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Showing 22 results for Artificial Neural Network
Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models
B. Shahinejad, A. Parsaei, H. Yonesi, Z. Shamsi, A. Arshia,
Volume 26, Issue 4 (12-2022)
Abstract
In the present study, the flow rate in flues containing lateral semi-cylinders (SMBF) was simulated and estimated under free and submerged conditions using back vector machine models (SVM), spin multivariate adaptive regression (MARS), and multilayer artificial neural network (MLPNN) model. In free flow mode, the dimensionless parameters extracted from the dimensional analysis include the ratio of upstream flow to throat width and contraction ratio (throat width to channel width), and in the submerged state, in addition to these two parameters, the depth-to-throat width, and bottom-depth parameters upstream depth were used as input and the two-dimensional form of flow rate was used as the output of the models. The results showed that in free flow mode in the validation stage, the MARS model with statistical indices of R2 = 0.985, RMSE = 0.008, MAPE = 0.87%, and the SVM model with statistical indices of  R2 = 0.971, RMSE = 0.0012, MAPE =1.376%, and MLPNN model with statistical indices of R2 = 0.973,  RMSE = 0.011, MAPE = 1.304% have modeled and predicted the flow rate. In the submerged state, the statistical indices of the developed MARS model were R2 = 0.978, RMSE = 0.018, MAPE = 3.6%, and the statistical indices of the SVM model were R2 = 0.988, RMSE = 0.014, 2%. MAPE = 4, and the statistical indicators of the MLPNN model were R2 = 0.966, RMSE = 0.022, and MAPE = 5.7%. In the development of SVM and MLPNN models, radial kernel and hyperbolic tangent functions were used, respectively.

Digital Mapping of Soil Equivalent Calcium Carbonate Using Landsat 8 Satellite Images and Environmental Data by Machine Learning Models in Badr Watershed, Kurdistan Province
M. Zarinibahador,
Volume 29, Issue 1 (4-2025)
Abstract
The calcium carbonate equivalent (CCE) in soil is one of the most important soil properties. Predicting the amount of calcium carbonate equivalent in soil is essential for sustainable soil fertility management. The present study aimed to digitally map calcium carbonate equivalent using auxiliary environmental variables, Landsat 8 satellite images, and predictive models and to present the best models in the Badr watershed in the south of Qorveh district. In the first phase, a geomorphologic map was created using a geologic map and based on the ZINC method in a geographic information system environment. In the second phase, the location of 125 survey profiles was determined using the Latin hypercube technique, and the calcium carbonate equivalent of the soil horizons was measured by acid titration. The auxiliary variables included derivatives of the digital elevation model, remote sensing indices from the Landsat 8 satellite, and a geopedological map. The principal component analysis (PCA) method was used to select suitable auxiliary variables. In the third phase, the modeling was carried out, digital maps of the soil classes and properties were created, and the models were evaluated. Two different cases were investigated in this study to estimate the calcium carbonate equivalent of the soil. In the first case, artificial neural network models, decision tree analysis, random forest, and the K-nearest neighbor model were used for prediction. The multiple linear regression model was also used to combine the results of the models. Among the models used to predict the equivalent amount of calcium carbonate using the 10-fold cross-validation method, the multiple linear regression (MLR) model had the highest prediction accuracy with a coefficient of determination of 0.796 and a mean square error of 6.514. In the 5-fold cross-validation method, the K-nearest neighbor (KNN) model had the highest predictive accuracy with a coefficient of determination of 0.9845 and a root mean square error of 2.1258. Due to the spatial nature of the 10-fold cross-validation method, the use of this method is preferable to the 5-fold cross-validation method. In addition, the most important auxiliary variables in order of importance to predict the calcium carbonate equivalent in soil were the carbonate index, slope direction, geomorphology, the base level of the catchment network, and the slope of the catchment.

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