Showing 52 results for Red
H. Nazaripour, Z. Karimi, M. Sedaghat,
Volume 20, Issue 75 (5-2016)
Abstract
Drought is a climatic anomaly that associates with a significant decrease (lack) of precipitation and water resources availability, which spreads on vast temporal and spatial scales, and significantly affects various aspects of life and environment. One of the most common methods of drought assessing and monitoring is calculating drought indices (DIs). Drought areal and temporal extent and its severity are determined by these indices. In this study, an aggregate drought index (Hydro-Meteorological) has been developed for the assessment of hydrological and meteorological droughts in Sarbaz river basin located in southeastern of Iran. The Aggregate Drought Index (ADI) comprehensively considers all physical forms of drought (meteorological, hydrological, and agricultural) through selection of variables that are related to each drought type. In this case, monthly values of Stream flow Drought Index (SDI) and Standardized Precipitation Index (SPI) indicators were used for four similar reference periods with principle component analysis and aggregate hydro-meteorological index was defined based on its first component. The study time span was set between 1981-82 to 2010-11, which begins of October in Iran. Results based on the aggregate drought index (ADI) revealed that a long period of hydro-meteorological drought occurred from 1999-2000 to 2005/06 in southeast of Iran, in which, 2003/04 water year has been extremely a drought year. The ADI methodology provides a clear, objective approach for describing the intensity of drought. This index is appropriately able to represent the behavior of Hydro-Meteorological droughts and recommended as an integrated index for assessing and monitoring of regional droughts. Finally, different states of hydro-meteorological drought have been extracted based on conventional regional thresholds, and have been modeled by Markov chain. This made the estimation of drought state transition frequency possible, and made the prediction of next drought state time more real. State transition frequency matrices, are the main instruments for predicting drought states in real time. Results of validation tests and conforming the predicted results with real data indicate that predicting hydrological drought state transitions in the study area using Markov chain method is valid.
M. Sadeghian, H. Karami, S. F. Mousavi,
Volume 21, Issue 4 (2-2018)
Abstract
Nowadays, greater recognition of drought and introducing its monitoring systems, particularly for the short-term periods, and adding predictability to these systems, could lead to presentation of more effective strategies for the management of water resources allocation. In this research, it is tried to present appropriate models to predict drought in city of Semnan, Iran, using time series, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (MLP and RBF). For these modeling processes, average monthly meteorological parameters of rainfall, temperature, minimum temperature, maximum temperature, relative humidity, minimum relative humidity, maximum relative humidity and SPI drought index were used during the period 1966 to 2013. The results showed that among the many developed models, the ANFIS model, with input data of average rainfall, maximum temperature, SPI and its last-month value, 10 rules and Gaussian membership function, showed appropriate performance at each stage of training and testing. The values of RMSE, MAE and R at training stage were 0.777, 0.593 and 0.4, respectively, and at testing stage were 0.837, 0.644 and 0.362, respectively. Then, the input parameters of this model were predicted for the next 12 months using ARIMA model, and SPI values were predicted for the next 12 months. The ANN and time series methods with low difference in error values were ranked next, respectively. The input parameters SPI and temperature had better performance and rainfall parameter had weaker performance.
R. Samiei Fard, H. Matinfar,
Volume 21, Issue 4 (2-2018)
Abstract
Reflectance spectroscopy is a fast and safe method to predict soil physicochemical and biological properties in low cost ways. Traditional methods to determine soil properties require spending a lot of time and money so that farmers are generally reluctant to use the results of laboratory measurements in soil and water management. Reflectance spectroscopy in the spectral range of 400-2500 nm (VNIR) is an alternative method for estimating the soil properties. The aim of this study was to evaluate the results of laboratory spectrometer to estimate the concentration of Lead (Pb) and Nickel (Ni) in soils irrigated with water from treatment of urban sewage sludge of Rey city and finally to compare these results with the results of measurements of atomic absorption spectrometry. In this study, the Partial Linear Square Regression (PLSR) model was used to estimate the concentration of heavy metals and Residual Mean Square Error (RMSE) was used to evaluate the performance of this model. In this research, after spectral corrections related to elimination of the water absorption bands as well as elimination of the inefficient spectrum from heavy metals estimations, the methods of estimating these elements were studied through mathematical derivation of spectral values and also the acquisition of the continuum removal spectra. The results show that the estimated values from first derivate spectra are more consistent with the results of atomic absorption spectrometers.
