Towards the Development of a Rule-based Drought Early Warning Expert Systems using Indigenous Knowledge
This addresses drought prediction challenges for local communities by incorporating indigenous knowledge, though it is incremental as it applies existing expert system methods to a new domain.
The paper tackles drought forecasting by developing a rule-based expert system (RB-DEWES) that uses local indigenous knowledge from domain experts, generating drought advisories with certainty factors based on user inputs.
Drought forecasting and prediction is a complicated process due to the complexity and scalability of the environmental parameters involved. Hence, it required a high level of expertise to predict. In this paper, we describe the research and development of a rule-based drought early warning expert systems (RB-DEWES) for forecasting drought using local indigenous knowledge obtained from domain experts. The system generates inference by using rule set and provides drought advisory information with attributed certainty factor (CF) based on the user's input. The system is believed to be the first expert system for drought forecasting to use local indigenous knowledge on drought. The architecture and components such as knowledge base, JESS inference engine and model base of the system and their functions are presented.