Spatial prediction of soil erosion susceptibility: an evaluation of the maximum entropy model

Soil erosion is considered as the most widespread form of soil degradation which causes serious environmental problems. This study investigates the performance of the maximum entropy (ME) in mapping rill erosion susceptibility in the Golgol watershed, Ilam province, Iran. To this end, ten rill erosion conditioning factors were selected to be employed in the modelling process based on an investigation of the literature. These layers are: elevation, slope percent, aspect, stream power index, topographic wetness index, distance from streams, plan curvature, lithology, land use, and soil. Then, a training dataset of rill erosion locations was used for modelling this phenomenon. The area under receiver operating characteristics curve was used for evaluating the performance of the ME model. In addition, Modified Pacific South-West Inter Agency Committee (MPSIAC) framework was applied and sediment yield was determined for different hydrological units in the study area. At last, Jackknife test was implemented to show the contribution of the factors in the modelling process. The results depicted that area under ROC curve for training and validation datasets were 0.867, and 0.794, respectively. Therefore, this conclusion can be achieved that ME worked well and could be a good tool for generating rill erosion susceptibility maps and its output could be employed for soil conservation in similar areas.     

GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique

Toroud Watershed in Semnan Province, Iran is a prone area to gully erosion that causes to soil loss and land degradation. To consider the gully erosion, a comprehensive map of gully erosion susceptibility is required as useful tool for decreasing losses of soil. The purpose of this research is to generate a reliable gully erosion susceptibility map (GESM) using GIS-based models including frequency ratio (FR), weights-of-evidence (WofE), index of entropy (IOE), and their comparison to an expert knowledge-based technique, namely, Analytic Hierarchy Process (AHP). At first, 80 gully locations were identified by extensive field surveys and Google Earth images. Then, 56 (70%) gully locations were randomly selected for modeling process, and the remaining 26 (30%) gully locations were used for validation of four models. For considering geo-environmental factors, VIF and tolerance indices are used and among 18 factors, 13 factors including elevation, slope degree, slope aspect, plan curvature, distance from river, drainage density, distance from road, lithology, land use/land cover, topography wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), and slope–length (LS) were selected for modeling aims. After preparing GESMs through the mentioned models, final maps divided into five classes including very low, low, moderate, high, and very high susceptibility. The receiver operating characteristic (ROC) curve and the seed cell area index (SCAI) as two validation techniques applied for assessment of the built models. The results showed that the AUC (area under the curve) in training data are 0.973 (97.3%), 0.912 (91.2%), 0.939 (93.9%), and 0.926 (92.6%) for AHP, FR, IOE, and WofE models, respectively. In contrast, the prediction rates (validating data) were 0.954 (95.4%), 0.917 (91.7), 0.925 (92.5%), and 0.921 (92.1%) for above models, respectively. Results of AUC indicated that four model have excellent accuracy in prediction of prone areas to gully erosion. In addition, the SCAI values showed that the produced maps are generally reasonable, because the high and very high susceptibility classes had very low SCAI values. The results of this research can be used in soil conservation plans in the study area.     

Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis

Accurate laboratory measurement of geo-engineering properties of intact rock including uniaxial compressive strength (UCS) and modulus of elasticity (E) involves high costs and a substantial amount of time. For this reason, it is of great necessity to develop some relationships and models for estimating these parameters in rock engineering. The present study was conducted to forecast UCS and E in the sedimentary rocks using artificial neural networks (ANNs) and multivariable regression analysis (MLR). For this purpose, a total of 196 rock samples from four rock types (i.e., sandstone, conglomerate, limestone, and marl) were cored and subjected to comprehensive laboratory tests. To develop the predictive models, physical properties of studied rocks such as P wave velocity (V_p), dry density (γ_d), porosity, and water absorption (A_b) were considered as model inputs, while UCS and E were the output parameters. We evaluated the performance of MLR and ANN models by calculating correlation coefficient (R), mean absolute error (MAE), and root-mean-square error (RMSE) indices. The comparison of the obtained results revealed that ANN outperforms MLR when predicting the UCS and E.     

Landslide Susceptibility Mapping along Bhalubang–Shiwapur Area of Mid-Western Nepal Using Frequency Ratio and Conditional Probability Models

Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang–Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and to predict future occurrence of the landslides, landslide susceptibility mapping(LSM) of this region was carried out using frequency ratio(FR) and weights-of-evidence(W of E) models. These models are easy to apply and give good results. For this, landslide inventory map of the area was prepared based on the aerial photo interpretation, from previously published/unpublished reposts, and detailed field survey using GPS. About 332 landslides were identified and mapped, among which 226(70%) were randomly selected for model training and the remaining 106(30%) were used for validation purpose. A spatial database was constructed from topographic, geological, and land cover maps. The reclassified maps based on the weight values of frequency ratio and weights-of-evidence were applied to get final susceptibility maps. The resultant landslide susceptibility maps were verified andcompared with the training data, as well as with the validation data. From the analysis, it is seen that both the models were equally capable of predicting landslide susceptibility of the region(W of E model(success rate = 83.39%, prediction rate = 79.59%); FR model(success rate = 83.31%, prediction rate = 78.58%)). In addition, it was observed that the distance from highway and lithology, followed by distance from drainage, slope curvature, and slope gradient played major role in the formation of landsides. The landslide susceptibility maps thus produced can serve as basic tools for planners and engineers to carry out further development works in this landslide prone area.     

