Snow water equivalent forecasting in Sub-Arctic and Arctic regions with recurrent neural networks ...
<!--!introduction!--> Snow water equivalent (SWE) expresses amount of liquid water in the snow pack. This information is crucial in many hydrological models, but the actual measurement of SWE requires a lot of labor demanding manual work. Because these in situ measurements are labor demanding...
| Main Authors: | , , |
|---|---|
| Format: | Conference Object |
| Language: | unknown |
| Published: |
GFZ German Research Centre for Geosciences
2023
|
| Subjects: | |
| Online Access: | https://dx.doi.org/10.57757/iugg23-2856 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019028 |
| Summary: | <!--!introduction!--> Snow water equivalent (SWE) expresses amount of liquid water in the snow pack. This information is crucial in many hydrological models, but the actual measurement of SWE requires a lot of labor demanding manual work. Because these in situ measurements are labor demanding and often scarce, efficient, and accurate, forecasts of SWE are crucial to get reliable estimates from current and future state of the hydrological systems. In this work, we compare two different recurrent neural network (RNN) architectures: long short-term memory (LSTM) and gated recurrent unit (GRU). We show that GRU is as accurate as LSTM, but requires less computation and is thus preferred RNN architecture for SWE forecasting. We optimise the efficiency and accuracy of these RNNs with hyper parameter Bayesian optimisation, and comprehensive data preprocessing. We furthermore improve the accuracy of the forecasts by using time to vector representation of time in the RNN architecture. We also show that the RNNs ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
|---|