Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)

Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. Then, multivariate regression and neural network model (feed-forward back propagation network) were employed to develop a pedotransfer function for predicting soil parameter using easily measurable characteristics of clay and organic carbon. The performance of the multivariate regression and neural network model was evaluated using a test data set. In order to evaluate the models, root mean square error (RMSE) was used. The value of RMSE and R2 derived by ANN model for CEC were 0.47 and 0.94 respectively, while these parameters for multivariate regression model were 0.65 and 0.88 respectively. Results showed that artificial neural network with seven neurons in hidden layer had better performance in predicting soil cation exchange capacity than multivariate regression

SOIL SPECTRAL PROPERTIES OF ARID REGION, KASHAN AREA, IRAN

This study determined some spectral characteristics and relationship between Landsat spectral reflectance and soil surface color in the arid region of Iran (Kashan). The study carried out in the kashan area that covers 90000 ha. Consisting of mountain, hills and flood plain. Enhanced Thematic Mapper (ETM+) data collected on July 2002 were used for this research. The color composite images produced from ETM+7, ETM+4 and ETM+2 as red, green and blue respectively used in order to choose sample sites. The twelve sample sites were chosen based on resampled 3*3 pixels (90*90 m). In each site, the soil surface conditions and the munsell color of the soil surface were investigated in the field. Some physico-chemical properties of soil samples were also determined in the laboratory. Soil surface particle sizes were categorized into three classes: bare soil 2mm in diameter and vegetated soil. The results of this study indicates that munsell notation of hue, value and chroma are significantly correlated to the visible bands of Landsat (ETM+) data. From this study it may be concluded that visible reflectance of Landsat can be used to estimate soil color, if very precise result is not expected .More investigation are necessary in order to improve the obtained results

Climate change impact on bioclimatic deficiency, using microLEIS DSS in Ahar soils, Iran

Regional impact studies of the future climate change effects are necessary because projected changes in meteorological variables differ from one region to another, and different climate systems can react in varied ways to the same changes. In this study, the effects of climate change on bioclimatic deficiency were compared in two cultivation methods (irrigated and rainfed) in a semi-arid region, Ahar (East Azarbaijan, IRAN). The agricultural land uses selected for evaluation were wheat (Triticum aestivum), alfalfa (Medicago sativa), sugar beet (Beta vulgaris), potato (Solanum tuberosum), and maize (Zea mays). In this way, Terraza model included in the land evaluation decision support system, called MicroLEIS DSS, was used. Terraza gives a quantitative prediction of a site bioclimatic deficiency. Soil morphological and analytical data were obtained from 44 sampling points based on a grid survey. Agro-climatic data, referred to temperature and precipitation, were collected from weather stations located in Ahar region, which benefits from more than 20 consecutive years of weather data. A future scenario of climate change was calculated according to the Intergovernmental Panel on Climate Change (IPCC) on regions of Asia under scenario A1FI (highest future emission) for 2080s. Although, increasing of precipitation being available by climate change in the future scenario, humidity index will be reduced because of high temperature. The results showed that climate change is likely to cause severe water stress in irrigated cultivation of alfalfa, sugar beet, potato, and maize, the use of irrigation methods being essential to maintain agricultural productivity. Although irrigation is indicated as very important in this regime of semi-arid agriculture, cultivation of rainfed wheat can be possible instead of the irrigated one. Also, it is revealed that climate perturbation effects on rainfed conditions are more serious than those on the irrigated conditions in the area.The authors wish to thank Tabriz University for funding this research work, a dissertation of Ph. D. program undertaken by Farzin Shahbazi. They also thank Consejo Superior de Investigaciones Científicas (CSIC), Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), Sevilla, Spain for their sincere cooperation during the candidate’ s sabbatical studies.Peer Reviewe

Digital Mapping of Surface and Subsurface Soil Organic Carbon and Soil Salinity Variation in a Part of Qazvin Plain (Case Study: Abyek and Nazarabad Regions)

