Soil science
Sh. Asghari; M. Hasanpour Kashani; H. Shahab Arkhazloo
Abstract
IntroductionThe penetration resistance (PR) of the soil shows the mechanical resistance of the soil against the penetration of a conical or flat probe; it is important in terms of seed germination, root growth and tillage operations. In general, if the PR value of a soil exceeds 2.5 MPa, the growth and ...
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IntroductionThe penetration resistance (PR) of the soil shows the mechanical resistance of the soil against the penetration of a conical or flat probe; it is important in terms of seed germination, root growth and tillage operations. In general, if the PR value of a soil exceeds 2.5 MPa, the growth and expansion of roots in the soil will be significantly limited. The direct measurement of PR is also a laborious and costly task due to instrumental errors. Therefore, it is useful the use of different models such as multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) to estimate PR through easily accessible and low-cost soil characteristics. The objectives of this research were: (1) to obtain MLR, ANN and GEP models for estimating PR from the easily accessible soil variables in forest, range and cultivated lands of Fandoghloo region of Ardabil province, (2) to compare the accuracy of the aforementioned models in estimating soil PR using the coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) criteria. Materials and MethodsDisturbed and undisturbed samples (n = 80) were nearly systematically taken from 0-10 cm soil depth with nearly 50 m distance in forest (n = 20), range (n = 23) and cultivated (n = 37) lands of Fandoghloo region of Ardabil province, Iran (lat. 38° 24' 10" to 38° 24' 25" N, long. 48° 32' 45" to 48° 33' 5" E) in summer 2023. The contents of sand, silt, clay, CaCO3, pH, EC, bulk (BD) and particle density (PD), organic carbon (OC), gravimetric field water content (FWC), mean weight diameter (MWD) and geometric mean diameter (GMD) were measured in the laboratory. Relative bulk density (BDrel) was calculated using BD and clay data. Mean geometric diameter (dg) and geometric standard deviation (σg) of soil particles were computed by sand, silt and clay percentages. The penetration resistance (PR) of the soil was measured in situ using cone penetrometer (analog model) at 5 replicates. Data randomly were divided in two series as 60 data for training and 20 data for testing of models. The SPSS 22 software with stepwise method, MATLAB and Gene Xpro Tools 4.0 software were used to derive multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) models, respectively. A feed forward three-layer (2, 5 and 6 neurons in hidden layer) perceptron network and the tangent sigmoid transfer function were used for the ANN modeling. A set of optimal parameters were chosen before developing a best GEP model. The number of chromosomes and genes, head size and linking function were selected by the trial and error method, as they are 30, 3, 8, and +, respectively. The rates of genetic operators were chosen according to literature studies. The accuracy of MLR, ANN and GEP models in estimating PR were evaluated by coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) statistics. Results and Discussion The studied soils had clay loam (n = 11), sandy clay loam (n = 6), sandy loam (n = 12), loam (n = 13), silty clay loam (n = 14), silty clay (n = 1) and silt loam (n = 23) textural classes. The values of sand (13.14 to 64.79 %), silt (21.11 to 74.96 %), clay (2.95 to 42.18 %), OC (1.01 to 7.17 %), FWC (11.58 to 50.47 mass percent), BD (0.84 to 1.43 g cm-3) and PR (1.03 to 5.83 MPa) showed good variations in the soils of the studied region. There were found significant correlations between PR with FWC (r = - 0.45**), silt (r = - 0.36**) and σg (r = 0.36**). Due to the multicollinearity of silt with σg (r = -0.84**), the σg was not used as an input variable to estimate PR. Generally, 3 MLR, ANN and GEP models were constructed to estimate PR from measured readily available soil variables. The results of MLR, ANN and GEP models showed that the most suitable variables to estimate PR were FWC, silt and BDrel. The values of R2, RMSE, ME and NS criteria were obtained equal 0.44, 1.19 MPa, 0.19 MPa and 0.36, and 0.92, 0.41 MPa, -0.05 MPa and 0.92, 0.79, 0.91 MPa, 0.13 MPa, 0.63 for the best MLR, ANN and GEP models, respectively. The former researchers also reported that there is a negative and significant correlation between PR with FWC. Conclusion The results indicated that field water content (FWC), silt and relative bulk density (BDrel) were the most important and readily available soil variables to estimate penetration resistance (PR) in the studied area. According to the lowest values of RMSE and the highest values of NS, the accuracy of ANN models to predict soil PR was higher than MLR and GEP models in this research.
