Predicting water allocations and trading prices to assist water markets

Abstract

Uncertain water allocations and water trading prices are a key constraint to efficient irrigated cropping and water trading decisions. This study shows that neural network models can reasonably forecast seasonal allocations and trading prices in water markets. These models can complement other forecasting techniques such as regression analysis and time series models as the former can better capture the non-linearities in the water trading system. Using a 50% probability risk factor for water variability, the water allocation model showed minor estimation error; however, in one instance the model underestimated the water allocation by 21%. This may be due to exceptionally low initial water allocations and borrowing of water from future years which was outside the training data sets. Similarly, the water trading price forecast model showed modest estimation error of about 11% during 2004/05 probably due to drought. Overall the models have good water allocation and price forecasting accuracy, and the determinants of water trading prices identified by the neural network models are those expected of the econometric models/economic theory.