William J. Walley scite author profile

The Biological Monitoring Working Party (BMWP) score system was introduced in 1980 to provide an index of river water quality for England and Wales based on aquatic macro-invertebrates. The score allocated to each taxon was set by a group of expert river biologists based upon their collective experience of the taxon's sensitivity to organic pollution (Hawkes, 1997). This paper describes an objective reappraisal of these subjectively-derived scores through the statistical analysis of a large and comprehensive database of field samples. Using the analytical approach of Walley & Hawkes (1996, 1997

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A computer-based development of the Biological Monitoring Working Party score system incorporating abundance rating, site type and indicator value

Walley

Hawkes

1997

Water Research

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Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain

Walley

Fontama

1998

Water Research

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Using machine learning techniques in the construction of models. II. Data analysis with rule induction

Džeroski

Grbovič²,

Walley

et al. 1997

Ecological Modelling

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A reappraisal of saprobic values and indicator weights based on slovenian river quality data

Walley

Grbovič²,

Džeroski

2001

Water Research

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Biological Monitoring: a Comparison between Bayesian, Neural and Machine Learning Methods of Water Quality Classification

Walley

Džeroski

1996

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Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes

Chong

Walley

1996

Artificial Intelligence in Engineering

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William J. Walley

A computer-based reappraisal of the Biological Monitoring Working Party scores using data from the 1990 river quality survey of England and Wales

Revision of the Biological Monitoring Working Party (Bmwp) Score System: Derivation of Present‐only and Abundance‐related Scores From Field Data

A computer-based development of the Biological Monitoring Working Party score system incorporating abundance rating, site type and indicator value

Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain

Using machine learning techniques in the construction of models. II. Data analysis with rule induction

A reappraisal of saprobic values and indicator weights based on slovenian river quality data

Biological Monitoring: a Comparison between Bayesian, Neural and Machine Learning Methods of Water Quality Classification

Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes

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