S. Nazari; M. Rostaminia; shamsollah Ayoubi; A. Rahmani; S.R. Mousavi
Abstract
Abstract Background and objectives: High-accuracy of soil maps is a powerful tool for achieving land sustainability in agricultural and natural resources. The present study was conducted in Vargar lands of Abdanan city related to Ilam province for digital mapping of soil classes at two taxonomic level ...
Read More
Abstract Background and objectives: High-accuracy of soil maps is a powerful tool for achieving land sustainability in agricultural and natural resources. The present study was conducted in Vargar lands of Abdanan city related to Ilam province for digital mapping of soil classes at two taxonomic level from subgroup up to family by random forest (RF) and fuzzy logic models. Materials and methods: Study area with 1027 hectare have 628.6 mm and 22.6 C° mean annual precipitation and temperature respectively. Three major physiographic units included Hilland, Piedmont plain and Alluvial plain were observed. Soil moisture and temperature regimes are ustic and hyperthermic calculated based on Newhall model in JNSM 6.1 version software. A total of 44 soil profile observation with random sampling pattern was determined based on standardized soil surveys then digging, description and after sampling from all genetic horizons then soil samples were transferred to laboratory. Finally, all of soil profiles were classified based on soil taxonomy system (2014) up to family level. Geomorphometric covariates as a representative of soil forming factors were prepared from digital elevation model (ALOS PALSAR Satellite,2011) with 12.5 m resolution in SAGA GIS 7.4 version software. Three feature selection approaches included Boruta, Variance inflation factors (VIF) and Mean decrease accuracy (MDA) with two Random forest (RF) and Fuzzy logic data mining algorithms were applied for relating soil-landscape relationship by using “randomforest”, “caret” packages in R 3.5.1 and SoLIM solution version 2015 software. Sample based project used for predicting soil classes in Fuzzy logic modeling process. In totally observation profile split into two data set included 80 percent (n=36) for calibrating and 20 percent for validating (n=8) based on bootstraps sampling algorithm random forest. Internal validation of random forest algorithm was done based on out of bag error percentage (OOB%). The best model performance was determined based on overall accuracy (OA) and kappa index, also for each individual class user accuracy (UA) and producer accuracy (PA) were applied. Results: The results shown that from number of 40 geomorphometrics covariates, six covariates included Terrain classification index for lowlands, Annual insolation, Topographic position Index, Upslope curvature, Real surface area and Terrain surface convexity were selected by MDA as the best environmental covariates. Also, RF-MDA method with overall accuracy 84% and Kappa index 0.56 had the best performance compared to other methods (RF_VIF, RF-BO, Fuzzy-MDA) in subgroup level with 58, 55, 50 and 0.3, 0.67 and 0.18 respectively. Out of bag error results (%OOB) for RF-MDA, RF-VIF and RF-Boruta were obtained that 72.42%, 67.86% and 82.76% for subgroup level and 93.10%, 93.10% and 86.21% for family level respectively. while there was little difference between the accuracy of the method at the family taxonomic level and performed similar results in modeling of soil classes process. The results of the fuzzy approach showed that the kappa index values and overall accuracy of this method were similar to the other three scenarios and there was a slight difference between the accuracy of the results at the soil family level. In the fuzzy method, it was observed that the kappa and overall accuracy values at the subgroup level were lower than the other scenarios. Fuzzy approaches in contrasted to RF modeling prevented continues spatial variability by generating of fuzzy maps for each of soil class in the landscape. These results indicate that the random forest method is superior to the fuzzy method in family class mapping and soil subgroups. Based on MDA sensitivity analysis index, similarly, three geomorphometrics covariate included Terrain surface convexity (convexity), Terrain classification index for lowlands (TCI_Low) and Real surface area (Surface_Ar) had highest importance for predicting soil classes at two taxonomic level. With regarded to final soil predicted maps area, two classes (Fine-silty, carbonatic, hyperthermic Typic Haplustepts) and Typic Calciustolls with 32.70% and 48.90% and (Fine-silty, carbonatic, hyperthermic Typic Calciustolls) and Typic Haplustepts with 0.18% and 1.85% had the highest and lowest content at family and subgroup maps respectively. Conclusion: In general, using different variable selection approaches in situations where soil classes have a relatively imbalanced abundance can increase the accuracy of digital mapping in soil studies. Increasing the number of field observations and the use of other environmental variables affecting soil formation can also be used for gradating in prediction low-accuracy soil classes.
Fatemeh Rahmati; Ardavan Kamali
Abstract
Introduction: Land suitability evaluation is a process to examine the degree of land fitness for specific utilization and also makes it possible to estimate land productivity potential. In 1976, FAO provided a general framework for land suitability classification. It has not been proposed a specific ...
