Main Article Content

Using fuzzy cognitive maps in modelling and representing weather lore for seasonal weather forecasting over East and Southern Africa

Solomon Mwanjele Mwagha
Muthoni Masinde


The creation of scientific weather forecasts is troubled by many technological challenges while their utilization is dismal. Consequently, the majority of small-scale farmers in Africa continue to consult weather lore to reach various cropping decisions. Weather lore is a body of informal folklore associated with the prediction of the weather based on indigenous knowledge and human observation of the environment. As such, it tends to be more holistic and more localized to the farmers’ context. However, weather lore has limitations such as inability to offer forecasts beyond a season. Different types of weather lore exist and utilize almost all available human senses (feel, smell, sight and hear). Out of all the types of weather lore in existence, it is the visual or observed weather lore that is mostly used by indigenous societies to come up with weather predictions. Further, meteorologists continue to treat weather lore knowledge as superstition partly because there is no means to scientifically evaluate and validate it. The visualization and characterization of visual sky objects (such as moon, clouds, stars, rainbow, etc) in forecasting weather is a significant subject of research. In order to realize the integration of visual weather lore knowledge in modern weather forecasting systems, there is a need to represent and scientifically substantiate weather lore. This article is aimed at coming up with a method of organizing the weather lore from the visual perspective of humans. To achieve this objective, we used fuzzy cognitive mapping to model and represent causal relationships between weather lore concepts and weather outcomes. The results demonstrated that FCMs are efficient for matrix representation of selected weather outcome scenarios caused visual weather lore concepts. Based on these results the recommendation of this study is to use this approach as a preliminary processing task towards verifying weather lore.

Keywords: Weather lore, indigenous knowledge, drought forecasting, fuzzy
logic, cognitive mapping.