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|CSDMS meeting abstract presentation=Long Short-Term Memory networks (LSTMs) have been around since the early 90’s but only in the last few years, LSTMs gained increasing popularity in hydrological sciences. Publication counts see exponential growth and LSTMs power some of the largest-scale operational flood forecasting systems. | |CSDMS meeting abstract presentation=Long Short-Term Memory networks (LSTMs) have been around since the early 90’s but only in the last few years, LSTMs gained increasing popularity in hydrological sciences. Publication counts see exponential growth and LSTMs power some of the largest-scale operational flood forecasting systems. | ||
In this presentation, we look at some of the research from the past few years and try to understand why the LSTM is a particularly well suited architecture for the application as rainfall-runoff model but also discuss limitations and open research questions. | In this presentation, we look at some of the research from the past few years and try to understand why the LSTM is a particularly well suited architecture for the application as rainfall-runoff model but also discuss limitations and open research questions. | ||
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Latest revision as of 16:34, 11 June 2025
CSDMS 2025: Exploring Earth's Surface with Models, Data & AI
Long Short-Term Memory networks for rainfall-runoff modeling
Abstract
Long Short-Term Memory networks (LSTMs) have been around since the early 90’s but only in the last few years, LSTMs gained increasing popularity in hydrological sciences. Publication counts see exponential growth and LSTMs power some of the largest-scale operational flood forecasting systems. In this presentation, we look at some of the research from the past few years and try to understand why the LSTM is a particularly well suited architecture for the application as rainfall-runoff model but also discuss limitations and open research questions.
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