Climate change, global warming and anthropogenic disturbances are threatening freshwater ecosystems globally. The protection and preservation of freshwater environments, its biodiversity and all of its services for human well-being requires comprehensive knowledge of the impacts that climate change and anthropogenic disturbances have on freshwaters and freshwater species. In-depth knowledge needed for conservation strategies can be established through versatile assessments. Quantitative assessments and the investigation of prevailing environmental relationships within ecosystems constitute the basis for sustaining freshwater systems. However, it is a great challenge to quantify the multifaceted effects of climate change and to broaden the understanding of complex environmental relationships. This thesis aims at contributing to an extension of the understanding of climate change impacts on freshwater ecosystems and environmental relationships, which implies the provision of useful guidelines for the protection and preservation of freshwaters. For this, various statistical approaches based on comprehensive data sets are applied at different scales, ranging from local to global assessments. In particular, five research studies investigating the (1) water quality-nutrient and temperature relationships in European lakes, (2) drivers of freshwater fish species distributions across varying scales in the Danube River delta, (3) globally derived thermal response curves and thermal properties of native European freshwater species, (4) differences between thermal properties derived from native and global range data, and (5) thermal performances of freshwater fish species for different life stages and different global future dispersal scenarios are presented to address the effects of environmental change. Main results of this thesis comprise various aspects of conservation implications and planning. (i) The first study outlines drivers influencing water quality through studying multi-dimensional relationships and compares different modelling techniques in order to outline models that are suitable for the identification of complex driver interactions. (ii) The second study addresses scale effects on the performance of species distribution models, which are commonly used for assessments of climate change impacts, and identifies key predictors driving distributions for the varying scales and studied species. (iii) The third study parameterizes thermal responses of species from different taxonomic groups and assesses the potential resilience in terms of warming tolerance and additional thermal properties as well as the influence of future rising temperatures on current distributions. (iv) The fourth study quantifies the differences in thermal response curves and thermal properties for freshwater fishes derived from global and continental data in order to clarify the need for using global range data in studies making suggestions for conservation planning. (v) The last study estimates the impact of changing climatic conditions on species distribution ranges of two fish species for different time periods by including biotic information about thermal performances for various life stages. Overall, this thesis contributes to the broad field of studying consequences and impacts of climate change on freshwater ecosystems. By applying statistical methods tailored to the underlying investigations, useful implications for conservation planning are derived.

The use of machine learning techniques in tackling hydrological problems has significantly increased over the last decade. Machine learning tools can provide alternatives or surrogates to complex and comprehensive methodologies such as physics-based numerical models. Machine learning algorithms have been used in hydrology for estimating streamflow, runoff, water table fluctuations and calculating the impacts of climate change on nutrient loading among many other applications. In recent years we have also seen arguments for and advances in combining physics-based models and machine learning algorithms for mutual benefit. This thesis contributes to these advances by addressing two different groundwater problems by developing a machine learning approach and comparing this previously developed physics-based models: i) estimating groundwater and surface water depletion caused by groundwater pumping using artificial neural networks and ii) estimating a global steady-state map of water table depth using random forests. The first chapter of this thesis outlines the purpose of this thesis and how this thesis is a contribution to the overall scientific knowledge on the topic. The results of this research contribute to three of the twenty-three major unsolved problems in hydrology, as has been summarized by a collective of hundreds of hydrologists. In the second chapter, we tested the potential of artificial neural networks (ANNs), a deeplearning tool, as an alternative method for estimating source water of groundwater abstraction compared to conventional methods (analytical solutions and numerical models). Surrogate ANN models of three previously calibrated numerical groundwater models were developed using hydrologically meaningful input parameters (e.g., well-stream distance and hydraulic diffusivity) selected by predictor parameter optimization, combining hydrological expertise and statistical methodologies (ANCOVA). The output parameters were three transient sources of groundwater abstraction (shallow and deep storage release, and local surface-water depletion). We found that the optimized ANNs have a predictive skill of up to 0.84 (R2, 2σ = ± 0.03) when predicting water sources compared to physics-based numerical (MODFLOW) models. Optimal ANN skill was obtained when using between five and seven predictor parameters, with hydraulic diffusivity and mean aquifer thickness being the most important predictor parameters. Even though initial results are promising and computationally frugal, we found that the deep learning models were not yet sufficient or outperforming numerical model simulations. The third chapter used random forests in mapping steady-state water table depth on a global scale (0.1°-spatial resolution) and to integrate the results to improve our understanding on scale and perceptual modeling of global water table depth. In this study we used a spatially biased ~1.5-million-point database of water table depth observations with a variety of iv globally distributed above- and below-ground predictor variables with causal relationships to steady-state water table depth. We mapped water table depth globally as well as at regional to continental scales to interrogate performance, feature importance and hydrologic process across scales and regions with varying hydrogeological landscapes and climates. The global water table depth map has a correlation (cross validation error) of R2 = 0.72 while our highest continental correlation map (Australia) has a correlation of R2 = 0.86. The results of this study surprisingly show that above-ground variables such as surface elevation, slope, drainage density and precipitation are among the most important predictor parameters while subsurface parameters such as permeability and porosity are notably less important. This is contrary to conventional thought among hydrogeologists, who would assume that subsurface parameters are very important. Machine learning results overall underestimate water table depth similar to existing global physics-based groundwater models which also have comparable differences between existing physics-based groundwater models themselves. The feature importance derived from our random forest models was used to develop alternative perceptual models that highlight different water table depth controls between areas with low relief and high relief. Finally, we considered the representativeness of the prediction domain and the predictor database and found that 90% of the prediction domain has a dissimilarity index lower than 0.75. We conclude that we see good extrapolation potential for our random forest models to regions with unknown water table depth, except for some high elevation regions. Finally in chapter four, the most important findings of chapters two and three are considered as contributions to the unresolved questions in hydrology. Overall, this thesis has contributed to advancing hydrological sciences through: i) mapping of global steady-state water table depth using machine learning; ii) advancing hybrid modeling by using synthetic data derived from physics-based models to train an artificial neural network for estimating storage depletion; and (iii) it contributing to answering three unsolved problems in hydrology involving themes of parameter scaling across temporal and spatial scales, extracting hydrological insight from data, the use of innovative modeling techniques to estimate hydrological fluxes/states and extrapolation of models to no-data regions. / Graduate

Indiana University-Purdue University Indianapolis (IUPUI) / Recommender systems are widely used in many domains. In this work, the importance of a recommender system in an online learning platform is discussed. After explaining the concept of adding an intelligent agent to online education systems, some features of the Course Networking (CN) website are demonstrated. Finally, the relation between CN, the intelligent agent (Rumi), and the recommender system is presented. Along with the argument of three different approaches for building a community recommendation system. The result shows that the Neighboring Collaborative Filtering (NCF) outperforms both the transfer learning method and the Continuous bag-of-words approach. The NCF algorithm has a general format with two various implementations that can be used for other recommendations, such as course, skill, major, and book recommendations.

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