Thesis (Ph. D.)--University of Alberta, 2010. / Title from pdf file main screen (viewed on June 18, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Environmental Engineering, Department of Civil and Environmental Engineering, University of Alberta. Includes bibliographical references.

Thesis (M.S. - Microbiology and Immunology)--University of Arizona, 1984. / Includes bibliographical references (leaves 52-55).

Thesis (M. S. - Geosciences)--University of Arizona, 1992. / Some pages are not numbered. Includes bibliographical references (leaves 36-37).

Thesis (M.S. - Hydrology and Water Resources)--University of Arizona. / Includes bibliographical references (leaves 161-165).

Thesis (M. App. Sc.)--Carleton University, 2002. / Includes bibliographical references (p. 166-174). Also available in electronic format on the Internet.

This work focuses on the use of Artificial Intelligence (AI), and in particular Machine Learning (ML) to tackle quality and quantity aspects of both surface water and groundwater. Traditionally, river water quality modelling and contaminant transport in groundwater studies resort to the solution of physical-based (PB) equations, which aim to define a conceptual model of reality. The complexity of the processes involved, in some cases undisclosed or indiscernible, calls for a sensitive parameterization by the modeler. For such reason, the PB models can be limited by the complexity of the system, the availability of data, and the consequent need for simplifying assumptions. On the other hand, ML models are data-driven and rely on algorithms to identify patterns in data. These techniques aim to extract a surrogate representation of the reality by learning existing correlations in data. They can handle complex and non-linear relationships between variables and can be more flexible and adaptable to new environments. However, they are directly affected by the quality and quantity of available data, requiring larger datasets than PB models. To explore the potential of these methods in addressing surface and groundwater challenges,we experimented with different algorithms in three distinct applications. First, we compared two ML techniques for a water quality catchment-scale model and the most performing was then employed to fill the gaps in environmental time series and to enhance the prediction of a PB model in the groundwater context. Therefore, in the first part of this work, a water quality model of the Adige River Basin is presented and discussed. For this purpose, Random Forest (RF) and Dense feed-forward Neural Network (DNN) were applied and compared to a standard linear regression (LR) approach and an Importance Features Assessment (IFA) of the drivers was performed. DNN showed to be more flexible and effective in detecting non-linear relationships than RF. LR performed at a satisfactory level, similar to RF and DNN, only when drivers linearly correlated to the observational variable were used, and a limited fraction of variability was explained. However, important drivers, non-linearly related to the water quality variables of interest introduced a significant gain when DNN was used. Regarding the variables investigated, water temperature and dissolved oxygen were modeled accurately, using RF or DNN, and sufficient accuracy was obtained by using the minimum information available, represented here by the Julian day of the measurements embodying the seasonality. The other variables showed instead a more balanced influence by the complete set of drivers, appreciable in the IFA procedure for DNN and RF, and a geogenic origin and anthropogenic disturbances were confirmed for chemical contaminants. The proposed analysis, by means of ML algorithms and through the IFA of the drivers, can be applied to predict spatial and temporal variability of contaminant concentrations and physical parameters and to identify the external forcing exerting the most relevant impacts on the dynamics of water quality variables. The second part of the thesis investigated the use of the DNN algorithm to gap-fill time series measurements, for daily flow rate and daily water temperatures from different sites downstream of the Careser glacier, in Pejo valley (northeastern Italy). Thus, an in-depth analysis of the streamflow response to the hydrological regime alterations of the glacier was carried out, through the reconstruction of the time series of the flow rate measured at a gauging station downstream the glacier, in the period 1976-2019. The water temperature time series, instead, were correlated to the macro-invertebrate population’s statistics in the same period at four sites along the Careser stream from the glacier to the reservoir immediately downstream the Careser Baia gauging station. In the first step, the water temperature was modelled just through the Julian day and air temperature information and, subsequently, precipitation, reconstructed flow rate, and evapotranspiration were introduced for sensitivity analysis of the features. With air temperature projections, the DNN model of the water temperature was also applied to simulate future scenarios up to 2050, considering different emission pathways. In this case, DNN proved to be a reliable tool for gap-filling the observational time series, even for time series with many gaps. The reconstructions of the water temperature allowed us to estimate the delay between the warming in air and water temperature and the effect on the biological invertebrate species in the glacier streams. The sensitivity analysis of the features was again key in underlining the contributions of the forcing available, unveiling the combined effects of the warming in air temperature and the decline of flow rate on the water temperature increase. The in-depth analysis of the flow rate revealed, besides the dramatic reduction of streamflow, the anticipation of the summer peak and the negligible influence of the precipitation in these alterations. Lastly, the framework for an ML-PB hybrid model in the context of contaminant transport by groundwater was presented. In this procedure, the contaminant concentration at several sampling locations was associated with physical parameters characterizing the aquifer. Through a synthetic case, a DNN model was employed to predict the physical parameters and a simplified PB equation was used to project the concentration into the future. The analysis demonstrated the capability of DNN to predict physical parameters by capitalizing on the information contained in the available concentration measurements. The thesis is articulated through 7 chapters. In Chapter 1, a broad overview of Machine Learning is presented, with its specific applications in Water sciences and the consequent motivations and objectives of this research. In Chapter 2 the main Machine Learning basic concepts are clarified and presented, in order to set the floor for the successive developments in which ML is applied to surface and subsurface hydrology. Chapter 3 covers the Machine Learning and statistical algorithms employed for modeling in the current research. In Chapter 4, Adige water catchment case study is presented and discussed. In Chapter 5, the gap-filling time series procedure for Careser case study is presented for both the variables investigated. In Chapter 6 the results of the hybrid Machine-Learning Physics-Based application of a groundwater model on synthetic data are presented. Finally, remarks and conclusions are summarized in Chapter 7, which provides also perspective work for these applications.

