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A comprehensive integrated framework was designed to categorize individual streams and watersheds based on their temporal variation, and to determine the key drivers controlling watershed hydrology in the Qorveh-Dehgolan Basin. Four stream networks were extracted from multi-temporal Pan-Sharpened Landsat 7 and Landsat 8 imageries. Nineteen geomorphometric indices are measured and classified into the linear aspect, aerial aspect, and relief aspect. The results revealed a significant decline in stream networks from 2000 to 2017, reducing basin order. The factor analysis identified three principal factors including 'factor of basin magnitude' (affecting low-altitude watersheds), 'factor of steepness' (influencing high-altitude watersheds), and 'factor of texture'. The focal mechanism highlighted the complex fault mechanisms in the study area. Finally, RadViz clustering showed that for the lower order streams in the high-altitudes watersheds, stream width showed higher temporal variability in comparison to other parameters. In contrast, flow accumulation showed high variance in higher-order streams. [ABSTRACT FROM AUTHOR]

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]

Landslide is a natural hazard which causes huge loss of properties and human life in many places of the world. Mapping of landslide susceptibility is an important task for preventing and combating the landslides problems. Main objective of this study is to use different artificial intelligence methods namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), and reduced error-pruning tree (REPT) in the development of models for landslide susceptibility mapping of Muong Lay district of Vietnam. In total data of 217 landslide locations of the study area was used for the development and evaluation of the models. Nine landslide-conditioning factors were used for generating the datasets for training and validating the models. Results show that the SVM outperformed all other methods namely ANN, LR and REPT. Thus, it can be suggested that the SVM method is more useful in developing accurate and robust landslide prediction model. [ABSTRACT FROM AUTHOR]

In this study, morphotectonic analysis was performed to investigate the influence of tectonic activity on drainage network in Qorve-Dehgolan basin, located in Sanandaj - Sirjan Zone (SSZ). Eight watersheds were delineated using burnt DEM and eleven morphotectonic indices including stream length gradient index (SL); drainage basin asymmetry (Af); hypsometric curve (HC); hypsometric integral (HI); roughness (R); elongation ratio (Re); mountain front sinuosity (Smf); basin shape (Bs); curvature of surface (C), basin slope (Sb) and river sinuosity index (K) were calculated. The higher altitude watersheds specifically showed various anomalies in the calculated indices. These indices when subjected to factor analysis (using Varimax rotation) resulted in extraction of "steepness factor" and "asymmetric factor" as two principal factors influencing the drainage network. The integrated analysis revealed that steepness factor, indicating ongoing tectonism, plays a significant role in the hydrology of higher altitude watersheds, whereas asymmetric factor is more dominant in lower altitude watersheds. [ABSTRACT FROM AUTHOR]

In the current study, a combination of automated lineament extraction from Landsat 8 satellite imagery and 3D interactive visual interpretation (using DEM) alongside image processing techniques (Gram-Schmidt pan-sharpening, convolution directional filter) was carried out to investigate expression and influence of tectonic activity on drainage morphometry within Qorveh-Dehgolan basin. The watersheds, derived from burned DEM using SWAT, were categorized into high and low-altitude watersheds depending on the associated relief. Analysis of spatial relationship of lineament density with drainage density and basin slope revealed that structural lineaments have a decisive control over drainage density distribution particularly at higher basin slopes of certain watersheds. These high-altitude watersheds, associated with more-resistant lithology, were also found to cover higher percentage of total lineaments compared to the lower-altitude watersheds associated with less-resistant lithology. The analysis presented significant insights into the influence of tectonic activity on drainage network in a relatively unexplored area and provided vital baseline information for future investigations. [ABSTRACT FROM AUTHOR]

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC = 0.930). However, RS model (AUROC = 0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC = 0.972), MB (AUROC = 0.970) and AB (AUROC = 0.957) models, respectively. [ABSTRACT FROM AUTHOR]

In this study, the main goal is to compare the predictive capability of Support Vector Machines (SVM) with four Bayesian algorithms namely Naïve Bayes Tree (NBT), Bayes network (BN), Naïve Bayes (NB), Decision Table Naïve Bayes (DTNB) for identifying landslide susceptibility zones in Pauri Garhwal district (India). First, landslide inventory map was built using 1295 historical landslide data, then in total sixteen influencing factors were selected and tested for landslide susceptibility modelling. Performance of the model was evaluated and compared using Statistical based index methods, Area under the Receiver Operating Characteristic (ROC) curve named AUC, and Chi-square method. Analysis results show that that the SVM has the highest prediction capability, followed by the NBT, DTNBT, BN and NB, respectively. Thus, this study confirms that the SVM is one of the benchmark models for the assessment of susceptibility of landslides. [ABSTRACT FROM AUTHOR]

Karst springs of the Zagros Mountains contribute a significant amount to agricultural and human water demands of western and south-western Iran. For an adequate management of available water resources in semi-arid and arid regions, sufficient hydrological monitoring is needed, and hydro-chemical and isotope hydrological data provide important additional information. About 350 water samples were collected from precipitation, river water, and karst springs of the upper part of the Karkheh River Basin (20,895 km2) located between 33°35′and 34°55′North and 46°22′and 49°10′East with elevations ranging from 928 to 3563 m above sea level. Sampling was conducted in monthly time resolution from August 2011 to July 2012. All samples were analysed for hydro-chemical parameters (pH, electrical conductivity, and major ions) and stable isotopes (deuterium, oxygen-18). Isotope values of precipitation indicate a local meteoric water line (Zagros MWL δ2H=6.8 δ18O+10.1;R2=0.99) situated between the Mediterranean MWL and Global MWL. Spring and river water isotope values vary between−7.1 and−4.1 ‰, and−38 and−25 ‰ for δ18O and δ2H, respectively, responding to winter snowmelt and evaporation. This work implements stable isotopes and hydro-chemical information of springs and river water to understand hydrological and hydro-geological interrelations in karstic semi-arid areas and helps to improve the current water resources management practices of western Iran. [ABSTRACT FROM AUTHOR]