S. Zahedi, K. Shahedi, M. Habibnejhad Roshan, K. Solaimani, K. Dadkhah,
Volume 21, Issue 4 (2-2018)
Abstract
Soil depth is a major soil characteristic commonly used in distributed hydrological modeling in order to present watershed subsurface attributes. It strongly affects water infiltration and accordingly runoff generation, subsurface moisture storage, vertical and lateral moisture movement, saturation thickness and plant root depth in the soil. The objective of this study is to develop a statistical model that predicts the spatial pattern of soil depth over the watershed from topographic and land cover variables derived from DEM and satellite image, respectively. A 10 m resolution DEM was prepared using 1:25000 topographic maps. Landsat8 imagery, OLI sensor (May 06, 2015) was used to derive different land cover attributes. Soil depth, topographic curvature, land use and vegetation characteristics were surveyed at 426 profiles within the four sub-watersheds. Box Cox transformations were used to normalize the measured soil depth and each explanatory variable. Random Forest prediction model was used to predict soil depth using the explanatory variables. The model was run using 336 data points in the calibration dataset with all 31 explanatory variables (18 variables from DEM and 13 variables from remote sensing image), and soil depth as the response of the model. Prediction errors were computed for validation data set. Testing dataset was done with the model soil depth values at testing locations (93 points). The Nash-Sutcliffe Efficiency coefficient (NSE) for testing data set was 0.689. The results showed that land use, Specific Catchment Area (SCA), NDVI, Aspect, Slope and PCA1 are the most important explanatory variables in predicting soil depth.
A. Khorsand, M. Bazaneh, S. Besharat, K. Zeynalzadeh,
Volume 22, Issue 4 (12-2018)
Abstract
Subsurface drip irrigation systems, compared to other irrigation systems (basin and furrow), enhance the delivery of water and nutrients directly into the root zone. The purposes of this study were to determine wetting front advancement in a subsurface drip irrigation and to compare the results with the HYDRUS 2D model simulation. In this study, the irrigation using T-Tape was carried out on a sandy-loam soil by two emitters at different irrigation times. The Wet moisture meter device was used to determine the soil water content. Evaluation of the simulated and measured soil water content was performed by using the adjusted determination coefficient (R2), relative error (RE), and the normalized root mean square error (NRMSE). Based on the results, the NRMSE of soil water content prediction for the emitters at the depths of 20 and 40 cm was calculated to be in the range of 10 to 19 and 10 to 13 percent, respectively. Also, RE for the emitters at depths of 20 and 40 cm was in the range of -16 to -5 and 8 to 11 percent, respectively. The average R2 for the emitters at depths of 20 and 40 cm was calculated to be 0.87 and 0.98, respectively. Also, five scenarios (F1, F2, T1, T2 and S1) were evaluated to assess the amount of water stored in the soil profile and water mass balance. The results indicated that the model could be used to predict the soil water content subsurface drip irrigation.
