Abstract:
Monitoring water management, especially in irrigation systems need to be efficient in order to
understand their fitness in a particular time frame, looking at this by collecting field data could
be time consuming and expensive. Therefore, modelling the whole system using factors effecting its inflow and outflow could be really useful. ANN can be used to monitor and predict
irrigation systems by using primary and secondary data. Using ANN, it is possible to build
nonlinear relationships between input and output parameters without relying on physical process. In this study we simulate the outflow pattern of “Yoda wewa” Sri Lanka considering six
parameter model. Comprising inflow, rainfall, evaporation, temperature, relative humidity and
wind speed. The model was obtained by performing the training with various transfer functions,
learning rates and momentum coefficients. The results found to be capable of predicting water
outflow pattern of “Yoda wewa” up to 66% of confidence. The success rate heavily depends on
the parameters used and the density of the data in the study. Few parameters were not being able
to be collected from the site itself. Hence it is necessary that a proper ground truth data to come
in as inputs for the model. It is desirable as an extension of this work to include soil structure
and underlying topography. The model could be tested for several Irrigational systems in order
to make substantial recommendation of applying the method for different types of Irrigational
System.