mahdi selahvarzi; B. Ghahraman; H. Ansari; K. Davari
Abstract
Introduction: Evaporation takes place from vegetation cover, from bare soil, or water bodies. In the absence of a vegetation cover, soil surface is exposed to atmosphere which increases the rate of evaporation. Evaporation of soil moisture will not only lead to water losses but also increase the risk ...
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Introduction: Evaporation takes place from vegetation cover, from bare soil, or water bodies. In the absence of a vegetation cover, soil surface is exposed to atmosphere which increases the rate of evaporation. Evaporation of soil moisture will not only lead to water losses but also increase the risk of soil salinity. The risk is increased under low annual rainfall, saline irrigation water and deep water table. Soil and water salinity is common in arid and semiarid regions where using saline water is common under insufficient fresh water resources. Evaporation is one of the main components of water balance in each region and also one of the key factors for proper irrigation scheduling towards improving efficiency in the region. On the other hand evaporation has a significant role in global climate through the hydrological cycle and its proper estimation is important to predict crop yield soil salinity, water loss of irrigation canals, water structure and also on natural disasters such as drought phenomenon. There are three distinct phases for evaporation process. Step Rate – initial stage is when the soil reaches enough moisture to transfer water to evaporate at a rate proportional to the evaporative demand. During this stage, the evaporation rate by external weather conditions (solar radiation, wind, temperature, humidity, etc.) is limited and therefore can be controlled, in other words, the role of soil characteristics will occur. In this case the air phase - control (at this stage the stage profile – control). Next step is to reduce the rate of evaporation rates during this stage of succession is less than the potential rate (evaporation, atmospheric variability). At this point, evaporation rate (the rate at which the soil caused by the drying up) can deliver the level of moisture evaporation in the area is limited and controlled. So it can be a half step - called control. This may be longer than the first stage.. Apparently when the soil surface is dry to the extent that, it is effectively cut off from water, this phase starts. This stage is often called vapor diffusion process where the surface layer so as to be able to dry quickly can be important.
Materials and Methods: This study was conducted to test the texture of sandy clay and four salinity levels (0.7, 2, 4 and 8 dS m-1 (the study used a PVC pipe with a diameter of 110 mm and a height of about 1 m (for the 90 cm soil profile). Evaporation measurements and weight measurements were performed using a water balance. Also the water out of the soil columns were carefully measured. Weight was measured in soil columns has been done with a digital scale with an accuracy of 5 g. The calculation of evaporation ,obtained by subtracting the weight of the soil column twice in a row, low weight and water out of the soil column.
Results and Discussion: Evaporation decreased with increasing salinity of the soil, even in the first stage mentioned earlier by external meteorological conditions (eg, radiation, wind, temperature and humidity) controlled, observed. It should be recognized that the ability of the atmosphere to evaporate completely independent of the properties of the object that is no evaporation occurs. Moreover, if we assume that the object is completely independent of the properties of water surface evaporation exactly equals, salinity reduced the water vapor pressure resulting in reduced evaporates. The first stage of evaporation decreases by increasing salinity, evaporation would be justified.
Gholam Reza Sheykhzadeh; shokrollah asghari; Tarahom Mesri Gundoshmian
Abstract
Introduction: Penetration resistance is one of the criteria for evaluating soil compaction. It correlates with several soil properties such as vehicle trafficability, resistance to root penetration, seedling emergence, and soil compaction by farm machinery. Direct measurement of penetration resistance ...