S. V. Razavi Termeh, K. Shirani, M. Soltani Rabii,
Volume 23, Issue 2 (9-2019)
Abstract
Today, supplying water to meet the sustainable development goals is one of the most important concerns and challenges in most countries. Therefore, identification of the areas with groundwater potential is an important tool for conservation, management and exploitation of water resources. The purpose of this research was to prepare the potential groundwater map in Nahavand, Hamedan Province, using the weight of evidence model and combining it with logistic regression. For this purpose, the information layers of slope angle, slope aspect, slope length, altitude, plan curvature, profile curvature, TWI, SPI, distance from fault, fault density, distance from river, drainage density, lithology and land use were identified as the factors affecting groundwater potential and digitized in the ArcGIS software. After designing the groundwater potential map with these three methods, ROCs were used to evaluate the results. Of 273 springs identified in this study, 191 (70%) were used to prepare the groundwater potential map and 82 springs (30%) were used to evaluate the model. The area under curve (AUC) obtained from the ROC curve showed an accuracy of 80.4% for the weight of evidence model and 82.5% for the weight of the evidence- regression combined model
M. Boustani, F. Mousavi, H. Karami, S. Farzin,
Volume 23, Issue 4 (12-2019)
Abstract
River discharge is among the influential factors on the operation of water resources systems and the design of hydraulic structures, such as dams; so the study of it is of great importance. Several effective factors on this non-linear phenomenon have caused the discharge to be assumed as being accidental. According to the basics the chaos theory, the seemingly random and chaotic systems have regular patterns that are predictable. In this research, by using methods of phase space mapping, correlation dimension, largest Lyapunov exponent and Fourier spectrum power, a period covering 43 years of Zayandehrud River discharge (1971-2013) was evaluated and analyzed based on the chaos theory. According to the results, the non-integer value of the correlation dimension for Eskandari and Ghale Shahrokh stations (3.34 and 3.6) showed that there was a chaotic behavior in the upstream of Zayandehrud-Dam Reservoir. On the other hand, in the Tanzimi-Dam station, the correlation dimension curve was ascending with respect to the embedding dimension, showing that the studied time-series in the downstream of Zayandehrud-Dam Reservoir was random. The slope of the Lyapunov exponent curve for Eskandari, Ghale Shahrokh and Tanzimi-Dam stations was 0.0104, 0.017 and 0.0192, respectively, and the prediction horizon in the chaotic stations was 96 and 59 days. The non-periodical feature of time series was studied by using the Fourier spectrum power. The wide bandwidth, besides other indices, showed that river discharge in the upstream stations of Zayandehrud Reservoir was chaotic.
L. Cheraghpoor, M. Pajoohesh, A. Davoodyan, A. Bozorgmehr,
Volume 23, Issue 4 (12-2019)
Abstract
River discharge is among the influential factors on the operation of water resources systems and the design of hydraulic structures, such as dams; so the study of it is of great importance. Several effective factors on this non-linear phenomenon have caused the discharge to be assumed as being accidental. According to the basics the chaos theory, the seemingly random and chaotic systems have regular patterns that are predictable. In this research, by using methods of phase space mapping, correlation dimension, largest Liapunov exponent and Fourier spectrum power, a period covering 43 years of Zayandehrud River discharge (1971-2013) was evaluated and analyzed based on the chaos theory. According to the results, the non-integer value of the correlation dimension for Eskandari and Ghale Shahrokh stations (3.34 and 3.6) showed that there was a chaotic behavior in the upstream of Zayandehrud-Dam Reservoir. On the other hand, in the Tanzimi-Dam station, the correlation dimension curve was ascending with respect to the embedding dimension, showing that the studied time-series in the downstream of Zayandehrud-Dam Reservoir was random. The slope of the Lyapunov exponent curve for Eskandari, Ghale Shahrokh and Tanzimi-Dam stations was 0.0104, 0.017 and 0.0192, respectively, and the prediction horizon in the chaotic stations was 96 and 59 days. The non-periodical feature of time series was studied by using the Fourier spectrum power. The wide bandwidth, besides other indices, showed that river discharge in the upstream stations of Zayandehrud Reservoir was chaotic.
