An optimisation approach based on Genetic Algorithms (GAs) is developed for real time allocation of irrigation water supplies. Appropriate objective functions for the water allocation problem have been derived previously and solved using quadratic programming (QP). There had been concerns that the QP approach may become computationally bounded for large systems. It was also thought that the approach would be difficult to apply to water scheduling problems. This research describes work on the development of a GA for the water allocation problem. Although GAs have been actively researched for 30 years, only one previous application to an irrigation problem has been found in the literature. The GA approach is very flexible, and is easily set up for a wide range of linear and non-linear objective functions. In developing the GA solver the intention was to have a generic code easily adapted and used. This has been achieved and the same core routine are used for a wide range of problems. Applied to the water allocation problem, the GA approach can provide solutions that are similar to those produced by QP. It is, however, sensitive to string length, and has difficulty in meeting nodal water balance constraints. It is concluded that the GA approach offers no advantage over QP for the water allocation problem. Further development of a Genetic Algorithm (GA) to solve an irrigation water scheduling problem is described. The objective is to optimise the utilisation of water resources during water stress periods in irrigation systems operating on a rotational basis. Objective functions for the water scheduling problem are developed. Solutions are presented using a GA, and the advantages and shortcomings of different approaches are discussed. The objective is to minimise the irrigation water supply to the system when adequate supplies exist, and to distribute crop stress in an equitable manner in periods of water shortage. It was demonstrated that a formulation called the "Zero-1" approach was most effective in solving the problem, performing significantly better than traditional systems, and more recent scheduling developments. A number of practical applications are presented that demonstrate the effectiveness of the GA approach.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:641585 |
| Date | January 2001 |
| Creators | Bhaktikul, Kampanad |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/10800 |
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