Version 1
: Received: 25 September 2023 / Approved: 26 September 2023 / Online: 28 September 2023 (08:38:03 CEST)
How to cite:
Ibeh, L.; Kouveliotis, K.; Mauser, W.; Unune, D.R.; Cuong, N.M.; Mutai, N.; Fountis, A.; Dr. Rostomyan, A.; Samoylenko, S.; Popoola, O.M. A Novel Approach for Integrating Community Knowledge into Fuzzy Logic-Adapted Spatial Modelling in the Analysis of Natural Resource Conflicts. Preprints2023, 2023091927. https://doi.org/10.20944/preprints202309.1927.v1
Ibeh, L.; Kouveliotis, K.; Mauser, W.; Unune, D.R.; Cuong, N.M.; Mutai, N.; Fountis, A.; Dr. Rostomyan, A.; Samoylenko, S.; Popoola, O.M. A Novel Approach for Integrating Community Knowledge into Fuzzy Logic-Adapted Spatial Modelling in the Analysis of Natural Resource Conflicts. Preprints 2023, 2023091927. https://doi.org/10.20944/preprints202309.1927.v1
Ibeh, L.; Kouveliotis, K.; Mauser, W.; Unune, D.R.; Cuong, N.M.; Mutai, N.; Fountis, A.; Dr. Rostomyan, A.; Samoylenko, S.; Popoola, O.M. A Novel Approach for Integrating Community Knowledge into Fuzzy Logic-Adapted Spatial Modelling in the Analysis of Natural Resource Conflicts. Preprints2023, 2023091927. https://doi.org/10.20944/preprints202309.1927.v1
APA Style
Ibeh, L., Kouveliotis, K., Mauser, W., Unune, D.R., Cuong, N.M., Mutai, N., Fountis, A., Dr. Rostomyan, A., Samoylenko, S., & Popoola, O.M. (2023). A Novel Approach for Integrating Community Knowledge into Fuzzy Logic-Adapted Spatial Modelling in the Analysis of Natural Resource Conflicts. Preprints. https://doi.org/10.20944/preprints202309.1927.v1
Chicago/Turabian Style
Ibeh, L., Svitlana Samoylenko and Olufunke Mercy Popoola. 2023 "A Novel Approach for Integrating Community Knowledge into Fuzzy Logic-Adapted Spatial Modelling in the Analysis of Natural Resource Conflicts" Preprints. https://doi.org/10.20944/preprints202309.1927.v1
Abstract
Resource conflicts represent a significant global challenge in regions abundant with natural re-sources. Modelling the myriad factors driving natural resource-based conflicts (NRBCs), spanning environmental, health, socio-economic, and political dimensions, is a complex endeavor exacer-bated by data scarcity. Furthermore, existing quantitative studies often focus solely on large-scale conflicts. This article introduces a novel algorithm, the Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management (SEFLAME-CM), which integrates the local knowledge of stakeholders into spatial decision-making technologies to support sustainable peace efforts. The results are validated with spatial multi-criteria evaluation (SMCE) using spatial statistics. The Moran’s I scatter plots for the overall conflicts reveal significant values of 0.99 and 0.98 for both the SEFLAME-CM and SMCE, respectively, with significant spatial autocorrelation. While there re-mains room for improvement in enhancing the model's quality, SEFLAME-CM demonstrates its capacity to transparently model complex real-world problems. The findings underscore the im-perative for a holistic approach to addressing environmental degradation, socio-economic, and political drivers of resource conflicts at the community level. Our paper demonstrates the signif-icance of spatial information technologies and knowledge exchange between experts and local stakeholders in effectively managing resource conflicts. These insights should inform national policies and international interventions, ensuring that the complex underlying issues are ad-dressed while prioritizing the knowledge and needs of affected communities.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
,
