A canary, a coal mine, and imperfect data: determining the efficacy of open-source climate change models in detecting and predicting extreme weather events in Northern and Western Kenya

Alvin M Igobwa, Jeremy Gachanja, Betsy Muriithi, John Olukuru, Angeline Wairegi, Isaac Rutenberg

Climatic Change · 01 October 2022

In one sentence. Artificial neural networks and isolation forests on Kenyan station data predict drought and flood occurrences with 78-90% accuracy, supporting early warning.

Abstract

Climate models, by accurately forecasting future weather events, can be a critical tool in developing countermeasures to reduce crop loss and decrease adverse effects on animal husbandry and fishing. In this paper, we investigate the efficacy of various regional versions of the climate models, RCMs, and the commonly available weather datasets in Kenya in predicting extreme weather patterns in northern and western Kenya. We identified two models that may be used to predict flood risks and potential drought events in these regions. The combination of artificial neural networks (ANNs) and weather station data was the most effective in predicting future drought occurrences in Turkana and Wajir with accuracies ranging from 78 to 90%. In the case of flood forecasting, isolation forests models using weather station data had the best overall performance. The above models and datasets may form the basis of an early warning system for use in Kenya's agricultural sector.

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