A decision support system for water quality in the Wadden Sea

Size: px

Start display at page:

Download "A decision support system for water quality in the Wadden Sea"

Jonathan Paul
5 years ago
Views:

1 47th Liege Colloquium Luigi Ceccaroni, Meinte Blaas, Marcel R. Wernand, Filip Velickovski, Anouk Blauw and Laia Subirats Liège, May 8 th 2015 A decision support system for water quality in the Wadden Sea

2 Index INDEX 1.- Optical monitoring in a citizen-science context 1. Item Optical properties and marine-environment status 2. Item Decision support system 3. Item Target users and applications 4. Item Conclusions 2

3 Index Optical monitoring in a citizen-science context 1. Item 1 Combination of data collected by: 2. Item 2 3. Item 3 4. Item 4 a distributed group of people (Citclops crowdsourcing) publicly available data: government satellites research-institutes campaigns Interpretation by context-aware artificialintelligence techniques 3

4 Index Optical properties and marine-environment status 1. Item 1 Optical properties as proxies of: 2. Item 2 3. Item 3 4. Item 4 sewage impact dissolved organic matter sediment load natural pigmentation And more generally: marine-environment status anthropogenic pressures on resources natural causes of abnormal conditions 4

5 Index Focus on color (FU) 1. Item 1 Dependent mostly on the optically active 2. Item 2 3. Item 3 4. Item 4 substances: SPM, algae, CDOM (and POC, TOC) Related to light extinction (Kd) and Secchi depth (SD), also dependent on the same substances Presence of the optically active substances steered by: currents, waves, wind, insolation, temperature, nutrients (TotN, TotP. PO4, NO3, NO3NO2, NH4, SiO2) Salinity as a proxy for river water, which carries most (not all) nutrients into the sea 5

6 Study area in the Wadden Sea over a fixed study period ( ) 6

7 Index General objectives 1. Item 1 Assess and use available marine data: FU color 2. Item 2 3. Item 3 4. Item 4 index, suspended particulate matter, TSM, dissolved organic carbon, light extinction, Secchi disk depth, chlorophyll-a, waves height, river input, weather Learn a model of the target variable FU color at future points (2 days, 4 days, 7 days) Evaluate model s prediction of FU using 10-fold cross-validation Integrate model into Citclops Data Explorer Marine Data Analyser 7

8 Index Available data sources 1. Item 1 In-situ: SPM, chlorophyll-a, DOC, Kd ( ) 2. Item 2 3. Item 3 4. Item 4 average time resolution: 2 data points per month (Deltares) In-situ: River input (1 data point per day), waves height (1 data point per hour) (Deltares) Satellite: MERIS instrument: FU, chlorophyll-a ( ) - time resolution: daily (missing data on cloudy days) In-situ: TSM, FU, chlorophyll-a ( ) - time resolution: every 2 min during daylight (some missing periods) (NIOZ) 8

9 Jetty Data - Daily means Mar 2013 Sep 2013 Oct 2014 Feb

10 Machine-learning pipeline Extract data from source Convert to data array Parsing Feature preparation Convert data into feature vectors + target value Select feature variables, amount of past time points Feature Selection Model selection Train and test a set of models (decision trees, SVM, Bayesian) Evaluate best model on unseen test set Final evaluation 10

11 Converting to three class classification problem 11

12 Using mean daily values (example: TSM) 30 th Apr 6 th May 12 th May 18 th May 24 th May

13 Using mean daily values, sometimes few data points (example: TSM) 14 th Dec 18 th Dec 22 nd Dec 26 th Dec

14 Short-term correlation to tide (Dominant semi-diurnal lunar tidal component (M2) with a period of 12:25 h) 28 th Sep 1 st Oct 4 th Oct

16 Index Knowledge coming from pre-processing 1. Item 1 Clear relation among color, tides, and other drivers: 2. Item 2 3. Item 3 4. Item 4 wind (which steers both waves and currents), river/sluice discharge and biology Short-term forecasting (within a tidal period): not possible without de-tiding the signals Longer-term forecasting: possibly improved by detiding the signals 16

17 Feature vector at time t Training data Time (days) t TSM Chl-a FU FU t+2 (target) FU t FU t-1 Feature and targetvariable preparation Chl t Chl t-1 TSM t 17

18 Index Highly flexible system with real-time processing 1. Item 1 Data set 2. Item 2 Feature configuration 3. Item 3 Machine-learning technique 4. Item 4 Temporal range of prediction Experiments and examples of results 18

19 Wave height TSM Chl-a Feature configuration ML technique Support Vector Machine C (soft-margin) : 1.0 Kernel : radial basis FU Number of examples for training 415 Blind predictor benchmark 48% Distribution of target FU classes decrease stable increase 27% 48% 25% Accuracy (10-fold cross- validation) 53% 19

predictor benchmark 35% Distribution of target FU classes decrease

20 Wave height Feature configuration ML technique TSM Chl-a Random Forest n_trees: 10 FU Number of examples for training 359 Blind predictor benchmark 35% Distribution of target FU classes decrease stable 33 % 32 % increase Accuracy (10-fold cross- validation) 35 % 52 % 20

21 Wave height TSM Chl-a FU Feature configuration ML technique Decision Tree max depth: 10 Number of examples for training 345 Blind predictor benchmark 35% Distribution of target FU classes decrease stable 34 % 31 % increase Accuracy (10-fold cross- validation) 35 % 45 % 21

22 Index Target users and applications 1. Item 1 Data from citizens, marine scientists and coastal 2. Item 2 3. Item 3 4. Item 4 planners to citizens and policy makers Plenty of potential applications: to simulate environmental crises to chart emergency-management plans of the coastal zone to provide sea farmers with bulletins about algal blooms to maximize citizens experience in activities in which water quality has a role to provide citizens with powerful, userfriendly tools of environment interpretation 22

23 Index Conclusions and future work 1. Item 1 First steps in real-time prediction of water color 2. Item 2 3. Item 3 4. Item 4 using easily-available data sources as context Transition towards a citizen-science scenario Use of citizen data to complement remotesensing and official in-situ data Use of machine-learning techniques in a very flexible framework Short-term impact on policy decision via decision support system 23

24 24

25 25

26 First stage: (Handcraft) Third stage, unit II (Prototype II, 3D print external LEDs) Second stage, unit I (Prototype I, 3D print, internal LED) 26

27 Slide 27 27

28 Slide 28 28

47th Liege Colloquium Luigi Ceccaroni, Barcelona Digital Technology Centre, Spain; 1000001 Labs, Spain Meinte Blaas, Deltares, The Netherlands Marcel R.

29 47th Liege Colloquium Luigi Ceccaroni, Barcelona Digital Technology Centre, Spain; Labs, Spain Meinte Blaas, Deltares, The Netherlands Marcel R. Wernand, Royal Netherlands Institute for Sea Research, The Netherlands Filip Velickovski, Barcelona Digital Technology Centre, Spain Anouk Blauw, Deltares, The Netherlands Laia Subirats, Barcelona Digital Technology Centre, Spain Liège, May 8 th 2015

Zu-Tao Ou-Yang Center for Global Change and Earth Observation Michigan State University

Zu-Tao Ou-Yang Center for Global Change and Earth Observation Michigan State University Ocean Color: Spectral Visible Radiometry Color of the ocean contains latent information on the water qualitycdom,