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Introduction: Penetration resistance is one of the criteria for evaluating soil compaction. It correlates with several soil properties such as vehicle trafficability, resistance to root penetration, seedling emergence, and soil compaction by farm machinery. Direct measurement of penetration resistance is time consuming and difficult because of high temporal and spatial variability. Therefore, many different regressions and artificial neural network pedotransfer functions have been proposed to estimate penetration resistance from readily available soil variables such as particle size distribution, bulk density (Db) and gravimetric water content (θm). The lands of Ardabil Province are one of the main production regions of potato in Iran, thus, obtaining the soil penetration resistance in these regions help with the management of potato production. The objective of this research was to derive pedotransfer functions by using regression and artificial neural network to predict penetration resistance from some soil variations in the agricultural soils of Ardabil plain and to compare the performance of artificial neural network with regression models.
Materials and methods: Disturbed and undisturbed soil samples (n= 105) were systematically taken from 0-10 cm soil depth with nearly 3000 m distance in the agricultural lands of the Ardabil plain ((lat 38°15' to 38°40' N, long 48°16' to 48°61' E). The contents of sand, silt and clay (hydrometer method), CaCO3 (titration method), bulk density (cylinder method), particle density (Dp) (pychnometer method), organic carbon (wet oxidation method), total porosity(calculating from Db and Dp), saturated (θs) and field soil water (θf) using the gravimetric method were measured in the laboratory. Mean geometric diameter (dg) and standard deviation (σg) of soil particles were computed using the percentages of sand, silt and clay. Penetration resistance was measured in situ using cone penetrometer (analog model) at 10 replicates. The data were divided into two series as 78 data for training and 27 data for testing. The SPSS 18 with stepwise method and MATLAB software were used to derive the regression and artificial neural network, respectively. A feed forward three-layer (8, 11 and 15 neurons in the hidden layer) perceptron network and the tangent sigmoid transfer function were used for the artificial neural network modeling. In estimating penetration resistance, The accuracy of artificial neural network and regression pedotransfer functions were evaluated by coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Akaike information criterion (AIC) statistics.
Results and discussion: The textural classes of study soils were loamy sand (n= 8), sandy loam (n= 70), loam (n= 6) and silt loam (n= 21). The values of sand (26.26 to 87.43 %), clay (3.99 to 17.34 %), organic carbon (0.3 to 2.41 %), field moisture (4.56 to 33.18 mass percent), Db (1.02 to 1.63 g cm-3) and penetration resistance (1.1 to 6.6 MPa) showed a large variations of study soils. There were found significant correlations between penetration resistance and sand (r= - 0.505**), silt (r= 0.447**), clay (r= 0.330**), organic carbon (r= - 0.465**), Db (r= 0.655**), θf (r= -0.63**), CaCO3 (r= 0.290**), total porosity (r= - 0.589**) and Dp (r= 0.266*). Generally, 15 regression and artificial neural network pedotransfer functions were constructed to predict penetration resistance from measured readily available soil variables. The results of regression and artificial neural network pedotransfer functions showed that the most suitable variables to estimate penetration resistance were θf, Db and particles size distribution. The input variables were n and θf for the best regression pedotransfer function and also Db, silt, θf and σg for the best artificial neural network pedotransfer function. The values of R2, RMSE, ME and AIC were obtained equal to 0.55, 0.89 MPa, 0.05 MPa and -14.67 and 0.91, 0.37 MPa, - 0.0026 MPa and -146.64 for the best regression and artificial neural network pedotransfer functions, respectively. The former researchers also reported that there is a positive correlation between penetration resistance with Db and a negative correlation between penetration resistance with θf and organic carbon.
Conclusion: The results showed that silt, standard deviation of soil particles (σg), bulk density (Db), total porosity and field water content (θf) are the most suitable readily available soil variables to predict penetration resistance in the studied area. According to the RMSE and AIC criteria, the accuracy of artificial neural network in estimating soil penetration resistance was more than regression pedotransfer functions in this research.
S. Samadianfard; R. Delirhasannia
Abstract
Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as ...
