Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ML for Flood Forecasting at Scale   Effective riverine flood forecasting at scale is hindered by a multitude of
factors, most notably the need to rely on human calibration in current
methodology, the limited amount of data for a specific location, and the
computational difficulty of building continent/global level models that are
sufficiently accurate. Machine learning (ML) is primed to be useful in this
scenario: learned models often surpass human experts in complex
high-dimensional scenarios, and the framework of transfer or multitask learning
is an appealing solution for leveraging local signals to achieve improved
global performance. We propose to build on these strengths and develop ML
systems for timely and accurate riverine flood prediction.

23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat