Advances in Neural Networks for prediction

Transcription

1 Symposium on EurOtop Unesco IHE Delft, September 10, 2014 Coasts, Marine Structures and Breakwaters Edinburgh, Sep 18-20, 2013 Advances in Neural Networks for prediction

2 Outline Background CLASH project: database and Neural Networks for predicting wave overtopping discharge Van Gent et al. (2004, 2007) CLASH project Verhaeghe et al. (2005, 2008) CLASH project Wave transmission: Panizzo and Briganti (2007) DELOS project Van Oosten and Peixó Marco (2005) MsC TUDelft Wave reflection: Advances Zanuttigh et al. (2013) THESEUS project Extended database Optimised Neural network Conclusions

3 Background: the CLASH database CLASH project ( ) Database: more than 10,000 wave overtopping tests homogeneously described by means of the same 31 parameters: 15 parameters to schematize the structure geometry; 13 hydraulic parameters to represent the wave attack conditions; 3 «general» parameters

4 Background: the CLASH NN, parameters CLASH project ( ) 15 input parameters, scaled to Hm0t = 1m predicting the wave overtopping discharge Wave attack Crest and Slope Toe Berm conditions crown wall Roughness H m0,t cotα d B t B R c γ f T m 1,0,t cotα u h t h B A c β B h G c h tanα B

5 Background: the CLASH NN, architecture CLASH project ( ) NeuralNetwork: 15 input parameters, scaled to Hm0t = 1m; predicting the average wave overtopping discharge. Logarithmic transformation of the output values: log(q)

6 Background: CLASH NN EurOtop tool CLASH project ( ) Final prediction Tool for EurOtop (2007): the NN by Deltares, Van Gent (2004, 2007)

7 Background: CLASH NN EurOtop tool CLASH project ( ) Final prediction Tool for EurOtop (2007): the NN by Deltares, Van Gent (2004, 2007)

8 Background: CLASH further work on NN CLASH project ( ) Implementation of a classifier and quantifier (Verhaeghe, 2005, 2008) 3000 Complete database 10'000 data To classifier q<10 6 : NO OVERTOPPING q 10 6 : OVERTOPPING

9 Background: CLASH NN EurOtop tool CLASH project ( ) Implementation of a classifier and quantifier (Verhaeghe, 2005, 2008) From classifier: 1500 q 10 6 m 2 /s Database to quantifier 8'000 data, q 10 6 m 2 /s To quantifier

10 Developments after CLASH: Wave Transmission DELOS project ( ) database: more than tests on wave transmission behind low crested structures. ANN Van Oosten & Peixó Marco (2005) MsCTUDelft ANN by Panizzo & Briganti (2007) DELOS project With respect to CLASH Less tests available (2 000 instead of ) LCSs are simpler structures to be schematized A simpler ANN architecture is required 6 input parameters

11 Wave Transmission NN Wave transmission ANN by Panizzo & Briganti (2007): Introduction of 1 additional geometrical parameter: D 6 non dimensional input parameters: a different way to scale R c /H m,0,t H m,0,t /D n,50 G c /H m,0,t G c /L m 1,0,t ξ 0,p H m,0,t /h

12 Developments after CLASH: wave reflection THESEUS project ( ) Database derived from CLASH, with additional datasets: nearly tests on wave reflection from a variety of structures NN by Zanuttigh et al. (2013) With respect to CLASH Same structure of the CLASH database Additional information D, Kr, Kt Physicallybased scaling 13 nondimensional input parameters

13 Wave reflection NN H m0,t /L m 1,0t h t/ /L m 1,0t 13 non dimensional input parameters Parameters non dimensionalised with H m0t ; L m 1,0t R c /H m0t γ f cotα d cotα incl D/H m0t B/L m 1,0,t h B/ /H m0t G c /L m 1,0t m β spreading

14 Developments after CLASH: conclusions 3 databases; 3 differently built NN prediction tools predicting 3 differentoutputs. # q K t K r (CLASH db) (DELOS db) (CLASH db+adds) 1 H m,0,t R c /H m,0,t H m,0,t /L m 1,0,t 2 T m 1,t G c /L m 1,0,t h t /L m 1,0,t 3 γ f G c /H m,0,t γ f 4 cotα d H m,0,t /h cotα d 5 cotα u ξ 0,p cotα incl 6 B H m,0,t /D n,50 D/H m,0,t 7 B t R c /H m,0,t 8 h B/L m 1,0,t 9 h t h b /H m,0,t 10 h b G c /L m 1,0,t 11 R c m 12 A c β [rad] 13 G c spreading 14 tanα B 15 β 16 TOT (*) Scale factor: H m0t = 1 m Non dimensionless, many scale factors Non dimensionless scale: H m0t ; L m 1,0t

15 Aim of cooperation

16 Aim of cooperation 3 Databases 3 Schematizations 3 NN tools 1 database 1 schematization for structure geometry 1 optimized ANN for the prediction of q, Kr and Kt Exe free, user friendlyinterface

17 The extended CLASH database Database of CLASH is the starting point for scheme and parameters Including the average unit size D representative of the structure elements, where applicable (zero = smooth): Add existing databases on transmission and reflection and uniform information in CLASH format Including Kt, Kr and q where available Establish RF and CF where missing Initial profile for berm breakwaters Add new data, if available (ongoing process) Now including tests

18 The extended database: overtopping CLASH database (2004, 2008) LWI reports Victor (2012) % 9% 11% q # % 9% A rock permeable B rock impermeable C armour units D smooth slopes % E non straight slopes and berms ) 1/2 3 q/(g*h m,0,t % F seawalls G oblique wave attacks R c /H m,0,t

19 The extended database: transmission DELOS database (2005) Lissev (1993) Datasets from CLASH (2004, 2008) K t # R c /H m,0,t

20 The extended database: reflection K r Zanuttigh and Van der Meer (ICCE 2006, CENG 2008) : Datasets from DELOS (2005) Datasets from CLASH (2008) Victor (2012) # % Csi 0p 23% 8% Kr #7413 4% A rock permeable 30% 3% B rock impermeable C armour units D smooth slopes E non straight slopes and berms F seawalls G oblique wave attacks

21 The advances with the ANN Comparison of the CLASH and THESEUS ANNs to predict overtopping, reflection and transmission. Qualitative and quantitative analysis (ICCE 2014). Selection of the input parameters for the advanced NN and sensitivity analysis. Revision and optimization of the architecture: Use of the bootstrapping resampling technique based on RF, CF as in CLASH (Van Gent, Verhaeghe) Training on 100% of the database no early stopping (Verhaeghe). Average results and reliability indication (confidence intervals) from the commitment of networks (500).

22 The advanced NN: inputs required Wave attack conditions Slope Toe Berm Crest and crown wall Roughness H m0,t cotα d B t B A c γ f T m 1,0,t cotα u h t h B G c Element size β m R c D h With respect to the EurOtop tool. B h, tanα B (the latter following also Verhaghe s work) have been discarded D and m have been added

23 The advanced NN: non dimensionalisation Wave attack conditions Slope Toe Berm Crest and crown wall Roughness, «permeability» H m0t /L m 1,0t cotα d B t /L m 1,0t B/L m 1,0t R c /H m0t γ f β cotα inc h t/ /L m 1,0t h B/ /H m0t A c /H m0t D/H m0t m G c /L m 1,0t With respect to the EurOtop tool. «physically based» scaling, with wave length and wave height 14 non dimensional parameters use of cotα inc automatically calculated by the ANN (based on CLASH)

24 The advanced NN: outputs qk rt With respect to the EurOtop tool. The user can derive not only q but also Kr and Kt from the same ANN The predictions can be obtained from the committment of 500 runs (as well as in EurOtop)

25 The advanced ANN and EurOtop ANN Present CLASH; EurOtop 10 0 Zanuttigh et al., 2013 q ANN q s

26 The advanced ANN and Panizzo & Briganti (2007) Present Panizzo & Briganti (2007) Kt ANN Kt s Kt s

27 The advanced ANN and Zanuttigh et al. (2013) Present Zanuttigh et al. (2013) Kr ANN Kr s

28 Applications Wave reflection Sensitivity of ANN to T m 1,0 (rock permeable structure) K r,ann ANN predictions c.i. 97.5% Z & VDM, Tm 1,0,t

29 Applications Wave reflection Sensitivity of ANN to h/h m,0,t (rock permeable structure) K r,ann h/h m,0,t ANN predictions c.i. 97.5% Z & VDM, 2006

30 Applications Wave transmission Sensitivity of ANN to T m 1,0 (rubble mound, submerged breakwater) K t,ann ANN predictions c.i. 97.5% VDM et al., T m 1,0,t

31 Applications Wave transmission Sensitivity of ANN to h/h m,0,t (smooth, submerged breakwater) K t,ann h/h m,0,t ANN predictions c.i. 97.5% VDM et al., 2005

32 Conclusions and further work A new extended database ( tests) based on CLASH (2004) database was prepared including D, q, Kr and Kt where available include q, include Kr and include Kt; include both q and Kr; include both Kr and Kt. The use of dimensionless parameters based on physical considerations showed an improved prediction capability of q, Kr and Kt. The advanced ANN can predict q, Kr and Kt for one structure schematisation (easy for design purposes). Plan to distribute the.exe file in a user friendly environment.

33 Coasts, Marine Structures and Breakwaters Edinburgh, Sep 18-20, 2013 Thank you for your attention The support of the EC funded FP7 project THESEUS (Innovative technologies for safer European coasts in a changing climate), is gratefully acknowledged

34 Synthesis of ANN performance Comparison among ANNs: no early stopping, results of 500 runs WAVE OVERTOPPING q(#11 825) ANN RMSE WI R 2 Large errors (%) Original CLASH 0.32 ± ± ± CLASH + D 0.30 ± ± ± ZANUTTIGH ± ± ± WAVE REFLECTION K R (#7 413) ANN RMSE WI R 2 Large errors (%) Original CLASH ± ± ± CLASH + D ± ± ± ZANUTTIGH ± ± ± WAVE TRANSMISSION K T (#3 366) ANN RMSE WI R 2 Large errors (%) Original CLASH ± ± ± CLASH + D ± ± ± ZANUTTIGH ± ± ± , ,,, ; ,,,, ; log 1,

35 New ANN, Classifier Quantifier WAVE OVERTOPPING Classifier (and criteria) Criterion 50% Criterion 80% (80% 50%)/50% % wrong tot 5.52 % 9.30 % % % wrong class % 3.26 % % % wrong class % 4.80 % % WAVE OVERTOPPING Quantifier Criterion classifier RMSE WI R 2 # Large errors (%) no classifier ± ± ± % 50% ± ± ± % 80% ± ± ± % No significant improvement. Suggested to have the quantifier first and then the classifier.