IntroductionKnowledge of the spatial distribution of soil salinity and soil organic carbon (SOC) leads to obtaining valuable information that is effective in decision-making for agricultural activities. More than a third of the world's land is affected by salt, which threatens the growth and production of crops, and prevents the development of sustainable agriculture. The high electrical conductivity (EC) content in soils poses significant challenges in arid and semi-arid regions, greatly impacting agricultural production. Saline and sodic soils often exhibit high levels of sodium which is a key characteristic. The presence of sodium ions leads to the destabilization of soil aggregates and the dispersion of soil particles resulting in the closure of soil pores. Consequently, unfavorable changes occur in the soil physical, chemical, and biological properties increasing its susceptibility to water and wind erosion. Additionally, high sodium levels can lead to the decomposition of soil organic carbon (SOC). SOC is crucial for water retention, cation exchange, and nutrient availability, making its reduction in agricultural soils a significant threat to sustainable soil management. Therefore, the investigation of soils in terms of EC and SOC contents and their spatial distribution is of great importance to support decision-makers in agricultural development planning to reduce challenges related to food security in arid and semi-arid regions.Materials and MethodsThis study was conducted with the aim of investigating the EC and SOC in topsoil (0-30 cm) and subsoil (30-60 cm) layers using four machine learning (ML) algorithms namely, random forest (RF), decision tree (DTr), support vector regression (SVR) and artificial neural network (ANN) performed in Qazvin Plain. The study area includes a part of agricultural lands and natural areas of Alborz and Qazvin provinces, between the Nazarabad and Abyek cities in Iran. This region with an area of 60,000 hectares is located at latitude 35° 54´ to 36° 54´ to the north and 50° 15´ to 50° 39´ to the east. This research was carried out in four stages including (i) soil sampling and measuring the physical and chemical properties of the soil and preparation of environmental covariates from a digital elevation model (DEM) with spatial resolution 12.5 m and Landsat 8 satellite imagery with spatial resolution 30 m by SAGA GIS and ENVI software, (ii) spatial modeling of soil EC and SOC in the topsoil and subsoil layers by the RF, SVR, ANN, and DTr ML algorithms, (iii) evaluating the efficiency of the ML algorithms and determining the relative importance of environmental covariates, and (iv) preparation of spatial prediction maps of EC and SOC in the topsoil (0-30 cm) and subsoil (30-60 cm) layers in the study area.Results and Discussion         The result of the spatial prediction maps of EC showed that the studied area has non-saline to very saline soils up to a depth of 60 cm. It is also possible that the EC equivalent shows a decreasing trend in soil salinity with a depth from 6.05 to 5.55 ds/m from the topsoil to the subsoil layer. The highest amount of SOC was observed in the surface layer equal to 3.3%. Globally SOC content decreased from the surface (average of 0.84%) to depth (average of 0.4%). The high spatial variability of SOC showed that the soils of the study area are affected by management activity. Environmental covariates were extracted as a proxy of topography and remote sensing indices including elevation, diffuse Insolation (Diffuse), Multi-Resolution Index of Valley Bottom Flatness (MrVBF), Normalized Differences Vegetation Index (NDVI), SAGA wetness index (SWI) and wind Effect (WE) were used as representatives of soil formation factors. The topography parameters, including the elevation, diffuse insolation, and Multi-Resolution Index of Valley Bottom Flatness, were most closely related to EC and SOC variations in each topsoil and subsoil layer. Elevation can be justified around 50% and 35% of EC and 28.56% and 29.47% of SOC variations in the topsoil and subsoil layers, respectively, followed by the diffuse variable can succeed to justified 19.7% and 25.1% of EC and 27.28% and 27.67% of SOC spatial variations in the topsoil and subsoil layers, respectively.The results confirmed that the RF was recognized as outperforming the ML model for predicting EC in the topsoil (R2 =0.74, RMSE =0.36, and nRMSE= 0.07), as well as predicting SOC in topsoil and subsoil layers (R2= 90 and R2=0.80), followed by the DTr for predicting EC (R2 0.77, RMSE/0.9, and nRMSE 0.17) in the subsoil layer in comparison other models. Conclusion       The RF (Random Forest) and DTr (Decision Tree) models incorporating topographic parameters demonstrated satisfactory accuracy in predicting the variation of topsoil and subsoil electrical conductivity (EC) and soil organic carbon (SOC) in the study area. Topography plays a crucial role in soil formation, and elevation-based topographic attributes are commonly used as key predictors in digital soil mapping projects. The variability in topography influences water flow and sedimentation processes which, in turn, affects soil development and the spatial distribution of soil properties. The resulting soil maps can be valuable tools for decision-making programs related to soil management in the region

Correlation models of critical heat flux and associated temperature for spray evaporative cooling of vibrating surfaces

