Showing 3 results for Hamadan Province
M. Arabi, A. Soffianian , M. Tarkesh Esfahani,
Volume 17, Issue 63 (6-2013)
Abstract
Physicochemical characteristics of soil, land cover/use and human activities have effects on heavy metals distribution. In this study, we applied Classification and Regression Tree model (CART) to predict the spatial distribution of zinc in surface soil of Hamadan province under Geographic Information System environment. Two approaches were used to build the model. In the first approach, 10% of total data were randomly selected as test data and residual data were used for building model. In the second approach, all data were used to build and evaluate the CART model. Determination coefficient (R2) and Mean Square Error (MSE) were applied to estimate the accuracy of model. Final model included 51 nodes and 26 terminal nodes (leaf). Calcium carbonate, slope, sand, silt and land use/cover were determined by the CART model to predict spatial distribution of Zn as the most important independent variables. The regions of western Hamadan province had the highest concentration of Zn whereas the lowest concentration of Zn occurred in the regions of northern Hamadan province. The results indicate good accuracy of CART model using R2 and MSE indices.
M. Barzin, H. Kheirabadi, M. Afyuni,
Volume 19, Issue 72 (8-2015)
Abstract
Soil pollution and accumulation of heavy metals in soils and crops are the most important bioenvironmental problems that threaten the life of plants, animals and humans. This study was conducted to explore contamination of heavy metals in soils of Hamadan province. A total of 286 composite surface soil samples (0-20 cm) were collected thoroughout the province. After preparation of the samples, the total contents of Zn, Pb, Cu, and Ni in soil samples were extracted using HNO3. Total contents of heavy metals were measured by ICP. Contamination factor results showed that most samples were moderately polluted and contamination factor for lead was highly polluted. Interpolated distribution map of contamination factors (CF) and pollution load index (PLI) of the heavy metals were prepared using GIS. The overlap of CF and PLI maps with geology and land use maps indicated that the concentrations of Ni, Pb, Zn, and Cu have been controlled by natural factors such as parent material, but agricultural activities according to excessive consumption of animal manure and chemical fertilizers can increase most of these elements in soil.
S. Moghim, A. A. Samavaki,
Volume 29, Issue 4 (12-2025)
Abstract
The effect of climate change on agricultural productivity and efficiency is a major concern and challenge for the agricultural industry. Different hydrometeorological variables, such as extreme temperature, precipitation, and their variations, affect the growth and yield of agricultural products. Saffron is one of the most important agricultural products in Iran. Iran produces the largest amount of Saffron globally, and Hamadan Province is one of the major saffron-producing regions in Iran. This study uses different Artificial Intelligence methods not only for clustering and sensitivity analysis of the hydroclimatological variables but also for evaluating the impacts of climate change on Saffron yield in Hamadan Province. Results indicated that the Random Forest algorithm performs the best for sensitivity analysis among all algorithms. Extreme climate change indices, particularly those related to the monthly maximum and minimum temperatures, have the highest negative impact on saffron yield compared to other hydroclimatological indices. Furthermore, the minimum temperature has a more significant negative impact on saffron yield compared to the maximum temperature. Additionally, the counties of Malayer, Nahavand, and Asadabad, located in the south and west of Hamadan Province, exhibited the highest accuracy in sensitivity analysis. The findings suggest that monthly extreme temperatures can be used to assess the risk of saffron production, increase agricultural productivity, and improve decision-making for the cultivation of this product.
