The planning and management of water resources are affected by streamflow. The analysis of the sustainability of water resources has used well-grounded methods such as artificial neural networks, used for streamflow forecasting by researchers in recent years. The main aim of this study is to evaluate the performance of various methods for long-term forecasting from the data of the mean monthly streamflows between 1981 and 2017 from the Kucukmuhsine station on the Meram Stream in the Turkish province of Konya. For that reason, the multilayer perceptron (MLP), long short-term memory (LSTM), and adaptive neuro-fuzzy inference system (ANFIS) artificial intelligence techniques were employed in this study for the long-term forecasting of streamflow for 12 months, 24 months, and 36 months. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) were used to evaluate the performance of the models developed to make predictions using the data from 1981 to 2017, and the Mann-Whitney test was applied to examine the differences between the actual data from 2018 to 2020 and each model’s forecasted results for those three years. The LSTM model showed superiority based on the values of R² (calculated as 0.730) and RMSE (lowest value of 0.510), whereas the MLP yielded better prediction accuracy as reflected by the value of MAE (lowest value of 0.519). The ANFIS model did not have the best prediction ability for any of the criteria. In accordance with the Mann-Whitney test results, LSTM and MLP indicated no significant difference between the actual data from 2018 to 2020 and the forecasted values; whereas, there was a significant difference for the ANFIS model at a confidence level of 95%. The results showed that the LSTM model had a better prediction performance, surpassing the MLP and ANFIS models, when comparing mean monthly streamflow forecasts.

The effect of number of cells deposited on decrease in hydraulic conductivity of porous media using CaCO₃ precipitation induced by Sporosarcina pasteurii (ATCC 11,859) was examined in columns packed with glass beads in the range of 0.25 mm and 3 mm in diameter. After resting Sporosarcina pasteurii cells were introduced into the columns, a precipitation solution, which consisted of 500 mM CaCl₂ and 500 mM urea, was introduced under continuous flow conditions. It was shown that hydraulic conductivity was decreased by formation of microbially induced CaCO₃ precipitation from between 8.37 * 10⁻¹ and 6.73 * 10⁻² cm/s to between 3.69 * 10⁻¹ and 1.01 * 10⁻² cm/s. The lowest hydraulic conductivity was achieved in porous medium consisting of the smallest glass beads (0.25 mm in diameter) using the highest density of cell suspension (OD₆₀₀ 2.25). The number of the deposited cells differed depending on the glass bead size of the columns. According to the experiments, 7 * 10⁻⁹ g CaCO₃ was produced by a single resting cell. The urease activity, which led CaCO₃ precipitation, depended on presence of high number of cells deposited in the column because the nutrients were not included in the precipitation solution and consequently, the amount of CaCO₃ precipitated was proportional with the cell number in the column. A mathematical model was also developed to investigate the experimental results, and statistical analysis was also performed.