Towards the Development of a Rule-Based Drought Early Warning Expert Systems Using Indigenous Knowledge

Autor:	Adeyinka K. Akanbi, Muthoni Masinde
Rok vydání:	2018
Předmět:	Warning system Computer science business.industry Knowledge engineering Inference Rule-based system computer.software_genre Machine learning Expert system Knowledge base Artificial intelligence Inference engine Traditional knowledge business computer
Zdroj:	2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD).
DOI:	10.1109/icabcd.2018.8465465
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b5112ae559e148fa62a813bfa51c43d9 https://doi.org/10.1109/icabcd.2018.8465465 Zobrazit plný text záznamu