The Relationship between the Slope Hill and Bedrock with Some Soil Properties

Introduction Changes in soil properties depend on factors such as climate, topography, landscape features, altitude, parent material, and vegetation. The quantity and quality of soils obtained from different rocks (igneous, sedimentary and metamorphic rocks) depend on the minerals that make up the rock, as well as weather and other factors. Soil parent material is one of the primary and important issues in soil classification in terms of physical quality and also one of the most important effective factors in soil erodibility. The topographical factor of each region is one of the important and influential features on the soil quality of that region. The present research was conducted with the aim of understanding the spatial changes of soil properties in different slopes and different types of rocks. Material and Methods The studied area is located in Razavi Khorasan province in the cities of Mashhad, Chenaran, Sarakhs and Torbat-Haidarieh. The geographic location of the region ranges from 58 degrees and 52 minutes to 60 degrees and 40 minutes east longitude and 35 degrees and 38 minutes to 36 degrees and 25 minutes north latitude. This research was carried out on seven types of rocks: granite, Sarakhs paleogene limestone, Chenaran jurassic limestone, marl, shale, sandstone and ophiolite from relatively pure rocks of Razavi Khorasan province. In the present study, two factors of rock type and slope were investigated as effective factors of soil properties. Soil samples were taken from the surface layer (0-20 cm) and from three slope classes ie., less than 10%, 10-25% and more than 25%, as well as all soil samples from the southern slopes. Tree soil samples were taken from each slope and a total of 63 samples were taken and the samples were transfered to the laboratory for physical and chemical tests. In this study, the soil particle size distribution (texture) was measured by hydrometer method, organic carbon and calcium carbonate were determined by wet oxidation and titration with HCl 6 M, the mean weight diameter of soil aggregates and surface crust factor were calculated by related equations. To measure soil cohesion and penetration resistance were used pocket vane test and pocket penetrometer, respectively. Comparison of means was done through Duncan test in spss software. Results and Discussion The results showed that all the studied variables in different types of stones had a significant difference at the level of 1%. There was no significant difference in the variable of surface level in different slopes. Also, the variables of calcium carbonate percentage and saturated conductivity at 5% level had significant differences in different slopes. Other characteristics of soil, including percentage of organic matter, the mean weight diameter of soil aggregates, the number of drops impact, and soil cohesion and penetration resistance in different slopes had a significant difference at the level of 1%. Althoug the soil texture class was not significantly different in different slopes, the percentage changes of clay, silt and sand had a lot of difference along the slope. The highest and lowest parameters of organic matter percentage, Soil cohesion and penetration resistance were observed in granite and shale, respectively. The highest percentage of calcium carbonate was observed in Chenaran limestone (40.41%) and the lowest in granite (14.72 %). The mean weight diameter of soil aggregates was the highest in ophiolite (1.005 mm) and the lowest in marl (0.403 mm). The mean weight diameter of soil aggregates in the medium slope was significantly higher than the other two slopes. The parameter of the number of drops impact was the highest in granite (47.14 number) and the lowest in marl (27.70 number). The highest value of saturated conductivity variable was observed in marl rock and the lowest value was observed in Chenaran limestone. Conclusion The results showed that all the investigated variables had significant differences in different types of stones. Also, some of the investigated variables such as percentage of organic matter, percentage of equivalent calcium carbonate and the mean weight diameter of soil aggregates had significant changes along the hillside. As a general conclusion, given that the physical and chemical properties of the soil are partly under the influence of the parent material and the slope, and also with the presence of good geological information in the country, it can be suggested to provide suitable management solutions to prevent soil erosion and degradation by comprehensive examination of soil properties under different slope and types of stones

Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS

© 2018, Springer International Publishing AG, part of Springer Nature. Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation

GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran

This study intends to investigate the performance of boosted regression tree (BRT) and frequency ratio (FR) models in groundwater potential mapping. For this purpose, location of the springs was determined in the western parts of the Mashhad Plain using national reports and field surveys. In addition, thirteen groundwater conditioning factors were prepared and mapped for the modelling process. Those factor maps are: slope degree, slope aspect, altitude, plan curvature, profile curvature, slope length, topographic wetness index, distance from faults, distance from rivers, river density, fault density, land use, and lithology. Then, frequency ratio and boosted regression tree models were applied and groundwater potential maps (GPMs) were produced. In the last step, validation of the models was carried out implementing receiver operating characteristics (ROC) curve. According to the results, BRT had area under curve of ROC (AUC-ROC) of 87.2%, while it was seen that FR had AUC-ROC of 83.2% that implies acceptable operation of the models. According to the results of this study, topographic wetness index was the most important factor, followed by altitude, and distance from rivers. On the other hand, aspect, and plan curvature were seen to be the least important factors. The methodology implemented in this study could be used for other basins with similar conditions to cope with water resources problem

Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer

none10siTrace element (TE) pollution in groundwater resources is one of the major concerns in both developing and developed countries as it can directly affect human health. Arsenic (As), Barium (Ba), and Rubidium (Rb) can be considered as TEs naturally present in groundwater due to water-rock interactions in Campania Plain (CP) aquifers, in South Italy. Their concentration could be predicted via some readily available input variables using an algorithm like the iterative classifier optimizer (ICO) for regression, and novel hybrid algorithms with additive regression (AR-ICO), attribute selected classifier (ASC-ICO) and bagging (BA-ICO). In this regard, 244 groundwater samples were collected from water wells within the CP and analyzed with respect to the electrical conductivity, pH, major ions and selected TEs. To develop the models, the available dataset was divided randomly into two subsets for model training (70% of the dataset) and evaluation (30% of the dataset), respectively. Based on the correlation coefficient (r), different input variables combinations were constructed to find the most effective one. Each model's performance was evaluated using common statistical and visual metrics. Results indicated that the prediction of As and Ba concentrations strongly depends on HCO3−, while Na+ is the most effective variable on Rb prediction. Also, the findings showed that the most powerful predictive models were those that used all the available input variables. According to models' performance evaluation metrics, the hybrid ASC-ICO outperformed other hybrid (BA- and AR-ICO) and standalone (ICO) algorithms to predict As and Ba concentrations, while both hybrid ASC- and BA-ICO models had higher accuracy and lower error than other algorithms for Rb prediction.restrictedKhosravi K.; Barzegar R.; Golkarian A.; Busico G.; Cuoco E.; Mastrocicco M.; Colombani N.; Tedesco D.; Ntona M.M.; Kazakis N.Khosravi, K.; Barzegar, R.; Golkarian, A.; Busico, G.; Cuoco, E.; Mastrocicco, M.; Colombani, N.; Tedesco, D.; Ntona, M. M.; Kazakis, N