A. Lotfi, M. Mamaghninejad,
Volume 23, Issue 4 (12-2019)
Abstract
Constructed wetland as a purification system plays an important role in water and wastewater treatment and so many research studies have been conducted to examine their efficiency for wastewater treatment. The aim of this study was to evaluate the efficiency of constructed wetland for Arak wastewater treatment plant. In this research, the efficiency of three horizontal subsurface constructed wetlands built with 3*12 meters in dimensions and 1 meter in depth was examined. In these constructed wetlands, two plants including Common reed (Phragmites australis) and Cattails (Typha latifolia) were planted and one unit was left unplanted. TSS, COD, BOD and TC parameters were measured in the 2 week samples and the results were analyzed by SPSS and Excel. The results showed that the type of vegetation had no significant influence on the organic matter removal in the subsurface constructed wetlands; however, the removal efficiencies in the planted constructed wetlands were more than those in the unplanted control one. The TSS, COD, BOD, FC and TC removal efficiency in the constructed wetlands changes was 79%, 60.7%, 45.6%, 86.1% and 90.1%, respectively, for Common reed wetland and 77%, 61.4%, 59.8%, 92.4% and 93.1%, respectively, for Cattails wetland; thee were 69%, 44.5%, 43%, 83.6% and 88.8% for the unplanted wetland, respectively. The results of this research also showed that the organic matter removal was dependent on the influent organics nature and biodegradability. The organic concentration in the wetland effluents met the Iranian regulation limits for different reuse applications, showing the constructed wetland could be a suitable technology for wastewater treatment in Iran.
H. Shabani, M. A. Delavar, S. T. Fardood,
Volume 24, Issue 1 (5-2020)
Abstract
Today, to reduce the risks of contaminants, new remediation techniques have been focused on low-cost and environmentally friendly manners. Given the frequency of access, inexpensiveness and good physical and chemical properties, biochar has a high potential for the remediation of water pollutants. In this paper, the efficiency of chitosan engineered biochar (Bc-Ch) and pristine biochar (Bg-Bc) prepared from sugarcane bagasse biomass (Bg) in the Cd2+ removal in aqueous solution was investigated. To this aim, the effects of contact time, adsorbent dosage and solution pH on cadmium removal were evaluated by adsorption isotherms and Kinetic models. The results indicated that the Langmuir isotherm and the pseudo-second-order kinetic model could be well fitted with the process of cadmium biosorption. The maximum adsorption capacities of Bc-Ch, Bg-Bc and Bg ,according to Langmuir model, were found to be 32/78 mg/g, 11/57 mg/g and 2/23 mg/g, respectively. For these absorbents, the pseudo-second-order kinetic model showed the best fit to the experimental adsorption data. This study, therefore, indicated that the chitosan engineered biochar could be used as an effective, low-cost, and environmentally-friendly sorbent to remediate heavy metals contamination in the environment.
Z. Maghsodi, M. Rostaminia, M. Faramarzi, A Keshavarzi, A. Rahmani, S. R. Mousavi,
Volume 24, Issue 2 (7-2020)
Abstract
Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.
F. Afzalinejad, S. Ghasemi, S. E. Seyfati, Sh. Shahbazi,
Volume 24, Issue 4 (11-2020)
Abstract
Today, the use of organic wastes as fertilizers to improve the physical, chemical and biological properties of soil is common. In this study, to investigate the effect of the sewage sludge on the growth and concentration of chlorophyll, nitrogen, phosphorus and potassium in quinoa plant, a factorial experiment was conducted based on a completely randomized design with three replications in a calcareous soil with electrical conductivity of 13.1 dS m-1. Treatments included three genotypes of quinoa (Red carina, Titicaca, Q29) and three levels of sewage sludge (0, 20 and 40 t ha-1). The results showed that the effects of the quinoa genotype, different levels of sewage sludge and the interaction of treatments on the root dry weight, shoot P concentration and chlorophyll concentration were significant. Also, the sewage sludge had a significant effect on the shoot length, shoot dry weight and shoot nitrogen concentration. By increasing the level of the sewage sludge, root length, shoot length, shoot dry weight and shoot N concentration were increased, on average, by 44.7 %, 48.8%, 42.0% and 46.6%, respectively. Also, application of 40 t ha-1 sewage sludge significantly increased the chlorophyll concentration and shoot P concentration in Q29 and Titicaca genotypes. According to the results of this study, application of sewage sludge can increase the growth of quinoa by improving its nutrition.
H. R. Matinfar, Z. Mghsodi, S. R. Mossavi, M. Jalali,
Volume 24, Issue 4 (11-2020)
Abstract
Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC.
