Date of Award


Degree Type


Degree Name


Degree Program

Computer Science


Computer Science

Major Professor

Mahdi Abdelguerfi

Second Advisor

Md Tamjidul Hoque

Third Advisor

Christopher Summa

Fourth Advisor

Syed Ahmed


The national Earth System Prediction (ESPC) initiative aims to develop the predictions
for the next generation predictions of atmosphere, ocean, and sea-ice interactions in the scale of days to decades. This dissertation seeks to demonstrate the methods we can use to improve the ESPC models, especially the ocean prediction model. In the application side of the weather forecasts, this dissertation explores imitation learning with constraints to solve combinatorial optimization problems, focusing on the weather routing of surface vessels. Prediction of ocean waves is essential for various purposes, including vessel routing, ocean energy harvesting, agriculture, etc. Since the machine learning approaches cannot forecast ocean waves with sufficient accuracy for longer forecast horizons and the numerical methods are not flexible due to being expert-designed, there is a need to study both methods to improve forecasts. One popular way to improve forecasts is to perform data assimilation, which fails to improve the numerical model in the model space. In this dissertation, we explore different ways to improve wave forecasts. We combine data assimilation and machine learning methods to improve predictions from the numerical model WaveWatch III. We have also explored rogue ocean waves, which are not predicted using traditional numerical methods. Moreover, using imitation learning to guide combinatorial optimization problems should allow fast training of reinforcement learning algorithms while satisfying the constraints.


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