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Background. In this research, the potential of the microbially induced carbonate precipitation method for the surface treatment of sand samples of Jabal Kandi dunes, located in the adjacency of Urmia Lake in the northwest of Iran, was considered. Methods. Sporosarcina pasteurii was used as ureolytic bacteria for the preparation of the microbial solution. Corn steep liquor containing urea was used as an inexpensive growth media. The effects of the concentration of cementation solution and the number of treatment cycles were considered. Because of the presence of some hydrolyzed urea in the prepared microbial suspension, two methods of treatment, i.e., the mixed and separate addition of microbial solution and cementation solution to the sand surface, were investigated. Penetration and erosion resistance of the microbial treated sand (sand crust) were measured using a handheld penetrometer and a wind tunnel system. Results. The results showed that the penetration and erosion resistance of the treated sand samples via microbial-induced carbonate precipitation method were improved significantly. In the method with the separate addition of microbial and cementation solutions to the sand surface, a stable sand crust was created on top of the sand. Discussion. This study tried to optimize the microbial application of Sporosarcina pasteurii for surface treatment of sand via microbial-induced carbonate precipitation. Significant and results showed that this method can be used on the field scale for the stabilization of sand dunes. The advanced biotechnology application of this bacterium can be used as an environmentally friendly and safe method instead of other methods.

Considering the three intrinsic components (of autoregressive, seasonality, and error) of streamflow time series, the overall performance of the streamflow modeling tool is associated with the correct estimation of these components. In this study, a new hybrid method based on the wavelet transform (WT) as a multiresolution forecasting tool and exponential smoothing (ES) method, with two presented scenarios (WES1 and WES2), was introduced. To this end, the performance of the proposed method was investigated versus four conventional methods of the autoregressive integrated moving average (ARIMA), ES ad-hoc, artificial neural network (ANN), and wavelet-ANN (WANN) for daily and monthly streamflow modeling of West Nishnabotna and Trinity River watersheds with different hydro-geomorphological conditions. In the presented WES technique, firstly, WT is employed for decomposing the observed signal to one approximation (deterministic trend) and more diverse components of subseries (each at a specific frequency). Then, for the first scenario (WES1), only two subseries are introduced to the model as input parameters; however, for the second scenario (WES2), decomposed subseries are separately used as the inputs of ES models. The obtained results indicated that combining WT with the ES method and ANN led to more accurate modeling. The proposed methodology (WES2) that used all decomposed subseries separately improved the efficiency of models up to 30% and 10% for the daily dataset and up to 88% and 57% for the monthly dataset, respectively, for the West Nishnabotna and Trinity Rivers.

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Accurate prediction of breached dam's peak outflow is a significance factor for flood risk analysis. In this study, capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and Gene Expression Programming method was assessed in breached dam's peak outflow predicting. Two types of modeling were considered. First, only dam reservoir height and volume at the failure time were used as the input combinations (state 1). Then, soil characteristics were added to input combinations to investigate particularly the impact of soil characteristics (state 2). Results showed that the use of only soil characteristics did not lead to a desired accuracy; however, adding soil characteristics to input combinations (state 2) improved the models accuracy up to 40%. The outcome of the applied models also was compared with existing empirical equations and it was found the applied models yielded better results. Sensitivity analysis results showed that dam height had the most important role in the peak outflow prediction, while the strength parameters did not had significant impacts. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was used and the results indicated that the SVM model had an allowable degree of uncertainty in peak outflow modelling.