Use this URL to cite or link to this record in EThOS:
Title: The analysis and application of artificial neural networks for early warning systems in hydrology and the environment
Author: Duncan, Andrew Paul
ISNI:       0000 0004 5372 7641
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2014
Availability of Full Text:
Access from EThOS:
Access from Institution:
Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warning Systems (EWS) might not wish to take the risk of implementing solutions perceived as containing unknown elements, despite the computational advantages that ANNs offer. This thesis therefore builds on existing efforts to open the box and develop tools and techniques that visualise, analyse and use ANN weights and biases especially from the viewpoint of neural pathways from inputs to outputs of feedforward networks. In so doing, it aims to demonstrate novel approaches to self-improving predictive model construction for both regression and classification problems. This includes Neural Pathway Strength Feature Selection (NPSFS), which uses ensembles of ANNs trained on differing subsets of data and analysis of the learnt weights to infer degrees of relevance of the input features and so build simplified models with reduced input feature sets. Case studies are carried out for prediction of flooding at multiple nodes in urban drainage networks located in three urban catchments in the UK, which demonstrate rapid, accurate prediction of flooding both for regression and classification. Predictive skill is shown to reduce beyond the time of concentration of each sewer node, when actual rainfall is used as input to the models. Further case studies model and predict statutory bacteria count exceedances for bathing water quality compliance at 5 beaches in Southwest England. An illustrative case study using a forest fires dataset from the UCI machine learning repository is also included. Results from these model ensembles generally exhibit improved performance, when compared with single ANN models. Also ensembles with reduced input feature sets, using NPSFS, demonstrate as good or improved performance when compared with the full feature set models. Conclusions are drawn about a new set of tools and techniques, including NPSFS and visualisation techniques for inspection of ANN weights, the adoption of which it is hoped may lead to improved confidence in the use of ANN for live real-time EWS applications.
Supervisor: Keedwell, Edward C. ; Savic, Dragan Sponsor: EPSRC ; UKWIR ; Environment Agency
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Neural Network ; ANN ; Ensemble ; Predictive model ; urban flood prediction ; bathing water quality ; water quality ; Bathing water directive ; Feature selection ; Neural pathway ; Machine learning ; classification ; non-linear regression ; receiver operating characteristic ; ROC ; evolutionary algorithm ; neuroevolution ; combined sewer overflow ; combined neural pathway strength analysis ; neural pathway strength diagram ; UCI dataset ; forest fire area ; Environment Agency ; manhole surcharge