In conversation with faculty opponent Associate Professor Søren Liedtke Thorndahl (Aalborg University) and the examination board, Hasan discussed his work on using remote sensing and artificial neural networks to measure precipitation and rainfall runoff.
In his thesis, Hasan uses two methods of remote sensing to measure rainfall remotely - weather radars (Swedish case study) and satellites (Irani case study). The data produced by remote sensing is based on indirect estimations of complex weather systems, which means they might be biased. Therefore, artificial neural networks were developed. This produced improved monthly runoff estimations for the Irani case study and improved rainfall detection and accuracy in the Swedish case study. Hasan has also studied the impact of human activities, for instance agricultural developments, on runoff possibilities. The thesis concludes that there is a promising relationship between remote sensing and artificial intelligence in estimating precipitation and runoff.
The thesis defence took place at the Division of Water Resources Engineering, Department of Building and Environmental Technology at Lund University on June 16. The event was chaired by Rolf Larsson.