  This paper addresses the problem of optimizing sensor deployment locations to
reconstruct and also predict a spatiotemporal field. A novel deep learning
framework is developed to find a limited number of optimal sampling locations
and based on that, improve the accuracy of spatiotemporal field reconstruction
and prediction. The proposed approach first optimizes the sampling locations of
a wireless sensor network to retrieve maximum information from a spatiotemporal
field. A spatiotemporal reconstructor is then used to reconstruct and predict
the spatiotemporal field, using collected in-situ measurements. A simulation is
conducted using global climate datasets from the National Oceanic and
Atmospheric Administration, to implement and validate the developed
methodology. The results demonstrate a significant improvement made by the
proposed algorithm. Specifically, compared to traditional approaches, the
proposed method provides superior performance in terms of both reconstruction
error and long-term prediction robustness.
