Disaggregation of Rainfall Time Series using Artificial Neural Network In Case Of Limited Data

Abstract

Temporal resolution of rainfall series needs to be necessarily less to use it in many engineering applications. But most of the simulated and observed rainfall series are coarser than 3hours. Hence, it is imperative to disaggregate coarser rainfall to finer. The quantum of necessary fineness depends on application in which the rainfall data is going to be used. In this paper, the competency of Artificial Neural Network to disaggregate 3 hour rainfall into hourly, in case of limited data is verified. It is found that the disaggregation is viable with the constraint of limited data also. The rainfall is disaggregated using three models, of which, performance of the second model is much better than the others. Nonetheless the constraint of limited data, the rationale behind the better performance of the second model, is clearly discussed

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“…and distance metrics defined in the KNN method, then the values are disaggregated based on the similar rainfall events. Some studies also explored using artificial neural network (ANN) for disaggregation (Burian et al 2000(Burian et al , 2001Poomalai andChandrasekaran, 2019, Bhattacharyya andSaha, 2023), the neural network models learn the mapping between the aggregate series and the disaggregated components using the period with both resolutions, the input to the neural network is the lower resolution records, and the outputs are the higher resolution values, then the lower resolution value without corresponding higher resolution values are feed into the trained neural network to predict the disaggregated components to fill the absent part of higher resolution data.…”