Indigenous Knowledge Aware Drought Monitoring, Forecasting and Prediction Using Deep Learning Techniques (Proposals Track)

Kidane W Degefa (Haramaya University)

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Abstract

The general objective of this proposed research work is to design deep learning based hybrid comprehensive framework for drought monitoring, forecasting and prediction using scientific and indigenous knowledge as an integration of connectionist and symbolic AI. In Ethiopia, among all extreme climate events, drought is considered as the most complex phenomenon affecting the country and its impact is also high due to absence of locally grounded intelligent and explainable technology-oriented drought early warning and monitoring system. Thus, studying Ethiopic perspective of drought monitoring and prediction in line with continental and global climate change is vital for drought impact minimization and sustainable development of the country. Moreover, having technology assisted early protective, preventative action is also many times cheaper than the associated response to humanitarian crisis. Accordingly, this proposed work will have different expected outputs, including: drought risk identification, drought monitoring, drought preparedness, drought forecasting, drought mitigation, and post drought best practice recommendation models with interactive visualizations and explanations.

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