Thesis (M.S.)--Missouri State University, 2008. / "December 2008." Includes bibliographical references (leaves 63-74). Also available online.

Thesis (M.S.)--University of Tennessee, Knoxville, 2009. / Title from title page screen (viewed on Nov. 5, 2009). Thesis advisor: Carol Harden. Vita. Includes bibliographical references.

Thesis (M.S.) -- University of Tennessee, Knoxville, 2006. / Title from title page screen (viewed on Sept. 19, 2006). Thesis advisor: Joanne Logan. Vita. Includes bibliographical references.

Thesis (M.E.S.)--The Evergreen State College, 2007. / Title from title screen viewed (1/17/2008). Includes bibliographical references (leaves 67-68).

Thesis (M. Phil.)--University of Hong Kong, 1993.

Thesis (M.Sc.)--University of Alberta, 2009. / A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Agricultural and Resource Economics, Department of Rural Economy. Title from pdf file main screen (viewed on September 20, 2009). Includes bibliographical references.

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.

Esta dissertação apresenta os resultados do estudo de monitoramento da qualidade de água na região hidrográfica da Baixada de Jacarepaguá através de coletas e posterior análise laboratorial realizadas na bacia hidrográfica experimental e representativa do Rio Morto. A bacia possui características predominantes peri-urbanas. / This thesis presents the results of the monitoring study of water quality in the river basin district of Jacarepagua marshland through collections and further laboratory analysis conducted in experimental and representative basin of the Dead River. The basin has peri- urban predominant features .

Esta dissertação apresenta os resultados do estudo de monitoramento da qualidade de água na região hidrográfica da Baixada de Jacarepaguá através de coletas e posterior análise laboratorial realizadas na bacia hidrográfica experimental e representativa do Rio Morto. A bacia possui características predominantes peri-urbanas. / This thesis presents the results of the monitoring study of water quality in the river basin district of Jacarepagua marshland through collections and further laboratory analysis conducted in experimental and representative basin of the Dead River. The basin has peri- urban predominant features .