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

Comparison of data mining techniques to predict and map the Atterberg limits in central plateau of Iran

The Atterberg limits display soil mechanical behavior and, therefore, can be so important for topics related to soil management. The aim of the research was to investigate the spatial variability of the Atterberg limits using three most common digital soil-mapping techniques, the pool of easy-to-obtain environmental variables and 85 soil samples in central Iran. The results showed that the maximum amount of liquid limit (LL) and plastic limit (PL) were obtained in the central, eastern and southeastern parts of the study area where the soil textural classes were loam and clay loam. The minimum amount of LL and PL were related to the northwestern parts of the study area, adjacent to the mountain regions, where the samples had high levels of sand content (>80%). The ranges of plasticity index (PI) in the study area were obtained between 0.01 to 4%. According to the leave-in-out cross-validation method, it should be highlighted the combination of artifiial bee colony algorithm (ABC) and artifiial neural network (ANN) techniques were the best model to predict the Atterberg limits in the study area, compared to the support vector machine and regression tree model. For instance, ABC-ANN could predict PI with RMSE, R2 and ME of 0.23, 0.91 and -0.03, respectively. Our fiding generally indicated that the proposed method can explain the most of variations of the Atterberg limits in the study area, and it could berecommended, therefore, as an indirect approach to assess soil mechanical properties in the arid regions, where the soil survey/sampling is difficult to undertake

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.Comment: 30pages, 9 figure

Prediction of soil macronutrients using fractal parameters and artificial intelligence methods

Aim of study: To evaluate artificial neural networks (ANN), and k-Nearest Neighbor (k-NN) to support vector regression (SVR) models for estimation of available soil nitrogen (N), phosphorous (P) and available potassium (K).Area of study: Two separate agricultural sites in Semnan and Gorgan, in Semnan and Golestan provinces of Iran, respectively.Material and methods: Complete data set of soil properties was used to evaluate the models’ performance using a k-fold test data set scanning procedures. Soil property measures including clay, sand and silt content, soil organic carbon (SOC), electrical conductivity (EC), lime content as well as fractal dimension (D) were used for the prediction of soil macronutrients. A Gamma test was utilized for defining the optimum combination of the input variables.Main results: The sensitivity analysis showed that OC, EC, and clay were the most significant variables in the prediction of soil macronutrients. The SVR model was more accurate compared to the ANN and k-NN models. N values were estimated more accurately than K and P nutrients, in all the applied models.Research highlights: The accuracy of models among the test stages illustrated that using a single data set for investigation of model performance could be misleading. Therefore, the complete data set would be necessary for suitable evaluation of the model

Estimativa da composição elementar de solos do Azerbaijão oeste, Irã, utilizando-se modelos espectrais de infravermelho

[Abstract] Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2 values of 0.8, and out-of-bag mean square errors of 0.2 and 1.1, respectively. The other oxides were not predicted satisfactorily, probably due to small differences in their elemental compositions. Random Forest provided the important spectral bands related to the content of each element. For Al2O3, these bands were between 500 and 650 cm-1, which represent out-of-plane OH bending vibrations and Al-O gibbsite and alumino-silicate vibrations. For CaO, the most important bands are related to carbonate content. A combination of Fourier transform infrared spectra and Random Forest models can be used as a rapid and low-cost technique to estimate the elemental composition of arid and semi-arid soils of Northern Iran.[Resumo] A caracterização da composição elementar fornece informações úteis para caracterizar o grau de alteração dos solos. Em Miandoab, norte do Irã, esta caracterização não existe. Os objetivos deste trabalho foram avaliar o grau de intemperismo dos solos típicos da região usando a sua composição elementar e estimar esta composição usando espectroscopia infravermelha com transformada de Fourier (FTIR) e modelos Random Forest (RF). Foram selecionados cinco perfis de solo, incluindo Aridisolos e Inceptisolos, como os mais representativos da área. Os principais óxidos elementares foram determinados por fluorescência de raios-X em cada horizonte genético, mostrando que estes solos estavam em um estágio de baixo grau de desenvolvimento. Apenas o Al2O3 e o CaO foram estimados com precisão, com valores de R2 de 0,8 e erro quadrático médio nos dados utilizados para validação de 0,2 e 1,1, respectivamente, enquanto os outros óxidos não foram preditos satisfatoriamente, provavelmente devido às pequenas diferenças na sua composição. O modelo Random Forest forneceu importantes bandas espectrais relacionadas com o conteúdo de cada elemento. Para o Al2O3, estes atingiram a região 500 a 650 cm-1, o que foi atribuído a vibrações de flexão de OH e vibrações de Al-O de gibbsita e alumino-silicatos. Para o CaO, as bandas mais importantes estavam relacionadas ao teor de carbonatos. Os resultados indicam que uma combinação de espectros infravermelha de transformada de Fourier e modelos Random Forest pode ser usada como uma técnica rápida e de baixo custo para estimar a composição elementar de solos do norte do Irã