Thesis (Ph. D.)--University of Nevada, Reno, 2008. / "August, 2008." Includes bibliographical references (leaves 140-147). Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2008]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web. Library also has electronic version on CD-ROM

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 65-70). Also available on the World Wide Web.

Mine abandonment and the discharge of contaminated mine water is recognised globally as a major source of surface water and groundwater pollution. Contamination generally arises from the oxidation of sulphide minerals, principally pyrite, by the mining process, and the subsequent chemical reactions can lead to the discharge of mineralised, often acidic, iron, and sulphate rich waters. In many historically mined river catchments, mine water discharge is the main cause of poor water quality. Within the UK, managing the legacy of abandoned mines is one of the principal challenges presented by modern environmental legislation, particularly the EU Water Framework Directive, a challenge that is exacerbated by the diverse and widespread nature of historical mining. The impact and hazard associated with abandoned mining in one of the UK’s most intensively mined regions, the Almond River Catchment, Scotland, was examined via: 1) a detailed GIS mapping and investigation of historical mining processes in the catchment, 2) mine site discharge sampling, 3) detailed site investigations, 4) geochemical modelling of four mine waste sites and 5) analysis of temporal and spatial river water quality in the catchment. The results are then brought together to produce a catchment scale mine water hazard map. Mapping has identified over 300 mine sites in the catchment including coal, oil shale and ironstone mine wastes and flooded coal and oil shale mines. The historical development of oil shale retort methods has been shown to have an impact on potential hazard. Sampling of discharge waters from the different mining activities, in conjunction with detailed mineralogical analysis and geochemical modelling at the four mine waste sites has characterised the main hazards. Ironstone and pyrite bearing coal mine wastes discharge waters with highly elevated Fe and sulphate concentrations, up to 160mgl-1 and 1900mgl-1 respectively, due to extensive pyrite oxidation and acid generating salt dissolution (principally jarosite). Coal mine wastes show variable mineralogy, due to the diverse nature of coal bearing strata, and discharge waters with variable chemistry. Oil Shale mine wastes are generally depleted in pyrite due to historic processing and discharge low sulphate waters with moderately elevated Fe concentrations, up to 5mgl-1. Flooded coal mines discharge sulphate dominant alkaline waters, due to the availability of carbonate minerals in the mine complex, with elevated Fe concentrations, up to 50mgl-1, while flooded oil shale mines discharge waters with moderately elevated Fe concentrations, up to 4mgl-1, due to lower pyrite content in mine strata and reduced availability of oxygen related to mine abandonment age. Once in the surface water environment iron and sulphate display significant concentration-flow dependence: iron increases at high flows due to the re-suspension of river bed iron precipitates (Fe(OH)3); sulphate concentrations decrease with increased flow as a result of dilution. Further examination of iron and sulphate loading at low flows indicates a close correlation of iron and sulphate with mined areas; cumulative low flow load calculations indicate that coal and oil shale mining regions contribute 0.21 and 0.31 g/s of iron, respectively, to the main Almond tributary. Decreases in iron loading on river sections demonstrate the deposition and diffuse storage of iron within the river channel. This river bed iron is re-suspended with increased flow resulting in significant transport of diffuse iron downstream with load values of up to 50 g/s iron. Based on this hazard classification, a catchment scale mine water hazard map has been developed. The map allows the prioritisation of actions for future mine water management.

Thesis (M.S. - Hydrology)--University of Arizona, 1984. / Includes bibliographical references (leaves 68-72).