Abstract
The steady increase in droughts worldwide has compelled many researchers to focus on water allocation. Multi-objective decision support for irrigation systems is a popular topic due to its relevance to the national economy and food supply. However, the majority of researchers have relied on conventional top-k designs for their decision support systems despite their limitations with regard to multi-objective systems. Thus, we propose applying a skyline query to the problem. As the input and output formats of skyline queries differ significantly from those of existing systems, we developed a new genetic algorithm and objective ranking. Qualitative and quantitative experiments using real-world data from Taiwan’s largest irrigated region demonstrate the effectiveness of the proposed approach.
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