Bayesian network-based predictive analytics applied to invasive species distribution

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Bayesian network-based predictive analytics applied to invasive species distribution

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Bayesian network-based predictive analytics applied to invasive species distribution

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Bayesian network-based predictive analytics applied to invasive species distribution

Wisdom Mdumiseni Dlamini

-PhD Student / Director of Nature Conservation-

University of South Africa / Swaziland National Trust Commission

- Aims
- Introduction
- Invasive alien plant species distribution modelling
- Bayesian networks (BNs)
- Methods (Predictive analytics –data mining using BNs)
- Findings
- Conclusions and on-going research.

- Investigate suitability of Bayesian networks (BNs) for species distribution (geospatial) data analysis (Chromolaena odorata and Lantana camara cases in Swaziland)
- Apply BN learning for geospatial predictive analytics (data mining) and ecological knowledge discovery
- Demonstrate potential/usefulness of BN-based data mining for geospatial analysis and decision-making

- Invasive alien plants are problematic in Swaziland and the world over.
- 80% of country invaded and about 400 invasive plant species in total
- Four plant species identified and declared a disaster in 2005 due to threat the economy and food security in Swaziland (Chromolaena odorata, Solanum mauritiunum, Caesalpinia decapetala and Lantana Camara)
- Degraded rangelands, reduced water flows in streams/rivers, threat to native flora and biodiversity.
- Estimate cost: ~3% of GDP to control these.
- Need for geospatial information for control, planning and decision-making and understanding their ecology

Chromolaenaodorata

(Photos R. Mackenzie)

Lantana camara

Photo: K Braun

Photo: E.M. Ossom

- All species distribution modeling approaches model the function approximating the true relationship between the environment and species geographic occurrences/distribution.
- Objective is to estimate some function f = μ(Gdata, E) - i.e. applying an algorithm to data given an environmental space E to estimate G (distribution)
- Used in ecology to:
- model present, past and future distribution of species
- predicting disease spread
- predicting invasive species spread
- niche conservation

- Many algorithms do not handle asymmetric data
- Many don’t handle interaction effects
- Some do not handle nominal/categorical environmental variables (e.g. vegetation types)
- Many stochastic algorithms present different solutions even under identical parameterization and input data
- ‘real’ distribution of species not known, so we do not know when models are making mistakes and when are filling knowledge gaps.

- Which factors determine the distribution of species:
- The answer is often complicated (but important)
- Species have physiological tolerances, migration limitations and evolutionary forces that limit adaptation
- A starting point for physiology may be traits
- A starting point for abiotic factors is often climate
- Climate variables often also correlate with other variables (e.g. elevation, land cover)

- Need for algorithms that will address the issues in previous slide
- Additionally, conventional SDMs are correlative and do not adequately capture causal species-environment relationships and ecological knowledge
- There remains a critical gap in the understanding of processes that induce observed invasion spatial patterns

- A BN is a graphical model that encodes probabilistic relationships among a set of variables
- Two components:
- Directed Acyclic Graph (DAG)
- Probability Table

- Variables depicted as nodes
- Arcs represent probabilistic dependence between variables
- Conditional probabilities encode the strength of dependencies
- Lack of an arc denotes a conditional independence

- Bayes theorem : the posterior probability for given D and a background knowledge :
p(/D, ) = p( / ) p (D/ , )

P(D / )

Where p(D/ )= p(D/ , ) p( / ) d

Note : is an uncertain variable whose value corresponds to the possible true values of the physical probability

Bayesian networks

A Bayesian network represents potentially causal patterns, which tend to be more useful for intelligent decision making

Bayesian network example

A

B

However, algorithms for constructing Bayesian networks from data were not designed to discover interesting patterns

C

Combined novel feature selection and structure learning is interesting by nature

D

Causality + interestingness tends to improve Usefulness

- BNs can readily handle incomplete (missing) data
- BNs allow one to learn about causal relationships
- BNs readily facilitate use of prior knowledge
- Bayesian methods provide an efficient method for preventing the over fitting of data (there is no need for complex pre-processing and data transformation)
- BNs also handle uncertainty very well
- Graphical nature readily allows for interpretation of interrelationships/interactions between variables

