Mapping of noise pollution by different interpolation methods in recovery section of Ghandi telecommunication Cables Company

Background: Noise pollution and workers\u27 noise exposure are common in industrial factories in Iran. In order to reduce this noise pollution, evaluation and investigation of noise emission are both necessary. In this study, different noise mapping methodsare used for determining the distribution of noise. Materials and Methods: In the present study, for preparing a noise map in a hall of an industrial factory, sampling was carried out in 6×6 m grid. After data normalization the variogram was developed. For interpolation of mentioned parameter, kriging and Inverse Distance Weighting methods were used. The best model for interpolation was selected by cross validation and error evaluation methods, such as Route Mean Square Error(RMSE). Results: The results showed that kriging method is better than other methods for prediction of noise property. The noise map was prepared, using the best interpolation method in Geographical Information System environment. Conclusion: Workers in this industrial hall were exposed to noise which is mainly induced by noisy machines. Noise maps which were produced in this study showed the distribution of noise and, also revealed that workers suffer from serious noise pollution

Development and analysis of the Soil Water Infiltration Global database.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type (~40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

Application of Minimal Data Sets for Quantitative Assessment and Investigation of Spatial Autocorrelation of Soil Quality in Southwestern Lands of Urmia Plain

Soil quality is one of the most crucial factors determining crop productivity and production stability. The soil's physical, chemical, biological, and ecological characteristics affect its quality. Numerous researchers have concentrated the evaluation on a small number of soil quality indicators because measuring all soil quality indicators would be time-consuming and expensive. This study looked at the spatial autocorrelation of soil quality in the southwest areas of the Urmia Plain to establish the minimal data set for quantitative assessment. To accomplish this, 120 composite soil samples were collected from a depth of 0 to 60 cm, and the soil quality index was then calculated using the IQI method in 4 modes: Total-Linear (IQIwL-TDS), Total-Nonlinear (IQIwNL-TDS), Minimum-Linear (IQIwL-MDS), and Minimum nonlinearity (IQIwNL-MDS). 22 physical and chemical characteristics were used to choose the data set. The characteristics of sand percentage, sodium absorption ratio, cation exchange capacity, Available phosphorus, active calcium carbonate, and nickel concentration were chosen as the minimum data set (MDS) using the decomposition method into principal components. The linear IQIMDS mode produced the greatest soil quality index result, whereas the non-linear IQIMDS mode produced the lowest. The non-linear mode of the IQI index has a greater correlation coefficient (R2=0.85) than the linear mode of the IQI index (R2=0.73), according to an analysis of the linear and non-linear correlation coefficient between the soil quality index with the total category and minimum data. The findings of computing the global Moran's index for study sets of IQI soil quality index data revealed that the soil quality data are not independent of each other and are spatially autocorrelated, distributed in clusters, and have spatial autocorrelation. Getis-ord GI statistics indicated that the eastern and southeastern parts of the research region comprise clusters with poor soil quality, salt marshes produced by Lake Urmia's drying up, and surrounding arid plains

Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran

Digital soil mapping involves the use of auxiliary data to assist in the mapping of soil classes. In this research, we investigate the predictive power of 6 data mining classifiers, namely Logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), and decision tree model (DTM) to create a DSM across an area covering of 3000. ha in Kurdistan Province, north-west Iran. In this area, using the conditioned Latin hypercube sampling method, 217 soil profiles were selected, sampled, analysed and allocated to taxonomic classes according to Soil Taxonomy up to family level. To test the user accuracy (UA) we established a calibration and validation set (70:30%). Of the 5 soil family classes we map, the highest overall accuracy (0.71) and kappa index (0.69) are achieved using the DTA and ANN method. More specifically, the UA of prediction was up to 18.33% better in comparison to LR. Moreover, our results showed that no improvement was obtained in prediction accuracy of DTA algorithm with minimizing taxonomic distance compared to minimizing misclassification error (0.71). Overall, our results suggest that the developed methodology could be used to predict soil classes in the other regions of Iran

Assessing soil organic carbon stocks under land-use change scenarios using random forest models

Identifying changes in soil organic carbon stocks (SOCS) is essential for determining appropriate ways to deal with land degradation, for understanding soil and crop management and for gathering useful information for a range of environmental studies. The aim of this study was to predict SOCS and compare under current and potential future land uses. Soil organic carbon (SOC) and bulk density were measured at 137 locations across the Marivan, Kurdistan Province, Iran, and soil SOCS was computed. Auxiliary data including, terrain attributes and Landsat 8 ETM+ data were acquired. Random forest (RF) models were used to relate the SOCS to the auxiliary data. Results suggested that the mean SOCS in the topsoil and subsurface in croplands were lower than in forestland and wetland, although not significantly so. In the area, approximately 18.48% of forestland and 17.39% of wetland has been brought into cultivation. The authors estimate that this has led to a loss of SOCS from forestland topsoil of 22,860 Mg C, and from subsurface of 15,685 Mg C. The SOCS loss from wetland topsoil and subsurface were not as great, at 4193 and 2680 Mg C, respectively, but this was due to the area not being as large

The spatial prediction of soil texture fractions in arid regions of Iran

To predict the soil texture fractions, 115 profiles were identified based on the Latin hypercube sampling technique, the horizons were sampled, and the clay, sand, and silt contents (in percentages) of soil samples were measured. Then equal-area quadratic spline depth functions were used to derive clay, sand, and silt contents at five standard soil depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm). Auxiliary variables used in this study include the terrain attributes (derived from a digital elevation model), Landsat 8 image data (acquired in 2015), geomorphological map, and spectrometric data (laboratory data). Artificial neural network (ANN), regression tree (RT), and neuro-fuzzy (ANFIS) models were used to make a correlation between soil data (clay, sand, and silt) and auxiliary variables. The results of this study showed that the ANFIS model was more accurate in the prediction of the three parameters of clay, silt, and sand than ANN and RT. Moreover, the ability of ANFIS model to estimate the soil texture fractions in the surface layers was higher than the lower layers. The mean coefficient of determination (R2) values calculated by 10-fold cross validation suggested the higher prediction performance in the upper depth intervals and higher prediction error in the lower depth intervals (e.g., R2 = 0.91, concordance correlation coefficient (CCC) = 0.90, RMSE = 4.00 g kg−1 for sand of 0–5 cm depth, and R2 = 0.68, CCC = 0.60, RMSE = 8.03 g kg−1 for 60–100 cm depth). The results also showed that the most important auxiliary variables are spectrometric data, multi-resolution, valley-bottom flatness index and wetness index. Overall, it is recommended to use ANFIS models for the digital mapping of soil texture fractions in other arid regions of Iran