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The application of multivariate statistical analysis (MSA) in analyzing drainage basin morphometry, in conjunction with seismic events (focal mechanism) and associated tectonic influences, has been discussed less in the framework of existing geomorphometric studies. Hence, in this study, an integrated approach was designed combining the geomorphometric analysis with MSA, focal mechanism and associated structural lineaments (using a remote sensing and GIS framework) to address the role of active tectonism on the drainage network characteristics in Qorveh-Dehgolan basin located in western part of Iran (Kurdistan province). Eight watersheds were delineated from burnt ASTER DEM using SWAT model and categorized into high-altitude watersheds and low-altitude watersheds. Four-sets of the drainage network were extracted from satellite imageries (Landsat 7 and 8) over a period of 17 years (2000–2017) to monitor the temporal stream network behavior. Overall, nineteen morphometric parameters were calculated (and categorized into linear, aerial, and relief aspects) exhibiting significant changes in the drainage network from 2000 to 2017 (the stream order declined from 5th to 4th order). The higher-altitude watersheds showed various anomalies in the calculated indices (indicating the potential influence of tectonic activity), while the lower altitude watersheds did not exhibit any specific anomalies. In addition, higher concentration of longer lineaments was observed in areas where the relatively resistant, fractured bedrock lithology (meta-volcanic and meta-sedimentary rocks) provided favorable conditions for origin and propagation of the lineaments. Further analysis of focal mechanism solutions (FMS, beachball diagrams) showed that the study area is primarily influenced by moderate earthquake events (3 ≤ Mw ≤ 6) and is influenced by a complex fault mechanism (primarily normal faults and strike slip faults). The morphometric parameters were further subjected to the factor analysis (principal axis factoring using Varimax rotation transformation) resulting in derivation of "factor of basin magnitude", "factor of steepness" and "factor of texture" as three principal factors impacting the drainage network. Finally, it was concluded that "factor of steepness" played a decisive role in defining hydrology of higher-altitude watersheds (an indication of tectonic influences in these watersheds) and thereby defining the overall hydrology of the study area as well. Overall, this study highlighted the significant role of the tectonic activity associated with structural lineaments and the seismic events in defining the nature and behavior of the drainage network and certain anomalies caused in the basin hydrology under these structural controls. [ABSTRACT FROM AUTHOR]

Identifying areas with high groundwater potential is important for groundwater resources management. The main objective of this study is to propose a novel classifier ensemble method, namely Random Forest Classifier based on Random Subspace Ensemble (RS-RF), for groundwater potential mapping (GWPM) in Qorveh-Dehgolan plain, Kurdistan province, Iran. A total of 12 conditioning factors (slope, aspect, elevation, curvature, stream power index (SPI), topographic wetness index (TWI), rainfall, lithology, land use, normalized difference vegetation index (NDVI), fault density, and river density) were selected for groundwater modeling. The least square support vector machine (LSSVM) feature selection method with a 10-fold cross-validation technique was used to validate the predictive capability of these conditioning factors for training the models. The performance of the RS-RF model was validated using the area under receiver operating characteristic curve (AUROC), success and prediction rate curves, kappa index, and several statistical index-based measures. In addition, Friedman and Wilcoxon signed-rank tests were used to assess statistically significant level among the new model with the state-of-the-art soft computing benchmark models, such as random forest (RF), logistic regression (LR) and naïve Bayes (NB). Results showed that the new hybrid model of RS-RF had a very high predictive capability for groundwater potential mapping and exhibited the best performance among other benchmark models (LR, RF, and NB). Results of the present study might be useful to water managers to make proper decisions on the optimal use of groundwater resources for future planning in the critical study area. [ABSTRACT FROM AUTHOR]

This study describes the application of logistic regression to rock-fall susceptibility mapping along 11 km of a mountainous road on the Salavat Abad saddle, in southwest Kurdistan, Iran. To determine the factors influencing rock-falls, data layers of slope degree, slope aspect, slope curvature, elevation, distance to road, distance to fault, lithology, and land use were analyzed by logistic regression analysis. The results are shown as rock-fall susceptibility maps. A spatial database, which included 68 sites (34 rock-fall point cells with value of 1 and 34 no rock-fall point cells with value of 0) was developed and analyzed using a Geographic Information System, GIS. The results are shown as four classes of rock-fall susceptibility. In this study, distance to fault, lithology, slope curvature, slope degree, and distance to road were found to be the most important factors affecting rock-fall. It was concluded that about 76 % of the study area can be classified as having moderate and high susceptibility classes. Rock-fall point cells were used to verify results of the rock-fall susceptibility map using success curve rate and the area under the curve. The verification results showed that the area under the curve for rock-fall susceptibility map is 77.57 %. The results from this study demonstrated that the use of a logistic regression model within a GIS framework is useful and suitable for rock-fall susceptibility mapping. The rock-fall susceptibility map can be used to reduce susceptibility associated with rock-fall. [ABSTRACT FROM AUTHOR]

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811). [ABSTRACT FROM AUTHOR]