moslem servati; Hamidreza Momtaz
Abstract
Introduction: Cation Exchange Capacity (CEC) refers to the amount of negative charges available on the soil colloids surface. Clay and organic colloids carry a negative charge on their surfaces. Cations are attracted to the colloids by electrostatic bonds. Therefore, the charge of the soil is zero. For ...
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Introduction: Cation Exchange Capacity (CEC) refers to the amount of negative charges available on the soil colloids surface. Clay and organic colloids carry a negative charge on their surfaces. Cations are attracted to the colloids by electrostatic bonds. Therefore, the charge of the soil is zero. For fertility map planning and commonly indicator of soil condition, CEC is an essential property. CEC is commonly measured on the fine earth fraction (soil particles less than 2 mm in size). CEC could be obtained directly but its measurement is difficult and expensive in the arid and semi-arid regions with high amounts of gypsum and lime. Pedotransfer functions (PTFs) are appropriate tools to estimate CEC from more readily measured properties such as texture, organic carbon, gravel, pH and etc. Regression PTFs, artificial neural networks (ANN), and hybrids technique (HA) could be used to developing pedotransfer functions. The prior research revealed that could provide superior predictive performance when developed ANN model. Furthermore, ANN technique has no comprehensive method to select network learning algorithm and stopping algorithm in the minimum local. Therefore, application of optimization algorithms such as Genetic (GA) and firefly (FA) is necessary. The Purpose of the present study was to evaluate the performance of FA and GA to predict the soil cation exchange capacity by ANN technique based on easily-measured soil properties.
Materials and Methods: 220 soil samples were collected from 39 soil profiles located in Golfaraj (Jolfa) area of East Azarbaijan province. The study site lies from 45° 30ʹ to 45° 53ʹ east longitudes and from 38° 42ʹ to 38° 46ʹ north latitudes. Then, soil samples were air-dried and passed through a 10 mesh sieve for removing gravels and root residues. Soil textural class, organic matter content and CEC were, respectively, determined by hydrometer, Walkley and Black, and bower methods. The artificial neural network (ANN), artificial neural network-Genetic algorithm (ANN-GA) and artificial neural network-Firefly algorithms (ANN-FA) models were applied to predict the soil cation exchange capacity on the basis of the easily-measured soil properties. In ANN-GA and ANN-FA models, soil CEC was estimated via an artificial neural network and were then optimized using a genetic algorithm and firefly algorithm. The Genetic algorithms are commonly used to generate high-quality solutions to be optimized by relying on crossover, mutation and selection operators. The firefly algorithm is modeled by the light attenuation over fireflies’ mutual gravitation, instead of the phenomenon of the fireflies light. The schema of flashes is frequently unique for specific types. The techniques’ results were then compared by four parameter, i.e., correlation coefficient (R2), root mean square errors (RMSE), Nash–Sutcliffe (NES) and Geometric mean error ratio (GMRE).
Results and Discussion: The correlation coefficients of soil characteristic factors with CEC were analyzed through correlation matrix analysis. According to this analysis, the factors which had insignificant influence on the CEC were excluded. The clay, silt, sand and organic matter content were selected as input data. The parameter of the best deployment for MLP network could be used to predict CEC in the studied site. This model comprised 4 neurons (sand, silt, clay percentage and OM) in input layer. The optimum number of neurons in hidden layer was estimated to be 5. Additionally, the most efficient activity function in hidden layer was Axon sigmoid. Results showed that three CEC models performed reasonably well. ANN-FA model had the highest R2 (0.94), lowest RMSE (1.31 Cmol+ Kg-1) and highest Nash–Sutcliffe coefficient (0.53) in training stage and high R2 (0.97), lowest RMSE (1.06 Cmol+ Kg-1) and highest Nash–Sutcliffe coefficient (0.59) in test stage. ANN-GA model had also higher R2 (0.91), lower RMSE (1.77 Cmol+ Kg-1) and higher Nash–Sutcliffe coefficient (0.45) in training stage and higher R2 (0.93), lower RMSE (1.50 Cmol+ Kg-1) and higher Nash–Sutcliffe coefficient (0.48) in test stage indicating good performance of the model as compared with ANN models. The results showed that both ANN and Hybrid algorithm methods performed poorly in extrapolating the minimum and maximum amount of CEC soil properties data. In addition, the comparison of ANN-FA, ANN-GA results with ANN models revealed that ANN-FA was more efficient than the others.
