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 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