Deep Learning Predictions for Hydrology Data

Here is the our research abstract: This research strives to approve that one can study several sets of sequences or time series of an underlying evolution operator using a deep learning network. The language of geospatial time series is used as a common application type, whereas the series can be any sequence, and the sequences can be in any collection (bag)-not just Euclidean space-time-as we need sequences labeled in some way, and having properties consequent of this label (position in abstract space). This problem has been successfully tackled by deep learning in many ways and within numerous research fields. The most advanced work is estimated to be Natural Language processing and transportation (ride-hailing). The second case, with traffic and the number of people needing rides, is a geospatial problem with significant constraints from the spatial locality. As in many problems, the data here is typically space-time-stamped events. However, these can be converted into spatial time series by binning in space and time. Comparing deep learning for such time series with coupled ordinary differential equations used to describe multi-particle systems motivates the introduction of an evolution operator that describes the time dependence of complex systems. With an appropriate training process, our research interprets deep learning applied to spatial time series as a particular approach to finding the time evolution operator for the complex system giving rise to the spatial time series. Whimsically we view this training process as determining hidden variables that represent the theory (as in Newton’s laws) of the complex system. This problem is formulated in general and presents an open-source package FFFFWNPF as a Jupyter notebook for training and inference using either recurrent neural networks or a variant of the transformer (multi-headed attention) approach. This assumes an outside data engineering step that can prepare data to ingest into FFFFWNPF. The approach, a comparison of transformer and LSTM networks are presented for time series of Hydrology streamflow, temperature, and precipitation data collected on 671 catchments from each nation: the US, the UK, and Chile. This research is intended to explore how complex systems of different types (different membership linkages) are described by different types of deep learning operators. Geometric structure in space and multi-scale behavior in both time and space will be important. We predict that the current forecasting formulation will be easily extended to sequence-to-sequence problems.



  1. GitHub repo: TBD
  2. Paper: TBD


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Last modified April 3, 2023: Updated timeseries.md (b32f1cd)