Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting

Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for morphometric characteristics of the catchment. The model developed using Time-Lagged Recurrent Neural Networks (TLRNs) is used to estimate runoff for river Dikrong, a tributary of river Brahmaputra in India. Comparisons with traditional static models, with and without integration of geomorphologic data, reveal the proposed model to be a promising tool in operational hydrology.

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