Agriculture accounts for roughly 72% of global freshwater withdrawals, but irrigation is often still based on intuition or fixed schedules. Model-based and data-driven approaches have shown promise, but they mostly rely on historical datasets or crop-specific knowledge that are unavailable in many settings. This thesis addresses that gap by proposing an irrigation control framework that requires no prior data and can learn the system dynamics online during the growing season. The framework combines three components: a Zone Model Predictive Control strategy, an online model estimator based on Recursive Least Squares (RLS), and a Readily Available Water (RAW) estimator. The framework is evaluated using the AquaCrop simulator on a sugarcane case study in southern Mozambique. Results show that the proposed framework performs comparably to a static controller preconfigured with historical data and crop-specific knowledge, while requiring neither. A sparse measurement strategy is also evaluated, reducing the number of required soil moisture measurements by around 65% with little impact on performance. That said, learning the system online comes with trade-offs, and the framework does not consistently outperform a well-configured static model. Together, the results suggest that effective predictive irrigation control is achievable in data-scarce environments.
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Commonly, when researchers are figuring out the effect of a putative cause, additional variables influence the cause and the effect. These are called confounders, and they obfuscate causal relationships. Inverse Probability Weighting is a method that can be applied to remove confounding and show a causal effect. This study aims to determine if Dota 2 game outcomes can be predicted based on team composition diversity and if 'Inverse Probability Weighing is a helpful tool for this. First, metrics to assess team diversity were determined, and the two confounders, "Player skill" and "Hero skill", to the 'is-diverse' treatment and game outcome were identified. Next, a dataset with Dota 2 games was gathered containing all the relevant variables to measure the confounders. Finally, inverse probability weighting was applied to measure the Average Treatment Effect of the putative cause.
The results suggest that team diversity significantly impacts the game outcome. However, the results were close in most cases when comparing the results with data that was not corrected for by confounders. A possible explanation may be that the confounders didn't have enough influence on some diversity metrics since they were too vague. For example, the most complex diversity metric, which covered the most team-composition characteristics, showed a clear difference between the average treatment effect with inverse probability weighting being applied and not applied.
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