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Showing 2 results for Bigdeli

Z. Bigdeli, A. Golchin, T. Mansouri,
Volume 21, Issue 4 (Winter 2018)
Abstract

To assess the effects of different levels of soil lead on mineralization of organic carbon and nitrogen, a factorial pot experiment was conducted using litter bag method. The factors examined were different levels of soil lead (0, 25, 50, 100, and 200 mg kg-1soil) and incubation periods (1, 2, 3 and 4 months) in three replications. At the end of incubation periods, the litter bags were pulled out of the pots and the weights of plant residues remained in bags were measured. The plant residues were also analyzed for organic carbon and nitrogen. Organic carbon and nitrogen losses were calculated by subtracting the remaining amounts of organic carbon and nitrogen in one incubation time interval from those of former one later incubation time interval. The results showed that the losses of organic carbon from wheat residues and carbon decomposition rate constant decreased as the levels of soil lead increased over than 25 and 50 mg/kg of soil respectively. The losses of organic nitrogen was more affected by lead pollution and decreased as the levels of soil lead increased, but nitrogen decomposition rate constant decreased as the levels of soil lead increased over than 25 mg/kg. The losses of organic carbon and nitrogen in 200 mg Pb/ kg of soil were 3.2 and 11.7 % lower than control treatment. The results of this research indicate that contamination of soil by lead increases residence time of organic carbon and nitrogen in soils and slows down the cycling of these elements.

S. Bigdeli, K. Ebrahimi, A. Hoorfar, A.a. Davudirad,
Volume 26, Issue 4 (Winiter 2023)
Abstract

In this study, the accuracy of the Adaptive Network-Based Fuzzy Inference System (ANFIS) in integrating with the Gray Wolf Algorithm (ANFIS-GWO) in predicting groundwater level was evaluated for the first time using unpublished observational data from 1998 to 2018 in the Zarandieh aquifer, central Iran. Three observational wells were randomly selected for analysis. Assessment of evaluation criteria demonstrated that among the proposed scenarios using the hybrid model, the D scenario was selected as the optimal scenario with input data including the previous month's groundwater level, precipitation, temperature, and groundwater extraction. In the D scenario, parameters including MAPE, RMSE, and NASH were 0.29 m, 0.47 m, and 0.99, respectively for the first observational well. Also, C scenario with input data including the previous month's groundwater level, precipitation, and groundwater extraction for the second observational well, for the same parameters mentioned above equal to 0.20 m, 0.26 m, and 0.99. As well for the third observational well, the A scenario with input data including the previous month's groundwater level for the same parameters equal to 0.29 m, 0.41 m, and 0.99 as the optimal scenarios were selected using the ANFIS-GWO model. Based on the results, the Gray wolf algorithm in training the ANFIS model was able to reduce the average forecast error by equal to 0.03 (RMSE) and 0.02 (MAPE) meter and increased the average NASH value equal to 0.01 and increased the accuracy of predictions.


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