Decision Support System for Water Management in the Besut Rice Irrigation Scheme
A decision support system (DSS) model was developed to improve decision-making with respect to water release schedules and timely water distribution in a large double cropping rice irrigation scheme. The model focuses mainly on water allocation decisions and timely water distribution. The DSS mod...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2004
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/283/1/549573_FK_2004_23.pdf |
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| Summary: | A decision support system (DSS) model was developed to improve decision-making
with respect to water release schedules and timely water distribution in a large
double cropping rice irrigation scheme. The model focuses mainly on water
allocation decisions and timely water distribution. The DSS model includes data
management, model management, a knowledge base and a user interface. Data
management and model management systems are external to the DSS. The data
management system is composed of the following subsystems: meteorological data,
hydrological data, irrigation canal data, soil data and crop data. Four mathematical
models; crop water, stochastic rainfall, canal simulation and water balance models
were developed for the model management system.
The Penman-Monteith method was applied for estimating reference
evapotranspiration. Then the crop water model was developed from reference
evapotranspiration and crop coefficient. Evapotranspiration was found to be 4.20 mm/day and 3.99 mm/day for off season and main season crop respectively. Crop
evapotranspiration was higher during the off season crop compared to that of the
main season crop, mainly as a result of prevailing weather conditions. A stochastic
rainfall model was developed using 30 years daily rainfall data from six stations. A
first order Markov chain was used to simulate the occurrence of rainfall, and a
skewed normal distribution was applied to fit the amount of rainfall for a rainy day.
The stochastic rainfall model verification was performed with a separate set of data.
Results obtained showed that the model could be used to generate rainfall data in the
area satisfactorily.
A water balance model was utilized to determine irrigation water requirements. It
was observed that a modification of the existing irrigation schedules would have
saved a considerable amount of irrigation water during the main season and off
season. Based on field water requirements during the pre-saturation and normal
irrigation supply periods and available flows at the intake structures, canal simulation
was performed using the CanalMan model. Results have shown that pre-saturation
should not be done continuously unless flow rates are at least 9.00 m3/sec and 3.00
m3/sec at the Besut and Angga intake gates respectively. If the flow rates fall below
these values, then pre-saturation should be done in two stages. However, when the
flow rate is between 5.00 and 5.65 m3/sec at the Besut intake, pre-saturation should
be done over three stages. During the normal irrigation supply period, flow rates of
5.00 m3/sec and 1.50 m3/sec at the Besut and Angga intake gates respectively, are to
be maintained for the whole irrigation scheme. Otherwise selective irrigation or
irrigation on a rotational basis has to be adopted.The knowledge base for the DSS was developed from the knowledge derived from
domain experts as well as the results from the model management system. The
models were used to extract knowledge related to aspects of irrigation water
management. The knowledge extracted was checked with domain experts in order to
verify the reliability of the knowledge. The knowledge extracted was then added to
the final decision support system in the form of rules. The knowledge generated
together with the domain experts’ knowledge, were compiled with rules and
incorporated to the menu driven DSS, developed using the wxCLIPS software. The
knowledge base thus created was continually tested for the consistency and
appropriateness, and updated during the development stage. The DSS was evaluated
to assess its decision-making capability using one-year water management data,
which was not used in the development of the DSS. Based on the evaluation, it can
be inferred that the DSS developed can be an effective tool for use in decisionmaking
on water management under practical situations. |
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