A. Ghorbani, M. Moameri, F. Dadjou, L. Andalibi,
Volume 25, Issue 2 (9-2021)
Abstract
The purpose of this study was to model biomass with soil parameters in Hir-Neur rangelands of Ardabil Province. Initially, considering the vegetation types and different classes of environmental factors, at the maximum vegetative growth stage, using one square meter plot, biomass was estimated by clipping and weighing method. For each transect, a soil sample was taken and transferred to the soil laboratory and the various parameters were measured by conventional methods. The relationship between soil factors and the rangeland biomass was analyzed and simulated using linear multiple regression. Among the measured soil factors, the Silt, EC, Ca, Ksoluble, OC, POC, pH, Mg, TNV, clay, P, and volumetric moisture had the highest effect and percentage of biomass forecast (p<0.01). The accuracy of the simulated maps was analyzed using RMSE criteria and for grasses, forbs, shrubs, and total biomass were equal to 0.81, 0.65, 0.34, and 0.46, respectively. The results of this study, not only point out the importance of soil factors on the biomass but also as a baseline data for managing rangelands, supply-demand, and carbon balance can be used in the current section.
M. Pakmanesh, H. Mousavi Jahromi, A. Khosrojerdi, H. Hassanpour Darvishi, Hossein Babazadeh,
Volume 25, Issue 3 (12-2021)
Abstract
The present study is investigated the earth dam stability during drawdown based on both numerical and experimental aspects. To validate the numerical model, a model was performed experimentally. Some soil mechanic tests were carried out through the hydraulic experiments to attain the usage factors of the numerical investigation. To investigate the effect of hydraulic conductivity on the rapid drop of water level and the use of hydraulic parameters of materials, seepage flow in the model was modeled by seep/w software. The input information to the software including hydraulic conductivity and water volume were measured by performing a constant load test and using a disc penetration meter, respectively. After validation of hydraulic conductivity with the experimental model, the results were compared with observed data. Comparison between numerical and laboratory discharge illustrated that the numerical model with laboratory model is well confirmed. In addition, saturated and unsaturated simulations demonstrated that the unsaturated model is highly consistent with the experimental model. It is assumed that due to the drawdown conditions, unsaturated models can achieve high accuracy for simulating the flow through a homogeneous earth dam.
F. Zarif, A. Asareh, M. Asadiloor, H. Fathian, D. Khodadadi Dehkordi,
Volume 26, Issue 2 (9-2022)
Abstract
An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.
A. Motamedi, J. Abedi-Koupai, A.r. Gohari,
Volume 26, Issue 2 (9-2022)
Abstract
Water scarcity and lack of soil fertility are two major problems in the agriculture sector. This study aimed to use Azolla anzali and Lemna minor as a cover for a free surface of the water since not only do they have the potential to reduce evaporation, but they can also produce green fertilizer. Therefore, a completely randomized design experiment with 4 treatments (Azolla anzali, Lemna minor, combination of Azolla anzali+ Lemna minor and control) was performed with three replications. The surface of the reservoirs was covered with the mentioned plants and the changes in water height were measured every other day and the amount of nutrients (nitrogen and phosphorus) of the plant tissue was measured three times at the beginning, middle, and end of the period. Eventually, water loss in tanks containing Lemna, Azolla, and Lemna+ Azolla, was 39, 33.2, and 28.7% less than the control tank. The highest amount of nutrients in plant tissue was observed in Lemna, Azolla+ Lemna, and Azolla treatments, respectively. Although the amount of nutrients in the combined treatment was not higher than that of Lemna more biomass was produced, which means it can provide more fertilizer. Finally, the combined treatment of the two plants is a more suitable option to be used.
