A Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg-Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir.

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The structure and diversity of epipelic diatoms and some physico-chemical features of the Akarc¸ay streams were studied in samples collected monthly from four sampling sites between March and December 2008. A total of 107 taxa of epipelic diatoms were identified. While Amphora veneta, A. pediculus, Cocconeis placentula, Cymatopleura solea, Cyclotella meneghiniana, Encyonema minutum, Nitzschia tubicola, Ulnaria ulna, and Sellaphora pupula were dominant upstream, N. palea was dominant with over 65% of total abundance downstream. According to canonical correspondence analysis analysis, BOD 5 , COD, TDS, pH, nutrients, and conductivity had the most significant effects on diatom community structure. N. palea was strongly and positively correlated with BOD 5 , COD, TDS, and nutrients. A. pediculus, Navicula cryptocephala, and S. pupula were positively correlated with pH , EC, TDS, and NO 3-N. We show that the species diversity values gradually decreased with increasing pollution downstream.

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Seasonal changes in phytoplankton community structure of the lake Tortum were studied over one year period, from March 2002 to February 2003. The collected data were compared with the data collected 21 years ago. Chlamydomonas microsphaerella, Cyclotella krammeri, C. glomerata, and Ceratium hirundinella were identified to be dominant several times during the study period. Species diversity and biomass of the phytoplankton were very low in spite of sufficient and high levels of nutrient concentrations. Maximum phytoplankton density levels were observed during summer and late autumn. Phytoplankton density was positively correlated with nutrients, temperature and pH, and it was negatively correlated with Secchi depth and dissolved oxygen. Phytoplankton growths were negatively affected from water transparency and high levels of water mass transport (circulation) and velocity in the lake.