machine learning for satellite-guided water quality monitoring

MACHINE LEARNING FOR SATELLITE-GUIDED WATER

QUALITY MONITORING

Marek B. Zaremba

Laboratoire de Systèmes Spatiaux Intelligents (LSSI)Département d’informatique et d’ingénierie

Université du Québec en Outaouais Gatineau, Canada

Vision-Geomatique, Gatineau, November 12, 2014

1. Machine Learning

2. Problems solved

3. Automated model development: multimodal data sets

4. Mission planning and optimization

5. Final Comments


OUTLINEOUTLINE


1. MACHINE LEARNING

Machine learning is a sub-field of artificial intelligence that is concerned with the design and development of algorithms that allow computers to learn the behavior of data sets empirically.

What is Machine Learning?

A major focus of machine-learning research is toproduce (induce) empirical models from data automatically.

WHY?This approach is usually used because of the absence of adequate and complete theoretical models.

Can’t you do anything

right?

Machine Learning Algorithms


About 2500 years ago Democritus wrote:

“Fools can learn from their own experience; the wise learn from the experience of others.”

Unsupervised learning

Vector QuantizationSelf-Organizing MapsEM algorithmHierarchical clusteringK-means algorithmFuzzy clusteringetc.

Supervised learning

As well as:

Reinforcement learningTransductive learningDeep learning

Machine learning task of inferring a function from labeled training data.

Supervised learning

Neural Networks

BackpropagationAutoencodersHopfield networksBoltzmann machinesRestricted Boltzmann MachinesSpiking neural networks

etc.

Support Vector MachinesSVMs map the training data into a higher-dimensional feature space via kernel mapping, and construct a separating hyperplane with a maximum error margin.

They learn complex nonlinear input-output relationships and adapt themselves to the data, using sequential training procedures.


Linear classifiersFisher's linear discriminantLogistic regressionMultinomial logistic regressionNaive Bayes classifierPerceptron


2. PROBLEMS SOLVED

Learning Algorithms – which are the best?

The No Free Lunch (NFL) theorem (Wolpert and Macready, 1995) has shown that learning algorithms cannot be universally good. Matching algorithms to problems gives higher average performance than does applying a fixed algorithm to all.

Hence:Experience with a broad range of techniques is the best insurance for solving arbitrary new problems

General classes of problems:

Classification Regression Optimization


Classification problems

Supervised and unsupervised

Ex. Water/Land cover classification

Regression problems

The use of machine learning can actually help us to construct multivariate, nonlinear mappings between satellite radiances and the suite of water products.


Example:Non-parametric inverse modeling architectures:

-Allow us to obtain complex bi-directional radiative transfer models;

-Production very fast;

-Can be adapted to different bio-optical models and applied in form of a NN library.


Optimization problems

If we start our search here

A local method will only find local extrema

Using ML techniques:


-1 0 1 2 3 4 5 60

20

40

60

80

100

120

140Chlorophyll-a Distribution

Chl

orop

hyll-

a co

ncen

trat

ion

mg/

m3

MCI-MERIS

Case study

Chlorophyll-a detection

-Using data from satellites and field spectrometers

3. AUTOMATED MODEL DEVELOPMENT: MULTIMODAL DATA SETS

Linear model(R2 = 0.679):

Parametric models

Examples:

Non-parametric models - data-driven models obtained using thestatistical learning process.

Neural Network technology:

Models


The problem …

Biased (statistics systematically different from the population parameter) and non-ergodic (distribution parameters vary in time) data sets

Biases are ubiquitous. With fusion of multiple datasets bias is often an issue (very relevant for climate variables). Yet, we typically need to fuse multiple datasets to construct long-term time series and/or improve global coverage.

If the biases are not corrected before data fusion we introduce further problems, such as spurious trends, leading to the possibility of unsuitable policy decisions.

So what can we do about this?.... we do not have a theoretical explanation (The Earth system is so complex, with many interacting processes, and often the instruments are also complex, this is not always possible to theoretically understand thecause of the bias and data issues from first principles).


Model development

Model development

Iterative Semi-Supervised Learning based data classification

Iterative Semi-Supervised Learning approach

Before and after the Iterative Semi-Supervised Learning procedure:

Model development - NN models

Objective: Optimization of the in-situ data acquisition process through the planning of an optimal ship trajectory.

4. MISSION PLANNING AND OPTIMIZATION

The path planning system generates an optimal path with the goal of maximizing the number and the value of the collected samples during the acquisition mission.

The acquisition mission can be varied depending on the strategy applied to collect the samples for different water pollutants (Chl-a, TSS, DOC, …) : Maximum gradient following strategy Maximum concentration areas Uniform coverage strategy

Any strategy can be represented by an objective function.

The strategies can be applied depending on the surrounding environment and the data acquisition mission constraints.

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= ==

JN

iK

S

KJ

S

JJi DtNVC

1 11

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Broader context of Hybrid Intelligent Control

ψ

P

Mapping and environment

modeling

α

Planning

E

Context

Reactive Control

E

ΨE

π

Logic Statement

Cost function

Reactive level

Deliberative level

ΨR

The deliberative level control architecture formally defined as:

},,,,{ απψ PEDC =

The reactive level deals with the obstacles and the ship maneuverability


Classes of Search Techniques:

GAs use different: Representations (chromosomes) Mutation and Crossover mechanisms Fitness functions

Genetic Algorithms approach


Multi-dimension chromosomes and multi-point crossover mechanism were applied to produce an optimal global path.

Multi-point crossover:

High value water sample patch

Start point

BC D E

ED

G

High value water sample patch

Target point

F

BC

F

Crossover point

This approach does not require a complete knowledge of the environment and can replace traditional navigation planning systems.

Genetic Algorithms - a class of probabilistic optimization algorithms inspired by the biological evolution process.


TSS Map

EXPERIMENTAL RESULTS

MCI Map

Satellite images (MODIS) of Lake Winnipeg


TSS and Chl-a (maximum values) samples acquisition

longitude latitude Value-97.071594 52.271004 0.3949-97.15443 52.271156 0.3678-97.0877 52.163826 0.4037-96.9688 51.998085 0.4001-96.94884 51.884686 0.4083-97.10551 51.87565 0.4532-97.17112 51.886684 0.4526-97.17112 51.886684 0.4378-97.19144 51.804962 0.4324-97.25087 51.705112 0.4360-97.27605 51.62972 0.4971-97.27722 51.555775 0.6226-97.27228 51.47804 0.6288-97.258446 51.456432 0.6196-97.213425 51.470726 0.6044-97.187546 51.485546 0.5692-97.18434 51.53722 0.5521-97.22941 51.522934 0.5597-97.19398 51.577347 0.3957-97.13055 51.624245 0.5948-97.10014 51.69328 0.3663-97.040436 51.83706 0.4298-97.08387 51.95991 0.4200-97.13075 52.102375 0.3001-97.14458 52.231052 0.4037-97.08629 52.273468 0.3931



5. FINAL COMMENTS Machine learning:

• Focuses on problems that otherwise cannot be solved;• A tool of fighting complexity;• Employs cognitive properties of intelligence:

generalization, attention focusing, combinatorial search, …

Extremely useful for automatic decision making.

Very well suited for monitoring environmental phenomena.

But:

Use of context is necessary for identifying complex patterns.

No single technique/model is suited for all problems.

“All models are wrong …… some models are useful”

George Box

machine learning for satellite-guided water quality monitoring

Technology

canada visiongeomatique

learning research

development of algorithms

labeled training data

qubec en outaouais gatineau

major focus of machine

multimodal data sets

behavior of data sets