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In recent years, the intensification of drought and unsustainable management and use of water resources have caused a significant decline in the water level of the Urmia Lake in the northwest of Iran. This condition has affected the lake, approaching an irreversible point such that many projects have been implemented and are being implemented to save the natural condition of the Urmia Lake, among which the inter-basin water transfer (IBWT) project from the Zab River to the lake could be considered an important project. The main aim of this research is the evaluation of the IBWT project effects on the Gadar destination basin. Simulations of the geometrical properties of the river, including the bed and flow, have been performed, and the land cover and flood map were overlapped in order to specify the areas prone to flood after implementing the IBWT project. The results showed that with the implementation of this project, the discharge of the Gadar River was approximately tripled and the water level of the river rose 1 m above the average. In April, May, and June, about 952.92, 1458.36, and 731.43 ha of land adjacent to the river (floodplain) will be inundated by flood, respectively. Results also indicated that UNESCO's criteria No. 3 ("a comprehensive environmental impact assessment must indicate that the project will not substantially degrade the environmental quality within the area of origin or the area of delivery") and No. 5 ("the net benefits from the transfer must be shared equitably between the area of origin and the area of water delivery") have been violated by implementing this project in the study area. The findings could help the local government and other decision-makers to better understand the effects of the IBWT projects on the physical and hydrodynamic processes of the Gadar River as a destination basin. [ABSTRACT FROM AUTHOR]

We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but "distance to road" was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas. [ABSTRACT FROM AUTHOR]

Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUCtraining = 0.859; AUCvalidation = 0.813); however, the RS ensemble model (AUCtraining = 0.883; AUCvalidation = 0.842) outperformed and outclassed the RF (AUCtraining = 0.871; AUCvalidation = 0.840), and the BA (AUCtraining = 0.865; AUCvalidation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment. [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]