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Two different methodologies for hydraulic head simulation were compared in this study. The first methodology is a classic numerical groundwater flow simulation model, Princeton Transport Code (PTC), while the second one is a black-box approach that uses Artificial Neural Networks (ANNs). Both methodologies were implemented in the Bavaria region in Germany at thirty observation wells. When using PTC, meteorological and geological data are used in order to compute the simulated hydraulic head following the calibration of the appropriate model parameters. The ANNs use meteorological and hydrological data as input parameters. Different input parameters and ANN architectures were tested and the ANN with the best performance was compared with the PTC model simulation results. One ANN was trained for every observation well and the hydraulic head change was simulated on a daily time step. The performance of the two models was then compared based on the real field data from the study area. The cases in which one model outperforms the other were summarized, while the use of one instead of the other depends on the application and further use of the model. 

Natural organic matter (NOM) has been identified as the prominent precursor for disinfection by-products (DBPs) formation during chlorination. Various studies have shown that the characteristics of NOM influence the Trihalomethanes (THMs) formation mechanism. The present study represents NOM categorization in terms of total organic carbon (TOC), dissolved organic carbon (DOC), UV absorbance at 254 nm wavelengths (UV254) and specific ultraviolet absorbance (SUVA) to investigate the effects of NOM on THMs formation mechanism. The high rate of dependency was found for each representative of NOM with respect to water quality characteristics and operational condition of disinfection process. Values of SUVA and UV254 is drastically reduced with respect from higher to a moderate chlorine dose which represent the chlorine contact is more predominant with hydrophobic fractions of NOM. The value of SUVA is decreasing with respect to temperature and reaction time, which reveled higher rate of utilization for hydrophobic fractions of NOM. Predictive modeling approach was carried out using multiple regression analysis with the combination of two surrogates at each stage of modeling with help of operational condition of disinfection process and water quality characteristics. The R2 value of the model was found in the range of 0.927 to 0.937 from the developed model and a model could be recommended for prediction of THMs in drinking water.

Overland flow is highly affected by increasing urbanization, and variations in land use and climatic variables, especially in the last few decades. This necessitates the development of modeling approaches for planning and management of catchments that play a significant role on water supply. The main objective of this study is to determine the effects of major hydrological and hydraulic parameters on runoff production in the Alibeyköy Reservoir catchment area in Istanbul. Storm Water Management Model (SWMM) is chosen to develop the catchment hydrological model and the model’s sensitivity is assessed based on the variations in eight major parameters of the model affecting runoff production. 55 years of time series precipitation data are used for model simulations. GIS-based maps including land use and land cover information are used to determine the imperviousness values required for SWMM. A one-at-a-time parametric sensitivity analysis is carried out to determine the most significant parameters affecting the model outcomes. Analysis results reveal that area of subcatchments, precipitation and conduit depth are the most significant parameters in SWMM affecting runoff production. Percent imperviousness and percent slope are the least significant parameters amongst other parameters influencing the output.