Optimal irrigation water allocation with stochastic supply based on production function | |
| Author | Manguerra, Henry Barquez |
| Call Number | AIT Thesis no. AE-89-56 |
| Subject(s) | Irrigation scheduling Dynamic programming |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Environment, Resources and Development |
| Publisher | Asian Institute of Technology |
| Abstract | A Two - Stage (Deterministic and Stochastic ) Dynamic Programming approach has been introduced in this study to solve the complex problem of optimal water allocation in an irrigation project. The complexity of a real-world situation is represented by incorporating in the optimization model the stochasticity of water supply and the non-linearity of crop production functions. As compared to the Explicit Stochastic Dynamic Programming which necessitates along with its u se an enormous computational complexity due to the so called " curse of dimensionality", the present mode l can approximate the theoretical global optimum, at least for the present case study, with dramatic reduction in computer processing time. It also eliminates the rigidness of the policy derived by t h e explicit approach since it provides irrigation planners alternative decision policies which can incorporate the intangibles and other social factors . The traditional method of fixing the cropping pattern based on deterministic estimates of dependable water supply can likewise b e evaluated by the use of the present model. The incorporation of crop production functions in the objective function of t he model extended further the degrees- of- freedom for optimization. Non-linear, dated and multiplicative production function is transformed into a sequentially additive type to replace the usual method of creating an additional "state of the plant variable " which only increases t he dimensionality of the problem. The present study contend s that the accuracy of the optimization mod el highly depends on the accuracy of the crop production function u sed. The relationships between the net benefit obtained from the deterministic model a nd the expected net benefit from the stochastic mod el are used as criteria for decision making. Similarly, system simulation can b e use d particularly when the data generation technique adopted in the study can be extended as a forecasting tool. The model is specifically intended for a rice-based irrigation system under diversified cropping during the dry season with a run- of- river type diversion. The selected case study is the Mae Taeng Irrigation Project in the northern part of Thailand. Th e results of the model application appear. to b e practically acceptable. |
| Year | 1989 |
| Type | Thesis |
| School | School of Environment, Resources, and Development |
| Department | Department of Food, Agriculture and Natural Resources (Former title: Department of Food Agriculture, and BioResources (DFAB)) |
| Academic Program/FoS | Agricultural and Food Engineering (AE) |
| Chairperson(s) | Paudyal, Guna N. ; |
| Examination Committee(s) | Gupta, Ashim Das ;Murty, V.V.N ; |
| Scholarship Donor(s) | Federal Republic of Germany (DAAD ) ; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 1989 |