Blue Print for Green Revolution in South Eastern River Basin, Nigeria using Bayesian and Game Models
 |
International Journal of Recent Engineering Science (IJRES) |  |
| © 2024 by IJRES Journal | ||
| Volume-11 Issue-5 |
||
| Year of Publication : 2024 | ||
| Authors : Anthony N. Ezemerihe |
||
| DOI : 10.14445/23497157/IJRES-V11I5P115 |
How to Cite?
Anthony N. Ezemerihe, "Blue Print for Green Revolution in South Eastern River Basin, Nigeria using Bayesian and Game Models ," International Journal of Recent Engineering Science, vol. 11, no. 5, pp. 148-165, 2024. Crossref, https://doi.org/10.14445/23497157/IJRES-V11I5P115
Abstract
The study aimed to develop a Blueprint for the Green Revolution in the south-eastern basin of Nigeria, using Bayesian and Game Theory models as climate variability solutions. The objectives were to use multipurpose/multi-objective capital projects to develop a blueprint for a green revolution at the river basin. The methodology uses Bayesian and Game decision theories based on the Bill of Engineering Measurement and Evaluation (BEME) data. The result shows that the optimal solution from the Bayesian Model analysis of the Maximum Expected Monetary Value (EMV*) was N68.72 billion. The optimal strategies for the Game theory were a maximum benefit of N69.02 billion, which is N0.30 billion higher than the result obtained from the Bayesian analysis. The amount of N12.504 billion released to the south-eastern Nigeria river basin for the period was deducted from the revenue generated from Bayesian EMV* (N68.72 billion), and optimal strategies of Game (N69.022 billion), then N56.22 billion and N56.52 billion respectively emerged which were the profit margins for the investment. The work concluded that since many uncertainties in climate change projections impact the ecosystem, optimal strategies should incorporate delivering benefits irrespective of climate conditions. It was recommended that status assessment, understanding the assumption made, long term consistent monitoring of data, long term effectiveness and cost efficiency, certainty in climate priority and posteriority predictions and logical cost sharing would assist in the use of green and clean energy sources for project development at the river basin. This would generate revenue and enhance social wellbeing for communities of the region. The allocation of money released for these ten development projects resulted in optimal benefits. The Bayesian and Game optimization offer an alternative solution for developing the blueprint for a green revolution at the river basin.
Keywords
Bayesian theory, Blueprint, Game theory, Green revolution, Optimal benefits.
Reference
[1] I.B. Abaje et al., “Climate Change Impact and Adaptation Framework for Rural Communities in Northern Nigeria,” Journal of Research in National Development, vol. 15, no. 2, pp. 142-150, 2017.
[Google Scholar]
[2] R.N.C. Anyadike, Statistical Methods for Social and Environmental Sciences, Ibadan: Spectrum Books, 2009.
[Google Scholar]
[3] B.U. Anyata, “System Analysis and Numerical Methods in Civil Engineering, University of Benin City, Nigeria,” 2009.
[Google Scholar]
[4] K.R. Arora, Irrigation, Water Power and Water Resources Engineering, Standard Publisher Distributors, 2002.
[Google Scholar]
[5] C.J. Barrow, “Dei^Loggient Planjj|ng and!,” Management World Development, vol. 26, no.1, 1998.
[Google Scholar]
[6] Bryson Bates et al., “Climate Change and Water,” IPCC Technical Paper VI, 2008.
[Google Scholar] [Publisher Link]
[7] Bukar Abba Gana et al., “Stream Ordering as a Tool for Effective River Basin Development: Example Form Komadugu-Yobe River Basin, Nigeria,” Journal of Resources Development and Management, vol. 44, pp. 67-78, 2018.
[Google Scholar] [Publisher Link]
[8] William J. Cosgrove, and Daniel P. Loucks, “Water Management: Current and Future Challenges and Research Directions,” Water Resources Research, vol. 51, no. 6, pp. 4823-4839, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Bithin Data, and V. Harikrisha, “Optimization Applications in Water Resources Engineering,” Research Journal of IIT Kanpur, pp. 57 63, 2005.
[Google Scholar]
[10] Eme Luke Chika, and Ohaji Evans, “Simulation Modeling in Prior-Posterior Decision Theory- A Case Study of Farmers’ Decision Problem,” Civil and Environmental Research, vol. 11, no. 2, pp. 61-68, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Eme Luke Chika, and Ohaji Evans, “Bayesian Decision Modeling in Watershed Management - Cross River Basin, Nigeria,” Civil and Environmental Research, vol. 11, no. 2, pp. 105-120, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Luke C. Eme, “Simulation Modeling in Markovian Decision Theory in Multi-Purpose/Multi-objective River Basin Development Planning and Management,” Doctorial Dissertation, Anambra State University, Uli-Nigeria, 2012.
[Google Scholar]
[13] Luke C. Eme, “Infinite Stage Simulation Model Optimization Solution using Exhaustive Enumeration Method for the Anambra/Imo River Basin Engineering Development Scheme, Nigeria,” American Academic & Scholarly Research Journal, vol. 7, no. 4, pp. 86-90, 2015.
[Google Scholar] [Publisher Link]
[14] Luke C. Eme, “Simulation Optimization for Model and Prototype using Non-Parametric Method: A Case Study of Anambra/Imo River Basin Engineering Development Scheme,” American Academic & Scholarly Research Journal, vol. 7, no. 4, pp. 63-70, 2015.
[Google Scholar] [Publisher Link]
[15] Ezenweani Raymond Sunday, “A Comprehensive Collation of River Basins in Nigeria: Benefits and River Basin Development Planning and Management,” International Journal of Scientific & Engineering Research, vol. 8, no. 11, pp. 1587-1595, 2017.
[Google Scholar] [Publisher Link]
[16] Michael T. Klare, “The New Geography of Conflict,” Foreign Affairs, vol. 80, 2001.
[Google Scholar] [Publisher Link]
[17] Francois Molle, Development Trajectories of River Basins: A Conceptual Framework, International Water Management Institute, 2003.
[Google Scholar] [Publisher Link]