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AISB event Bulletin Item

CALL FOR PAPERS: Machine Learning in Water Systems symposium, AISB 2013, 3-5 April 2013, Exeter, UK - EXTENDED DEADLINE

Date of one-day MaLWaS Symposium: Thu 4 April 2013

Themes of the symposium

The symposium seeks to bring together computer scientists, hydrologists, water engineers, and 
environmental scientists, to explore the key issues governing the successful application of 
machine learning (e.g. data-driven models and optimisation techniques) to water systems.
Papers are invited that explore issues of model and/or optimisation strategy design, selection 
and application, particularly those that investigate one or more of the following issues within 
the context of water systems:
 Water system knowledge requirements for model development
 Choice of machine learning and/or data-driven modelling technique:
 ANN, SVM, SOM, BBN, RBF, GP, ANFIS, Model/Decision Tree, CA etc
 Choice of learning/optimisation process: 
GA/EA, PSO, ACO, gradient-descent-based, backpropagation etc
 Machine learning innovations as applied to water systems
 Novel combinations of techniques to solve real-world water-related problems
 Hybrid modelling techniques to improve computational performance and model accuracy
 Model design and meta-heuristics
 Metrics and measures to quantify and evaluate model performance
 Operational setting and requirements' effect on model development
 Move from "black box" to "grey box" models
Papers that compare models and/or methods are particularly encouraged.
Important Dates and Submissions
The symposium welcomes both extended abstracts (4 pages), and full papers (8 pages).
 Please use the templates from last years convention, accessible from
Submissions are welcomed by 11:59pm GMT, 14th January 2013 to the MALWAS2013 easychair link. 
For further information please contact
The best papers will be considered for publication in Journal of Hydroinformatics.
 Extended abstract  and full paper submission deadline: 14 January 2013
 Notification of acceptance/rejection decisions: 11 February 2013
 Final versions of accepted papers (Camera ready copy): 4 March 2013
There will be a separate proceedings for each symposium, produced before the convention. Each 
delegate will receive a memory stick containing the proceedings of all the symposia.
Programme Committee
Prof Dragan Savi (Chair) - Professor of Hydroinformatics and Head of Engineering - Centre for 
Water Systems - University of Exeter
 Mr Andrew Duncan  - Centre for Water Systems - University of Exeter
 Dr Michele Guidolin - Centre for Water Systems - University of Exeter
 Dr Michael Hammond - Centre for Water Systems - University of Exeter
 Dr Chris Hutton - Centre for Water Systems - University of Exeter
Detailed Symposium Description
The emergence of water, alongside energy and food, as one of the three major, interlinked, 
global environmental security issues provides abundant challenges and opportunities for the 
application of Machine Learning to such problems as optimisation of water distribution and 
drainage networks design and operation, modelling and prediction of fluvial, pluvial, urban 
and coastal flooding, sediment transport and water quality issues. Advances in GIS, remote 
sensing and weather forecasting techniques mean that environmental data is becoming increasingly 
abundant at the same time as demands for solutions and tools to work on these problems become 
more urgent.
Numerical models have been applied widely to improve the understanding and operational management 
of natural and manmade water systems. Traditionally, so-called physically-based models have been 
applied for such purposes. However, such models are often computationally demanding, and frequently
require significant data to constrain model structures and parameters.  Data-Driven Models (DDMs) 
based on Machine Learning techniques - which seek to provide a mapping between the inputs and 
outputs of a given system, with little prior process knowledge  have emerged as an attractive 
option for prediction and classification in water systems. The principal benefit of such DDMs is 
their fast execution time, which allows many more model evaluations for a fixed computational 
budget. Such models have been applied widely to address a variety of problems within water systems 
modelling, including: system simulation (e.g. rainfall-runoff modelling/rating curve prediction) 
when trained on measured data, and also when employed as metamodels and trained to emulate models 
with a stronger physical (or process) basis; to improve the speed of the optimisation procedure by 
acting as a surrogate model to the full fitness evaluation; to correct systematic errors in 
physically-based models during real-time forecasting; to provide uncertainty bound predictions 
during model forecasting when trained on uncertainty bounds derived from offline calibration; and 
in classification (for example of predicted severity of a hazard or exceedances of regulatory 
Despite their potential benefit, successful application of machine learning techniques is not 
straightforward. A variety of machine learning techniques, optimisation methods and evaluation 
procedures have been applied in the research literature. It is not always clear which methods 
will perform best in different settings, and how choices made will influence performance. As an 
example, different machine learning techniques might perform best depending on how their 
performance is evaluated within a given operational setting. Furthermore, although such methods 
are technically black-box models, system understanding may be required to choose the best input 
variables, and tailor the approach to the operational setting in question. With a view towards 
sharing the interdisciplinary knowledge required to make appropriate methodological decisions, 
papers are invited that explore issues of model design and application.