Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network

• URL: http://arxiv.org/abs/2109.13235v1
• Date: Mon, 27 Sep 2021 09:19:53 GMT
• Title: Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network
• Authors: Michael Stalder, Firat Ozdemir, Artur Safin, Jonas Sukys, Damien Bouffard, Fernando Perez-Cruz
• Abstract summary: We propose to aggregate simulations of lake temperature at a certain depth together with a range of meteorological features. This work demonstrates that the proposed model can deliver homogeneously good performance covering the whole lake surface. Results are compared with a state-of-the-art Bayesian deep learning method.
• Score: 55.41644538483948
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate lake temperature estimation is essential for numerous problems tackled in both hydrological and ecological domains. Nowadays physical models are developed to estimate lake dynamics; however, computations needed for accurate estimation of lake surface temperature can get prohibitively expensive. We propose to aggregate simulations of lake temperature at a certain depth together with a range of meteorological features to probabilistically estimate lake surface temperature. Accordingly, we introduce a spatio-temporal neural network that combines Bayesian recurrent neural networks and Bayesian graph convolutional neural networks. This work demonstrates that the proposed graphical model can deliver homogeneously good performance covering the whole lake surface despite having sparse training data available. Quantitative results are compared with a state-of-the-art Bayesian deep learning method. Code for the developed architectural layers, as well as demo scripts, are available on https://renkulab.io/projects/das/bstnn.

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