ESoWC 2019 - Machine learning for predicting extreme weather hazards

A reproducible flood forecasting case study using different

machine learning techniques

Lukas Kugler & Sebastian Lehner

ESoWC-Mentors: Claudia Vitolo, Julia Wagemann, Stephan Siemen

16.10.2019

Modelingriverdischarge=>high-dimensionalproblem:manymeteorological/hydrologicalparametersimportantwithcomplexinteractions=>coupleddynamicalmodels.

●  CanstatisticalconnectionsbasedonMLtechniqueswithcomparableskillbeestablishedforextremefloodingevents?

➔  Explorativecomparisonstudy:investigatefloodforecastingcapabilityofMLtechniques(withdatafromECMWF/Copernicus)

➔  Codeanddocumentationforinterestedpeers(opensourceandreproducible;https://github.com/esowc/ml_flood)

Motivation & goal

2

Data

●  PredictordatafromERA5viatheClimateDataStoreAPIforpython

●  PredictanddatafromGloFASv2.0○  Reanalysis○  4ForecastrerunsforafloodingeventdirectlyfromtheGloFASteamviaFTP

●  Dailyresolution(preprocessingCDO);spatialdomainofinterest 3

Climate Data Store API

Notebook:1.03_data_overview.ipynb

Data characteristics

●  Riverdischargenon-normaldistribution=>Predictchangeindischarge

●  Predictors=>normalization

4 ©GLOWA Danube

Workflow

●  Determinepointofinterest(fordischargeforecasts)

●  Findupstreamarea(catchment)

●  Spatiallyaveragefeaturesinthecorrespondingarea

●  Predictand:dischargeatspecifiedoutflowpoint

=>one-dayforecastforchangeindischarge!

5

ERA5 reanalysis

river discharge

GloFAS reanalysis

precip runoff snow

Reanalysis

day 1

ML-Model

day 2 day 3

ML model setup // Overview

Models:

●  LinearRegressionModelviascikitlearn●  SupportVectorRegressorviascikitlearn●  GradientBoostingRegressorviascikitlearn●  Time-DelayNeuralNetviaKeras

6

Training 1981 - 2005

Validation 2006 - 2011

Test 2012 - 2016

ML model settings // Validation

Hyperparameteroptimization(viaGridSearch):●  StandardOLSLinearRegressiondoesnothaveanyhyperparameters✓ ●  SupportVectorRegressor:kernel=poly, C=100, epsilon=0.01, degree=3

●  GradientBoostingRegressor:n_estimators=200, learning_rate=0.1, max_depth=5

●  Time-DelayNeuralNet: one hidden layer, 64 nodes (1281 dof), batch size 90 d, tanh activation

7 Notebooks:3.02_LinearRegression.ipynb3.03_SupportVectorRegression.ipynb

3.04_GradientBoost.ipynb3.05_Time-delay_neural-net.ipynb

Validation LR SVR GBR TDNN

RMSE [m3/s] 169 156 142 126

NSE 0.84 0.86 0.89 0.93

ML model setup // SupportVectorRegressorexample

Hyperparameteroptimization:viaGridSearch

●  kernel:rbf,poly●  C:[1,10,100]●  epsilon:[0.01,0.1,1]●  degree(ifpoly):[1,2,3,4,5]

8 Notebook:3.03_SupportVectorRegression.ipynb

Bestsetting:

Results // Model comparison in the full test period

14-dayforecastsoverthefulltestperiod:Bestmodel=>Time-delayneuralnet

9 Notebook:3.06_model_evaluation.ipynb

Results // Samples: rapid changes in discharge

●  14-dayforecastsamples●  Inittimes:

a.  30.06.2012 b.  22.10.2013 c.  03.08.2014 d.  23.08.2016

●  Floodingevent2013=>moredetailedcasestudy

10 Notebook:3.06_model_evaluation.ipynb

a b c d

Results // Samples: rapid changes in discharge

metricsforall4sampleperiodstogether=>

11 Notebook:3.06_model_evaluation.ipynb

<=metricsforsampleperioda)

Results // Case study: 2013 European Flood

● Rerunsareforecast-driven

● MLisReanalysis-driven

➔  Time-delayneuralnet&SupportVectorReg.performbest

➔  Grad.Boost.&Lin.Reg.Modelshownosignificantincreaseindischarge(shapeisquitefine,butamplitudenot)

12 Notebook:3.06_model_evaluation.ipynb

Conclusion

●  TheTD-NNandSVRmodels:

○  areabletopredictanextremefloodingevent,despitetheirrelativelysimplesetting,

○  mayprovidecheapadditionalinformationtoassistforecasts.

●  LRandGBRwerenotabletopredictanadequateincreaseindischarge(althoughingeneralGBRperformedaboutasgoodasTD-NNandSVR)

Additionaltuningofmodels→neuralnetsexhibitthemosttheoreticalpotentialduetoalotofpossibleimprovementsinthebasearchitecture(LSTM)

●  Opensource/reproducible(e.g.viaGithub)approachhelpsingettingquickfeedback 13

Outlook

●  Incorporatestationaryinformation○  e.g. catchment specific variables

in a CNN or an LSTM as Embedding (see e.g. Kratzert et al 2019*)

●  Exploredifferentkindsofcatchments

●  Comparemoreextremeeventswithforecastrerunsandanalysethem

●  Coupledmodel:Conceptidea

14

Github:github.com/esowc/ml_floodNbviewer:link*Kratzert,F.,Klotz,D.,Shalev,G.,Klambauer,G.,Hochreiter,S.,&Nearing,G.(2019).BenchmarkingaCatchment-AwareLongShort-TermMemoryNetwork(LSTM)forLarge-ScaleHydrologicalModeling.arXivpreprintarXiv:1907.08456.

15

Acknowledgements Big thanks go to

●  Our mentors: Claudia Vitolo, Julia Wagemann and Stephan Siemen, for always providing helpful comments

●  Esperanza Cuartero, for communicating the results

●  The University of Vienna, Dept. of Meteorology and Geophysics, for office and server access

●  ECMWF-Copernicus, for setting up ESoWC and providing such an opportunity!