Conclusions: The results of present study illustrated that ANN model can predict CEC with acceptable limits. Therefore, FA and GA algorithms provide superior predictive performance when is combined with ANN model. Firefly algorithm as a new method is utilized to optimize the amount of the weights by minimizing the network error. Final results revealed that this suggested technique improves the modeling performance.
Parisa Farzamnia; Shahram Manafi; Hamidreza Momtaz
Abstract
Introduction: Minerals are one of the main components of soils which play different roles in the soils. Minerals make up about 50% of the volume of most soils. They provide physical support for plants, and create the water- and air-filled pores that make plant growth possible. Mineral weathering releases ...
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Introduction: Minerals are one of the main components of soils which play different roles in the soils. Minerals make up about 50% of the volume of most soils. They provide physical support for plants, and create the water- and air-filled pores that make plant growth possible. Mineral weathering releases plant nutrients which are retained by other minerals through adsorption, cation exchange, and precipitation. Minerals are indicators of the amount of weathering that has taken place, and the presence or absence of particular minerals gives clues to how soils have been formed. The physical and chemical characteristics of soil minerals are important consideration in planning, constructing, and maintaining of buildings, roads, and airports. Clay minerals can be used for understanding of soil formation, optimum management of dry and wet lands and interpretation of paleo environments. Moreover, clay minerals can provide some valuable information such as the origin of sediments, transportation and precipitation of sediments and also some information about intercontinental weathering regimes. Quaternary sediments have occupied most of the agricultural and natural resources of Urima plain and recognition of mineralogical of these soils is essential to optimum and stabile use of these soils. Additionally, caly mineralogical investigation can provide some information about the intensity of weathering processes and climate change in this area. Thus, in this study clay minerals of quaternary sediments in northeast of Urmia and the mechanisms of their formation and also tracing probable climate change in this area were investigated.
Materials and Methods: This study was performed in theUrmia plain in west Azerbaijan Province. The study area is located on quaternary sediments and physiographically, this area is a part of a river alluvial plain with the gentle slope toward Urmia Lake. The mean annual precipitation and temperature of this area are 345.37 mm and 10.83 °C respectively and the soil moisture and temperature regimes are dry xeric and mesic respectively. In this study, eight soil profiles in quaternary sediments were dug and sampled and the morphological, physical, chemical and mineralogical properties were determined using standard methods.
Results and Discussion: According to the results, Illite, smectite, Kaolinite, chlorite, vermiculite and hydroxy interlayer vermiculite (HIV) were the dominant clay minerals in these soils. The origin of illite, chlorite and kaolinite were related to inheritance from parent material. Regarding to the present of some smectite in the parent material of these soils, some of smectites have been inherited from parent material. Nevertheless it seems that, the most of smectites in these soils have pedogenic origin. Based on mineralogical results and trends variation of smectite and illite along studied profiles, we concluded that some of smectites in these soils have been formed from illite transformation. In profiles 4 and 6, regarding to low depth water table and consequently poor drainage, high pH and high values of calcium and magnesium cations, provide suitable conditions for the neoformation of smectit and so, some of smectites have been formed via neoformation from soil solution. In these soils, vermiculites were pedogenic and have been formed during transformation of illite to smectite. Small amounts of hydroxy interlayer vermiculites were present in buried horizons and regarding that they were not present in parent material, it might be because these minerals are pedogenic and have been formed in a past wetter climate. The transformation of illite to smectite in lower horizons needs high moisture and regarding to recent semiarid climate of study area, the suitable amount of moisture for this transformation, especially in lower depths and also in buried horizons, is not present. Thus, it seems the transformation of illite to smectite in lower depths and buried horizons has been taken place in a wetter past climate. So we concluded that smectite and hydroxy interlayer vermiculite are evidences of a wetter past climate in this area.
Conclusion: In this study the origin of smectite in buried horizons was related to transformation of illite. According to high moisture condition which is necessary for the weathering of illite, the occurrence of this process related to more humid climate of the past. Additionally, the presence of hydroxy interlayer vermiculites was related to previously wetter climate as well. So results of this study can be used for recognition of climatic change in the study area.
