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• W2949109675 abstract "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." @default.
• W2949109675 created "2019-06-27" @default.
• W2949109675 creator A5046964093 @default.
• W2949109675 creator A5085899058 @default.
• W2949109675 date "2018-09-19" @default.
• W2949109675 modified "2023-10-14" @default.
• W2949109675 title "Towards the Development of a Rule-based Drought Early Warning Expert Systems using Indigenous Knowledge" @default.
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• W2949109675 doi "https://doi.org/10.48550/arxiv.1809.08101" @default.
• W2949109675 hasPublicationYear "2018" @default.
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