Read More
Introduction: Land suitability evaluation is a process to examine the degree of land fitness for specific utilization and also makes it possible to estimate land productivity potential. In 1976, FAO provided a general framework for land suitability classification. It has not been proposed a specific method to perform this classification in the framework. In later years, a collection of methods was presented based on the FAO framework. In parametric method, different land suitability aspects are defined as completely discrete groups and are separated from each other by distinguished and consistent ranges. Therefore, land units that have moderate suitability can only choose one of the characteristics of predefined classes of land suitability. Fuzzy logic is an extension of Boolean logic by LotfiZadeh in 1965 based on the mathematical theory of fuzzy sets, which is a generalization of the classical set theory. By introducing the notion of degree in the verification of a condition, fuzzy method enables a condition to be in a state other than true or false, as well as provides a very valuable flexibility for reasoning, which makes it possible to take into account inaccuracies and uncertainties. One advantage of fuzzy logic in order to formalize human reasoning is that the rules are set in natural language. In evaluation method based on fuzzy logic, the weights are used for land characteristics. The objective of this study was to compare four methods of weight calculation in the fuzzy logic to predict the yield of wheat in the study area covering 1500 ha in Kian town in Shahrekord (Chahrmahal and Bakhtiari province), Iran.
Materials and Methods: In such investigations, climatic factors, and soil physical and chemical characteristics are studied. This investigation involves several studies including a lab study, and qualitative and quantitative land suitability evaluation with fuzzy logic for wheat. Factors affecting the wheat production consist of climatic conditions like mean, maximum and minimum air temperatures during growing period as well as edaphologic properties like EC, pH, ESP, percent of clay, silt, sand, gravel, gypsum and CaCO3 content. Climatic data collected from the Shahrekord synoptic station were used to assess climatic land suitability for wheat. Qualitative land suitability evaluation was carried out using the fuzzy approach. Potential yield was calculated using the method proposed by FAO. Using MATLAB software, qualitative and quantitative land evaluation were classified based on fuzzy logic approach. In fuzzy method, climatic factors are used to achieve climatic index. Clay and sand percent were applied to calculate soil texture. To determine the membership degrees,bell membership functions were used. Parameters of function shapes were transformed to equations with variable coefficients and the best coefficients were eventually chosen based on the model determination coefficient. In evaluation method based on fuzzy logic, the weights are used for land characteristics. In fuzzy logic method, weights were calculated by four methods. These methods consist of neural network using 1 neuron and 4 neurons, multivariate and Partial Least Squares (PLS) regressions. Comparison of the coefficient of determination results of multivariate regression and RMSE is carried out between observed and predicted yield. Weight calculations were conducted by using MINITAB software to PLS and multivariate regression. Also, Neurosolution 5 was used for weight calculation based on neural network.
Results and Discussion: The calculated weights were differed by using the four applied methods. In all methods, the maximum weight was related to gravel, and minimum weight was related to clay. The results of land index and predicted yield calculation were different in some points (3, 6, 7, 13, 14, 19, and 21) for four methods. The coefficient of determination of calculated weights were 0.595, 0.56, 0.6 and 0.56 for neural network, 1 neuron, 4 neurons, multivariate regression and PLS and RMSE values in these methods were 6.38, 6.4, 6.38 and 6.38 Ton/ha, respectively. The correlation coefficient between the observed and predicted yield indicated the partially appropriate selection of the factors and evaluation approach.
Conclusion: The results of weight calculation were not showed significant difference in three methods (neural network, PLS, regression). The predicted yield was somewhat closer to the observed yield when 1 neuron was introduced to the neural network than 4 neurons. The maximum coefficient of determination as well as the minimum RMSE was achieved for weights calculated by multivariate regression. Because the method is almost accurate and easy to use, it is recommended in this study. The coefficient of determination generally became low because different traditional management practices were carried out in the study area. Finally, in regard to achieved results about the used methods, it is suggested to take into account the management factors in land suitability processes and compare the other weight calculated methods in land suitability evaluation based on fuzzy logic.
S.A. Ghassemi; Sh. Danesh
Abstract
In this research a fuzzy decision making model was presented to assess and rank various water desalination methods and ultimately select the best alternative. The desalination alternatives which were investigated included Reverse Osmosis, Electrodialysis, Multi-Stage Flash Distillation, Multi-Effect ...
Read More
In this research a fuzzy decision making model was presented to assess and rank various water desalination methods and ultimately select the best alternative. The desalination alternatives which were investigated included Reverse Osmosis, Electrodialysis, Multi-Stage Flash Distillation, Multi-Effect Distillation, Vapor Compression and Ion Exchange. The model was carried out in three steps: problem definition, fuzzy computations and ranking of alternatives. The hierarchy structure used for problem definition included 5 levels of: goal, main criteria, sub-criteria, factors, and desalination alternatives. The criteria, sub-criteria and factors and the relative importance of each were determined based on the experts' opinions and the literature results. In the next step, by using Chang's extent analysis, various desalination alternatives were evaluated on the basis of the selected criteria, sub-criteria and factors. For assessment of accuracy and its practical application, the model was used in a case study concerning quality management of the brackish water from a number of wells located in the City of Torbat-e-Heydaryieh. The results of the research indicated that the Electrodialysis process, with the final weight of 0.255, was the best method of desalination for the investigated wells. The sensitivity analysis also showed that the fuzzy model has a low degree of sensitivity in regard to the changes in criteria weights, meaning that the results are adequately reliable. The results furthermore pointed out that the fuzzy analytical hierarchy process can be used as an efficient tool for systematic decision making in the area of qualitative water resource management.
