A hybrid framework for short-term irrigation demand forecasting

Abstract

Reliable short-term forecasts of Irrigation Water Demand (IWD) can provide useful information to help water supply system operators with day-to-day operating decisions. Forecasting IWD is a complex task due to different natural (soil, water, crop, and climate interactions) and behavioral (farmers’ decision-making) components of the irrigation process. So far, various approaches have been developed to estimate IWD values in different contexts. One common approach is the application of data-driven methods to map the relationship between the main influential factors and IWD. Data-driven approaches often do not consider any conceptual understanding of the system in modeling IWD, which has been found to be effective in improving the predictive performance when considered. In this study, a hybrid framework has been introduced and developed by incorporating existing physical knowledge of the system into a data-driven model to predict IWD. This framework consists of two modules: In the first module, a simple conceptual approach was implemented to model the understood factors leading to crop water needs using observation data. In the second module, a data-driven model was used to capture the remaining relationships between inputs and the output in the irrigation process. The proposed hybrid framework was then applied to estimate daily IWD up to 7 days ahead for an irrigation district in Victoria, Australia. Results show that the integration of physical system understanding into data-driven models can improve the performance of IWD forecasting models, particularly during the high-demand period. In addition, the hybrid framework provides improved system understanding and thus leads to increased capacity to support operational decisions.