
[
Kim, J.H., Sharma, G., Boudriga, N., Iyengar, S.S. and Prabakar, N. (2015). Autonomous pipeline monitoring and maintenance system: a RFID-based approach. EURASIP Journal on Wireless Communications and Networking, 2015(1), pp.1-21.
]Search in Google Scholar
[
Lu, H., Iseley, T., Behbahani, S. and Fu, L. (2020). Leakage detection techniques for oil and gas pipelines: State-of-the-art. Tunnelling and Underground Space Technology, 98, p.103249.
]Search in Google Scholar
[
McCullagh, P. and Nelder, J.A. (2019). Generalized linear models. Routledge.
]Search in Google Scholar
[
Ognjanovski, G., (2019). Everything you need to know about neural networks and backpropagation—machine learning easy and fun. Towards Data Science.
]Search in Google Scholar
[
Osuagwu, E.S. and Olaifa, E. (2018). Effects of oil spills on fish production in the Niger Delta. PloS one, 13(10), p.e0205114.
]Search in Google Scholar
[
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-project.org/.
]Search in Google Scholar
[
Riskiawan, H.Y., Puspitasari, T.D., Hasanah, F.I., Wahyono, N.D. and Kurnianto, M.F. (2018). Identifying Cocoa ripeness using K-Nearest Neighbor (KNN) Method. In 2018 International Conference on Applied Science and Technology (iCAST) (pp. 354-357). IEEE.
]Search in Google Scholar
[
Tharin, J. (2016). Virtual Robot Experimentation Platform. Retrieved May 18, 2016,from http://www.coppeliarobotics.com/helpFiles/en/welcome.htm.
]Search in Google Scholar
[
Yinka-Banjo, C.O., Akinyemi, M.I., Nwadike, C.O., Misra, S., Oluranti J., Damasevicius, R. (2021). Unmanned Vehicle Model Through Markov Decision Process for Pipeline Inspection. In: Tripathi, M., Upadhyaya S. (eds), ICDLAIR 2019. Lecture Notes in Networks and Systems, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-030-67187-7_33
]Search in Google Scholar
[
Zhang, X.D. (2020) Neural Networks. In: A Matrix Algebra Approach to Artificial Intelligence. Springer, Singapore. https://doi.org/10.1007/978-981-15-2770-8_7
]Search in Google Scholar