F. Zarei, M.r. Nouri Emamzadehei, A.r. Ghasemi Dastgerdi, A. Shahnazari,
Volume 26, Issue 4 (12-2022)
Abstract
The pattern of root distribution in layered soils is one of the significant issues in the calculations of soil water and irrigation management and planning. The objective of this study was to determine the pattern of root distribution of soybean in layered soils and its effect on water uptake. The research was conducted in a completely randomized design with 15 treatments consisting of three different textures of soil (light, heavy, and medium) in four replications. The pattern of root distribution was monitored by the sampling of columns at the end of the growth period of the soybean. It was observed that the presence of the layer with medium texture has led to better plant development and growth after comparing the treatments in terms of plant growth. In general, root length density decreased with increasing soil depth, except in cases where there were different layers of soil, and root length density takes place in the following order: root length density in layers with medium texture≥ heavy texture≥ light texture. The rate of root water uptake rate was highest in the sandy layers, intermediate in clay, and lowest in loamy texture. Also, the rate of root water uptake rate increased significantly with increasing depth regardless of treatments. It can be concluded that the pattern of root distribution and plant growth is significantly affected by soil texture and its stratification.
M. Zarinibahador,
Volume 29, Issue 1 (4-2025)
Abstract
The calcium carbonate equivalent (CCE) in soil is one of the most important soil properties. Predicting the amount of calcium carbonate equivalent in soil is essential for sustainable soil fertility management. The present study aimed to digitally map calcium carbonate equivalent using auxiliary environmental variables, Landsat 8 satellite images, and predictive models and to present the best models in the Badr watershed in the south of Qorveh district. In the first phase, a geomorphologic map was created using a geologic map and based on the ZINC method in a geographic information system environment. In the second phase, the location of 125 survey profiles was determined using the Latin hypercube technique, and the calcium carbonate equivalent of the soil horizons was measured by acid titration. The auxiliary variables included derivatives of the digital elevation model, remote sensing indices from the Landsat 8 satellite, and a geopedological map. The principal component analysis (PCA) method was used to select suitable auxiliary variables. In the third phase, the modeling was carried out, digital maps of the soil classes and properties were created, and the models were evaluated. Two different cases were investigated in this study to estimate the calcium carbonate equivalent of the soil. In the first case, artificial neural network models, decision tree analysis, random forest, and the K-nearest neighbor model were used for prediction. The multiple linear regression model was also used to combine the results of the models. Among the models used to predict the equivalent amount of calcium carbonate using the 10-fold cross-validation method, the multiple linear regression (MLR) model had the highest prediction accuracy with a coefficient of determination of 0.796 and a mean square error of 6.514. In the 5-fold cross-validation method, the K-nearest neighbor (KNN) model had the highest predictive accuracy with a coefficient of determination of 0.9845 and a root mean square error of 2.1258. Due to the spatial nature of the 10-fold cross-validation method, the use of this method is preferable to the 5-fold cross-validation method. In addition, the most important auxiliary variables in order of importance to predict the calcium carbonate equivalent in soil were the carbonate index, slope direction, geomorphology, the base level of the catchment network, and the slope of the catchment.
Narjes Sanchooli, Hashem Khandan Barani,
Volume 29, Issue 4 (12-2025)
Abstract
The biological desalination system has lower energy consumption and environmental impacts, as well as simpler engineering technology and complexity compared to conventional desalination methods. This study aimed to investigate the effect of nutrients in the Chlorella vulgaris algae culture medium on the rate of algae growth, salinity reduction, TDS, and EC. For this purpose, an amount of algae was inoculated into culture media-containing treatments to achieve a density of 5 × 106 cells/ml. The results showed that the highest amount of dry biomass of algae was in the deep aquifer well water + BG-11 culture medium treatment, with a value of 0.76 ± 0.02 g. The highest amount of chlorophyll a and b was observed on days 4, 17, and 30 in the control treatment, which was significantly different from the other treatments (p < 0.05). The lowest value of light absorption of algae was observed in the control treatment on all days. At the end of the 30-day experimental period, the highest reduction in salinity, TDS, and EC was observed with 27.60, 26.83, and 41.60 percent reduction in the deep aquifer well water + culture medium treatment, respectively, which showed a significant difference (p < 0.05) with the deep aquifer well water treatment. The results showed that deep aquifer well water, due to its nutrient content, has a high potential for algae growth and, as a result, biological desalination and the absence of the use of commercial culture medium, which can reduce desalination costs.
