RAINFALL DISAGGREGATION USING ARTIFICIAL NEURAL NETWORKS

A precipitation time series is often a necessary input for the analysis and design of hydrologic and hydraulic systems. The precipitation records employed for these purposes can be either measured observations or generated by stochastic simulation. One common problem with recorded and generated precipitation data is that often it is not in small enough time increments for use in engineering applications (e. g., continuous hydrologic simulation). To solve this problem, rainfall amounts can be disaggregated into shorter time increments. This paper evaluates the use of artificial neural networks (ANNs) for the disaggregation of hourly rainfall data into subhourly time increments. Two different ANN models are introduced and evaluated in this paper. A back-propagation/steepest-descent algorithm trains one model and the other model uses the idea of self-organization in a competitive learning ANN. The results indicate that the performance of both ANN models are: (1) comparable to other disaggregation schemes in terms of predicting the overall disaggregated rainfall hyetograph; and (2) improvements over other disaggregation models in the prediction of the maximum incremental rainfall intensity (depth) within a storm hyetograph. Based on these results, the use of ANN models can be recommended as a viable alternative for hourly rainfall disaggregation.