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Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting
Abstract 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.
Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting
Abstract 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.
Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting
Saharia, Manabendra (author) / Bhattacharjya, Rajib Kumar (author)
KSCE Journal of Civil Engineering ; 16 ; 862-869
2012-06-29
8 pages
Article (Journal)
Electronic Resource
English
Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting
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