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Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting

Authors :
Villia Maria Myrto
Tsagkatakis Grigorios
Moghaddam Mahta
Tsakalides Panagiotis
Source :
Sensors; Volume 22; Issue 5; Pages: 1851
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Forecasting the values of essential climate variables like land surface temperature and soil moisture can play a paramount role in understanding and predicting the impact of climate change. This work concerns the development of a deep learning model for analyzing and predicting spatial time series, considering both satellite derived and model-based data assimilation processes. To that end, we propose the Embedded Temporal Convolutional Network (E-TCN) architecture, which integrates three different networks, namely an encoder network, a temporal convolutional network, and a decoder network. The model accepts as input satellite or assimilation model derived values, such as land surface temperature and soil moisture, with monthly periodicity, going back more than fifteen years. We use our model and compare its results with the state-of-the-art model for spatiotemporal data, the ConvLSTM model. To quantify performance, we explore different cases of spatial resolution, spatial region extension, number of training examples and prediction windows, among others. The proposed approach achieves better performance in terms of prediction accuracy, while using a smaller number of parameters compared to the ConvLSTM model. Although we focus on two specific environmental variables, the method can be readily applied to other variables of interest.

Details

ISSN :
14248220
Volume :
22
Database :
OpenAIRE
Journal :
Sensors
Accession number :
edsair.doi.dedup.....9228288cad06d8d095c43f8c57aebf59
Full Text :
https://doi.org/10.3390/s22051851