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Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as artificial neural networks, fuzzy systems and genetic programming in water science and engineering have been grown extensively. These mentioned methods are able to model nonlinear process of river flows without any need to geometric properties. A huge number of studies have been reported in the field of using intelligent methods in water resource engineering. For example, Noorani and Salehi (23) presented a model for predicting runoff in Lighvan basin using adaptive neuro-fuzzy network and compared the performance of it with neural network and fuzzy inference methods in east Azerbaijan, Iran. Nabizadeh et al. (21) used fuzzy inference system and adaptive neuro-fuzzy inference system in order to predict river flow in Lighvan river. Khalili et al. (13) proposed a BL-ARCH method for prediction of flows in Shaharchay River in Urmia. Khu et al. (16) used genetic programming for runoff prediction in Orgeval catchment in France. Firat and Gungor (11) evaluated the fuzzy-neural model for predicting Mendes river flow in Turkey. The goal of present study is comparing the performance of genetic programming and M5 model trees for prediction of Shaharchay river flow in the basin of Lake Urmia and obtaining a comprehensive insight of their abilities.
Materials and Methods: Shaharchay river as a main source of providing drinking water of Urmia city and agricultural needs of surrounding lands and finally one of the main input sources of Lake Urmia is quite important in the region. For obtaining the predetermined goals of present study, average monthly flows of Shaharchay River in Band hydrometric station has been gathered from 1951 to 2011. Then, two third of mentioned data were used for calibration and the rest were used for validation of study models including genetic programming and M5 model trees. It should be noted that for prediction of Shaharchay river flows, previous data of mentioned river in 1, 2 and 3 months ago (Q, Q, Q) were used.
Genetic programming: was first proposed by Koza (17). It is a generalization of genetic algorithms. The fundamental difference between genetic programming and genetic algorithm is due to the nature of the individuals. In genetic algorithm, the individuals are linear strings of fixed length (chromosomes). In genetic programming, the individuals are nonlinear entities of different sizes and shapes (parse trees). Genetic programming applies genetic algorithms to a “population” of programs, typically encoded as tree-structures. Trial programs are evaluated against a “fitness function”. Then the best solutions are selected for modification and re-evaluation. This modification-evaluation cycle is repeated until a “correct” program is produced.
Model trees generalize the concepts of regression trees, which have constant values at their leaves. So, they are analogous to piece-wise linear functions. M5 model tree is a binary decision tree having linear regression function at the terminal nodes, which can predict continuous numerical attributes. Tree-based models are constructed by a divide-and-conquer method.
Results and Discussion: In order to investigate the probability of using different mathematical functions in genetic programming method, three combinations of the functions were used in the current study. The results showed that in the case of predicting river flows with Q, M5 model trees with root mean squared error of 4.7907 in comparison with genetic programming by the best mathematical functions and root mean squared error of 4.8233 had better performances. Obtained results indicated that adding more mathematical functions to the genetic programming and producing more complicated analytical formulations did not have positive effect in reducing prediction error. Unlike the previous observed trend, in case of predicting river flows with Q Q, the genetic programming method with root mean squared error of 3.3501 in comparison with M5 model trees with error of 3.8480 had more satisfied performance. Finally, in the case of predicting river flows with Q, Q,Q, the genetic programming method with root mean squared error of 3.3094 in comparison with M5 model trees with error of 3.5514 presented better predictions. As a result, it can be stated that genetic programming by the best mathematical functions and considering the input parameters of Q,Q,Q, by resulting less root mean squared error and high correlation coefficients had the best performances among others. Also, the results showed that adding more trigonometric functions did not improve the precisions of the predictions.
Conclusion: In this research, the intelligent models such as genetic programming and M5 model trees have been used for prediction of monthly flows of Shaharchay River located in East Azerbaijan, Iran. The obtained results showed that the genetic programming by the best mathematical functions and M5 model trees in case of considering the input parameters of Q,Q,Q, by less root mean squared error had the best performances in river flow predictions. As a conclusion, the genetic programming method by specific mathematical functions including four basic operations, logarithm, power and using input parameters of Q,Q,Q, has been proposed as the best and precise model for predicting Shaharchay River flows.
