Towards the Development of a Rule-based Drought Early Warning Expert Systems using Indigenous Knowledge
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Prediction and Forecast of Short-Term Drought Impacts Using Machine Learning to Support Mitigation and Adaptation Efforts
Drought is a complex natural hazard that affects ecological and human systems, often resulting in substantial environmental and economic losses. Recent increases in drought severity, frequency, and duration underscore th…
Development of Semantics-Based Distributed Middleware for Heterogeneous Data Integration and its Application for Drought
Drought is a complex environmental phenomenon that affects millions of people and communities all over the globe and is too elusive to be accurately predicted. This is mostly due to the scalability and variability of the…
Data IntegrationTowards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting
In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through de…
Deep learning for satellite image forecasting of vegetation greenness
The advent of abundant Earth observation data enables the development of novel predictive methods for forecasting climate impacts on the state and health of terrestrial ecosystems. Here, we target the spatial and tempora…
Deep LearningEarth ObservationEarth Surface ForecastingVideo PredictionQuantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter
Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impa…
Management