Prediction models have been constructed to investigate the effect of vibrating surfaces on the critical heat flux (CHF) and its associated temperature in spray evaporative cooling. Dimensional analysis has been used to construct the models to account for the influence of key dynamic parameters. Experimental measurements have been obtained from a flat, electrically-heated, copper test-piece, located inside a spray-chamber mounted on top of a shaker. A wide range of large-amplitude and high-frequency measurements have been obtained which correspond to test conditions for a piece of hardware mounted on board a light-duty automotive vehicle with vibration amplitudes ranging from 0 to 8 mm and frequencies from 0 to 200 Hz. Three nozzle types have been fed with distilled water at flow rates ranging from 55 to 100 ml/min being used to cool with subcooling degrees ranging from 10°C to 45°C. Measured data for both static and dynamic cases have been used to explore the influence on the CHF and the surface-to-fluid saturation temperature at which this occurs, of subcooling degrees, surface vibration amplitude and frequency, vibrational Reynolds Number and vibrational Acceleration Number. The measured data has also subsequently been used to calibrate the predictive models for use in thermal management systems. Static measurements (without vibration) show that the influence of flow rate, volumetric flux, and subcooling are largely in agreement with published literature. For dynamic cases, the influence of vibration is best explained in terms of the nondimensional parameters: Vibration Reynolds Number and Acceleration Number. The effect of vibration on CHF and associated temperature is assessed in detail for the three nozzle types at different flow rates and degrees of subcooling. Predictions of CHF and associated excess temperature, using the calibrated correlation models for the dynamic conditions, are very reasonable, and suitable for the intended purpose of ensuring safe operation of thermal management systems using spray evaporative cooling

Evaluación de algunos modelos de infiltración y de algunos parámetros hidráulicos

The evaluation of infiltration characteristics and some parameters of infiltration models such as sorptivity and final steady infiltration rate in soils are important in agriculture. The aim of this study was to evaluate some of the most common models used to estimate final soil infiltration rate. The equality of final infiltration rate with saturated hydraulic conductivity (Ks) was also tested. Moreover, values of the estimated sorptivity from the Philip's model were compared to estimates by selected pedotransfer functions (PTFs). The infiltration experiments used the doublering method on soils with two different land uses in the Taleghan watershed of Tehran province, Iran, from September to October, 2007. The infiltration models of Kostiakov-Lewis, Philip two-term and Horton were fitted to observed infiltration data. Some parameters of the models and the coefficient of determination goodness of fit were estimated using MATLAB software. The results showed that, based on comparing measured and model-estimated infiltration rate using root mean squared error (RMSE), Horton's model gave the best prediction of final infiltration rate in the experimental area. Laboratory measured Ks values gave significant differences and higher values than estimated final infiltration rates from the selected models. The estimated final infiltration rate was not equal to laboratory measured Ks values in the study area. Moreover, the estimated sorptivity factor by Philip's model was significantly different to those estimated by selected PTFs. It is suggested that the applicability of PTFs is limited to specific, similar conditions.En la agricultura es importante evaluar las características de la infiltración y de algunos parámetros de los modelos de infiltración, como sortividad y tasa de infiltración constante final de los suelos. El objetivo de este estudio fue evaluar algunos de los modelos utilizados más frecuentemente para estimar la tasa de infiltración final del agua en el suelo, así como estudiar la relación de la tasa de infiltración final con la conductividad hidráulica saturada (Ks) y comparar los valores estimados de la sortividad según el modelo de Philip con los estimados según funciones seleccionadas de edafotransferencia (PTFs). Se realizaron unos experimentos de infiltración en la cuenca de drenaje Taleghan, Teherán, Iran, desde septiembre a octubre de 2007, mediante el método del doble anillo, en suelos con dos diferentes usos. Se ajustaron los modelos de infiltración de Kostiakov-Lewis, el de dos términos de Philip y el de Horton a los datos de infiltración observados. Se estimó la bondad del ajuste de algunos parámetros de los modelos y el coeficiente de determinación mediante el software MATLAB. Al comparar la tasa de infiltración medida y estimada según los modelos, utilizando la raíz del cuadrado medio del error (RMSE), los resultados mostraron que el modelo de Horton predijo mejor la tasa de infiltración final. Los valores Ks medidos en laboratorio fueron más altos y significativamente diferentes que las tasas de infiltración finales estimadas según los modelos seleccionados. La tasa de infiltración final estimada no fue igual a los valores Ks medidos en laboratorio. Además, el factor de sortividad estimado según el modelo Philip fue significativamente diferente de aquellos estimados según PTFs seleccionados. Se sugiere que la aplicabilidad de los PTFs se limite a condiciones específicas y similares