Exploring field scale salinity using simulation modeling, example for Rudasht area, Esfahan Province, Iran

Salinity / Simulation models / Soil-water-plant relationships / Soil properties / Climate / Irrigated farming / Water quality / Iran / Esfahan Province / Rudasht Area

Assessment of groundwater vulnerability and sensitivity to pollution in Aquifers Zanjan Plain, Iran

Groundwater pollution caused by human activity is a serious environmental problem in cities. Pollution vulnerability assessment of groundwater resources provides information on how to protect areas vulnerable to pollution. The present study is a detailed investigation of the potential for groundwater contamination through construction of a vulnerability map for the study aquifer in Zanjan plain. The parameters used in the DRASTIC model are depth-to-water table, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity. The overlying index, GIS and AHP were used with the modified DRASTIC model to evaluate the vulnerability of the alluvial Zanjan aquifer to nitrates. AHP was used to determine the rate coefficient of each parameter. The correlation coefficients were produced by comparing the vulnerability index with the nitrate concentrations in the groundwater. The results show that the DRASTIC index values for the study area ranged from 82 to 186 and were divided into low, medium, and high vulnerability classes. GIS was found to provide an efficient environment for such analyses. The DRASTIC aquifer vulnerability map indicates the dominance of the medium vulnerability class in the most parts of the study area (49.033%). The high correlation coefficient for the modified DRASTIC index (0.92) and nitrate layer than for the standard DRASTIC model (0.74) suggests that the actual condition in the study area can be better explained by the modified DRASTIC model.Keywords: Groundwater vulnerability, GIS, DRASTIC Model, AHP, Zanja

Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter

A large class of modeling and prediction problems involves outcomes that belong to an exponential family distribution. Generalized linear models (GLMs) are a standard way of dealing with such situations. Even in high-dimensional feature spaces GLMs can be extended to deal with such situations. Penalized inference approaches, such as the (Formula presented.) or SCAD, or extensions of least angle regression, such as dgLARS, have been proposed to deal with GLMs with high-dimensional feature spaces. Although the theory underlying these methods is in principle generic, the implementation has remained restricted to dispersion-free models, such as the Poisson and logistic regression models. The aim of this manuscript is to extend the differential geometric least angle regression method for high-dimensional GLMs to arbitrary exponential dispersion family distributions with arbitrary link functions. This entails, first, extending the predictor\ue2\u80\u93corrector (PC) algorithm to arbitrary distributions and link functions, and second, proposing an efficient estimator of the dispersion parameter. Furthermore, improvements to the computational algorithm lead to an important speed-up of the PC algorithm. Simulations provide supportive evidence concerning the proposed efficient algorithms for estimating coefficients and dispersion parameter. The resulting method has been implemented in our R package (which will be merged with the original dglars package) and is shown to be an effective method for inference for arbitrary classes of GLMs

Evaluation of some heavy metals contaminated soils around the Shahid Salimi power plant, Neka, Mazandaran Province, Iran

One of the most important problems threatening the health of natural resources and, in turn, the food safety of societies is environmental contamination. Heavy metals are considered as the environmental pollutants. The entry of heavy metals into the soil is done through the atmospheric sources and mostly via melting plants, oil refieries and power plants. Due to the mazut consumption in some seasons, power plants are considered as a threat to the soil. This study was conducted with the aim of evaluating contamination of some heavy metals including copper, zinc, cadmium, lead, and nickel in the soils around the Shahid Salimi power plant, Neka located in Mazandaran province, north of Iran. One of the greatest threats is the possible contamination of cultivated paddy by pollutant elements. A number of 50 samples from the soil around the power plant were taken from a depth of 0–20 cm within the form of a regular grid and the concentration of the corresponding metals was measured in each of them. The mean background concentration of copper, nickel, lead, zinc, and cadmium was 36.2, 339.8, 90.8, 13.8, and 0.20 mg∙kg, respectively. The maximum mean contamination factor belongs to nickel, lead, copper, zinc, and cadmium, respectively. The frequency of the obtained contamination evaluation classes indicates that the majority of the analyzed samples have a medium level of contamination. Copper, nickel, and lead belong to the class of very high contaminants. By comparing the concentrations of the heavy metals of studied region with quality standard of Iranian soil resources, presented by the Department of Environment Protection of Iran, it was observed that the concentrations of cadmium, zinc, and copper have been signifiant at the level of 5% based on the standards determined by the agency for agricultural uses, environmental standard and groundwater level. In other words, they do not have conflct with the determined standard at any of the three levels

Interpolation of soil infiltration in furrow irrigation: Comparison of kriging, inverse distance weighting, multilayer perceptron and principal component analysis methods

Study on soil infiltration rate as part of water cycle is essential for managing water resources and designing irrigation systems. The present study was conducted with the aim to compare Kriging, inverse distance weighting (IDW), multilayer perceptron (MLP) and principal component analysis (PCA) methods in the interpolation of soil infiltration in furrow irrigation, and determine the best interpolation method. To conduct infiltration tests, furrows were made on the farm in four triad groups. Infiltration through the blocked furrows method was measured 10, 20, 30, 40, 50, 60, 90, 120, 150, 160, 180 and 210 min after irrigation at a 10-meter distance in each furrow. Data were analyzed by GS+ and Neuro Solutions (NS) software packages. In this study, the maximum error (ME), mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative error (RE) and correlation coefficient (r) were used to compare the interpolation methods. The results of analysis of variance (ANOVA) indicated that differences in methods based on RMSE, MBE, MAE and ME indices were not significant; however, this difference was significant based on r and RE indices. According to the ANOVA results, it can be said that the PCA method with a r of 0.69 and RE of 31%, was predicted with a higher accuracy as compared to other methods

Exploring field scale salinity using simulation modeling, example for Rudasht area, Esfahan Province, Iran

The objectives of this paper are to demonstrate the possibility of making combined use of data and a simulation model for a rapid assessment of salinity problems. This approach was tested by analyzing the water and salt balance and yields in relation to the quantity and quality of water applied for irrigation

Sparse relative risk survival modelling

Cancer survival is thought to closed linked to the genimic constitution of the tumour. Discovering such signatures will be useful in the diagnosis of the patient and may be used for treatment decisions and perhaps even the development of new treatments. However, genomic data are typically noisy and high-dimensional, often outstripping the number included in the study. Regularized survival models have been proposed to deal with such scenary. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and (near) non-convex regularizer