- Identify the modelling goals
- Identify many possible observations/variables that may be relevant to the problem
- Determine what subset of those observations is worthwhile to model
- Organize the observations into variables having mutually exclusive and collectively exhaustive states.
- Build a Directed Acyclic Graph that encodes the assertions of conditional independence
- Use the graph to describe the ecology species invasion patterns and processes

- “Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data”(Fayyad et al., 1996)
- Focus on the quality of discovered patterns
- A lot of research on discovering valid, accurate patterns
- Little research on discovering potentially useful patterns

- Data Mining consists of extracting patterns from data, and is the core step of the knowledge discovery process

- Species distribution data obtained from 2009 aerial survey (~50m altitude flight throughout country) – GPS coordinates from experts.
- 115 geospatial data sets covering biophysical, climatic, socio-economic and topographic data.
- All processed to rasters/grids of uniform size (~1km)
- Raster geodatabase created and exported to CSV file

- CSV file imported to Weka (open source machine learning/data mining package) for analysis
- Most species occurrence data was imbalanced (i.e. too many absence (-ve) than presence (+ve) instances) - Sampling variation and/or noisy data may mislead the BN construction method, further contributing to the discovery of a sub-optimal BN.
- Data balancing implemented using Spread Subsample approach

- Discretization (using Minimum Description Length (MDL) criterion with Kononenko correction)

- The problem of constructing the optimal net is too complex in large datasets

- Hybrid approach: GainRatio Attribute Evaluation followed by Peng’s maximum Relevance minimum Redundancy (mRmR) subset evaluation algorithm based on Correlation-based Feature Subset (CFS) selection and Symmetric Uncertainty
- The CFS search was done via particle swarm optimization (PSO)
- Done to reduce data dimensionality and redundancy whilst simultaneously ensuring that only relevant, predictive and uncorrelated features (variables) are selected

- Various structure learning approaches being implemented and tested on final subset of variables.
- Both local and global search strategies were implemented using Bayes score.
- Methods based on search guided by a scoring function
- Iteratively create candidate solutions (BNs) and evaluate the quality of each created network using a scoring function, until a stopping criteria is satisfied
- Sequential methods consider a single candidate solution at a time
- Population-based methods consider many candidate solutions at a time

- Conditional independence based algorithms also used (CI and Inductive Causation (ICS) to extract causal relationships.
- Not scalable to datasets with many variables (attributes)

- Markov blanket applied in all cases (i.e. all variables constitute the set of parents and children and parents of children of the class variable).

- Examples of sequential method
- Hill climbing algorithm starts with an empty network and at each iteration adds, to the current candidate solution, the edge that maximizes the value of the scoring function
- K2 algorithm requires that the variables be ordered and the user specifies a parameter: the maximum number of parents of each variable in the network to be constructed

- Both are greedy methods (local search), which offer no guarantee of finding the optimal network
- Population-based methods are global search methods, but are stochastic, so again no guarantees

C. odorataBN

NB: the probabilistic dependencies between variables

Note the complexity on spatial distribution highlighting a complex interplay of factors

Identified invasion hotspots not identified by training data but verified with independent tree atlas data

L. camaraBN

NB: the probabilistic dependencies between variables

Identified invasion hotspots not identified by training data but verified with independent tree atlas data

- Distinguishing properties of BNs:
- their ability to reduce the joint probability distribution of the model into a set of conditional probabilities
- their capability to express model uncertainties,
- propagate information quickly,
- represent complex topologies,
- combine domain knowledge with hard data, and update model parameters as new information becomes available.

- We proposed a method for integrating feature selection and BN learning algorithms in non-spatial and geospatial data mining
- Algorithms for constructing Bayesian networks
- Discover potentially causal, more useful patterns
- Discover surprising patterns, potentially more useful

- Algorithms for constructing Bayesian networks
- Hopefully, combining the “best of both worlds”, increasing the chance of discovering ecological patterns and processes useful for intelligent decision making and invasion plant species management
- Ongoing research: computational implementation of the proposed method and ecological knowledge discovery to 14 other species.

- Geospatial predictive analytics: an emerging field in ‘big data’ era.
- Applicability of our method to broader natural resource management and geospatial analysis in particular where both prediction and decision-making are paramount.
- Accessibility and sharing are crucial if we are to reap maximum benefits from geospatial data
- (A)Spatial data repositories/SDI could act as good data mines from which to extract patterns to solve various socio-economic/NRM problems.

Questions ??

Thanks you for listening!