JWSS - Journal of Water and Soil Science

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Chenges in the soil characteristics is rather continuously. A method that takes this continuity into account would present a realistic pattern of soil distribution either in taxonomic or geographical space. The fuzzy set theory provides such an approach. In this study, the robustness of fuzzy clustering in soil pattern recognition was evaluated in a subcatchment of western Iran. The clustering carried out on the basis of minimization of an objective function in assigning membership values to each pedon in each fuzzy class. Fuzziness exponent values from 1.15 to 1.5 were used. The following validation of the resulted clusters (classes), optimal number of classes in whole, morphological and particle-size subsets were determined 8, 4, and 5 respectively. Plots of membership values across the landscape indicated class overlap and considerable contiguity. Considering low differentiation of these young soils and the high similarity among their properties, the method indicated a high capacity in recognizing different soil types over the study area. Furthermore, there was relationships between the soil fuzzy classes and landform. Thus, the method is capable in continuous classification, which could be so important in construction of continuous soil maps at low aggregation levels, e. g., pedon.

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Electrical conductivity and acidity of soil are the most important chemical factors of soil for agriculture. The nature of soil is in such a way that its change has a continuous form. The method that can take into account this continuity will be able to show a better picture of change in soil characteristics. Objectives of this research are to investigate the relations between measured electrical conductivity and soil acidity of Qom plain, and clustering, compare the clustering methods, determine the optimum numbers of cluster, and to zone the clusters in the study area. Accordingly, two fuzzy clustering methods FCM and GK, were used for data mining and clustering of 465 measured data. For estimating the appropriateness and comparison of two methods, some criteria including Partition Coefficient, Classification Entropy, Partition Index, Separation Index and Xie and Beni's Index were used. Data mining results showed that the optimum number of clusters for FCM and GK method was 15 and 17, respectively. After investigating the results of clustering and based on the criteria of appropriateness, it was indicated that GK was the best clustering method. According to this method, 295 data from 465 measured samples had more than 40 percent of membership function. So, 9 clusters from 17 clusters had more than 20 members. Then salinity-alkalinity zoning based on GK method to show the clusters distribution better in the study area was prepared. This prepared fuzzy map explained that most of Northwest and west belonged to cluster 1 and eastern parts of study area include belonged to cluster 17. Based on this, salinity-alkalinity and the ensuing soil degradation in east of study area is more likely than the west of it.

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In many water resource projects such as dams, flood control, navigability, river aesthetics, environmental issues and the estimation of suspended load have great importance. The complexity of sediment behavior and mathematical and physical model inability in simulation of sedimentation processes have led to the development of new technologies such as fuzzy logic which has the ability to identify nonlinear relationship between input and output variables. In this study, the application of fuzzy clustering algorithm in estimating the annual amount of sediment was studied. So, the corresponding data of flow and sediment discharge of Valykben station in kasilian basin during 1349-1350 till 1353-1354 period was daily determined. The data was divided in two groups i. e. 75% as training data and 25% for test data. Then, the efficiency of model was obtained by using statistical parameters such as correlation coefficient, nash-satklyf coefficient, mean square error root and variance ratio. The result showed that the classification of data on the annual time scale and use of fuzzy clustering algorithm can estimate 0.49 values of the measured annually suspended sediment transport. Furthermore, on the same scale of classification, i.e. annual scale, this value was obtained 0.19. Thus, using fuzzy clustering algorithm can lead to higher accuracy and reliability than rating curve method, which is suggested for estimating suspended sediment transport.