Research Article | Open Access | Download Full Text
Volume 3 | Issue 1 | Year 2025 | Article Id: AIR-V3I1P101 DOI: https://doi.org/10.59232/AIR-V3I1P101
Rules-Based Expert System for Determining Groundwater Quality in Tropical Areas in Africa
Humberto Cuteso Matumueni
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 05 Jan 2025 | 01 Feb 2025 | 03 Mar 2025 | 28 Mar 2025 |
Citation
Humberto Cuteso Matumueni. “Rules-Based Expert System for Determining Groundwater Quality in Tropical Areas in Africa.” DS Journal of Artificial Intelligence and Robotics, vol. 3, no. 1, pp. 1-8, 2025.
Abstract
This article proposes a rule-based expert system to determine groundwater quality in tropical Africa, where communities rely heavily on groundwater for drinking, agriculture, and domestic use. In tropical regions, the consumption and use of unsafe water is common; it is a cause of disease, and communities face water-related health problems. The criteria and methods for determining water assessment parameters are based on international standards established by the World Health Organization, with particular attention paid to Class A parameters corresponding to drinking water quality. After compiling the acquired knowledge or parameters, a decision grid was developed to structure and simplify repetitive knowledge. Based on this knowledge, rules were created to provide a knowledge base for determining water quality. Implementing an expert system using Corvid Exys software to determine water quality in water quality decision-making in regions facing water-related health problems. A comparative test was conducted with Corvid Exsys software, and based on the results obtained, the experts validated the developed expert system. Finally, the results were evaluated using Cohen's Kappa coefficient to measure the reliability of the expert system. The results of this comparative study highlighted the usefulness of a rule-based expert system in determining groundwater quality and thus reducing the risk of emerging diseases in tropical areas of Africa. The results indicated good agreement with the experts' manual assessments and revealed areas at high risk of contamination.
Keywords
Artificial Intelligence, Expert system, Rule-based system, Groundwater, Exsys Corvid.
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