Irrigation water allocation is essential to the management of agricultural water use in irrigation districts. Many irrigation optimization models were proposed from previous studies to provide decision support for water managers. In order to capture the complex nonlinear relationships and meet different water demands, more advanced multi-objective nonlinear programming models were developed in the past decade. However, it is still a challenging task to address varies uncertainties associated with irrigation optimization. Fuzzy programming, interval programming, and chance-constrained programming can be used to quantify uncertainties in simplified formats, but none of them can represent complex uncertainty in a composite format. In this thesis, a cloud-based dual objective nonlinear programming (CDONP) model is developed by implementing a cloud modeling method in an irrigation model to address the uncertainties of reference evapotranspiration (ET0) and surface water availability (SWA). The cloud modeling method is used to generate 2,000 data samples from historical data. The results show that the generated samples are consistent with historical data. Optimized allocation schemes are provided, and the performance of the CDONP model are discussed. This is the first Canadian study that used the cloud modeling method in irrigation water allocation. This method provides a solution to quantify composite uncertainties based on limited data, which represents a unique contribution to irrigation water allocation modeling. This study provides valuable decision support for agriculture management to improve water use efficiency. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26112 |
| Date | January 2020 |
| Creators | Yan, Zehao |
| Contributors | Baetz, Brian, Li, Zoe, Civil Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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