Interaction of a Fresh Water Lake and a Karstic Spring via a Syncline Fold

Kaftar Lake is a high‐altitude fresh water lake located in High Zagros, south of Iran. Despite the high annual evaporation to precipitation ratio in the area, lake water electrical conductivity is usually lower than 1000 µS/cm, this may be due to high seepage from the floor of the lake. Therefore, the hypothesis of possible underground connections between Namdan Basin, where the lake is located, and the surrounding basins with lower elevation (Aspas and Dehbid Basins) was investigated. Hydrogeology, hydrochemistry, and stable isotopes data of the lake and surrounding basins along with the lake water balance study were applied to test the hypothesis. Results indicate that Kaftar Lake has no connection with Aspas Basin in south, but it is hydraulically connected to Dehbid Basin. In Dehbid Basin, “Ghasr_e_Yaghoob spring” (average discharge ?1200 L/s) emerges from a small outcrop (about 0.8 km²) of Daryan limestone Formation, where this outcrop is much smaller than the required recharge area for such average discharge rate. The study shows that this spring is recharged by Kaftar Lake and Namdan Basin aquifer, through Daryan Formation of Gandboee Syncline located to the northern part of the lake.     

Application of randomized sampling schemes to curvelet‐based sparsity‐promoting seismic data recovery

Reconstruction of seismic data is routinely used to improve the quality and resolution of seismic data from incomplete acquired seismic recordings. Curvelet‐based Recovery by Sparsity‐promoting Inversion, adapted from the recently‐developed theory of compressive sensing, is one such kind of reconstruction, especially good for recovery of undersampled seismic data. Like traditional Fourier‐based methods, it performs best when used in conjunction with randomized subsampling, which converts aliases from the usual regular periodic subsampling into easy‐to‐eliminate noise. By virtue of its ability to control gap size, along with the random and irregular nature of its sampling pattern, jittered (sub)sampling is one proven method that has been used successfully for the determination of geophone positions along a seismic line. In this paper, we extend jittered sampling to two‐dimensional acquisition design, a more difficult problem, with both underlying Cartesian and hexagonal grids. We also study what we term separable and non‐separable two‐dimensional jittered samplings. We find hexagonal jittered sampling performs better than Cartesian jittered sampling, while fully non‐separable jittered sampling performs better than separable jittered sampling. Two other 2D randomized sampling methods, Poisson Disk sampling and Farthest Point sampling, both known to possess blue‐noise spectra, are also shown to perform well.     

Geochemistry of Carboniferous Sandstones (Sardar Formation), East‐Central Iran: Implication for Provenance and Tectonic Setting

Geochemical analysis of sandstones from the Sardar Formation (from two stratigraphic successions) in east-central Iran were used for identification of geochemical characterization of sandstones, provenance and tectonic setting. Sandstones in the two lithostratigraphic successions have similar chemical compositions suggesting a common provenance. Bulk-rock geochemistry analysis of Carboniferous sandstones from Sardar Formation indicates that they are mainly quartz dominated and are classified as quartzarenites, sublitharenites and subarkoses, derived from acid igneous to intermediate igneous rocks. Discrimination function analysis indicates that the sandstones of Sardar Formation were derived from quartzose sedimentary provenance in a recycled orogenic setting. Also, major and trace elements in sandstones of Sardar Formation (e.g., K2O/Na2O vs. SiO2) indicate deposition in a stable passive continental margin (PM). Chemical index of alteration (CIA) for these rocks (> 65%) suggests a moderate to relatively high degree of weathering in the source area.     

Seismicity patterns associated with the September 10th, 2008 Qeshm earthquake, South Iran

The b value of the Gutenberg-Richter relation and the standard deviate, Z, were calculated to investigate the temporal and spatial variations in seismicity patterns associated with the September 10th, 2008 (Mw?=?6.1) Qeshm earthquake. The temporal variations of b value illustrate a distinct dramatic drop preceding the Qeshm earthquake, and the spatial changes in b value highlight a zone with an abnormally low b value around the epicenter of this event. The cumulative number and Z value as a function of time show a precursory seismic quiescence preceding the 2008 Qeshm earthquake that observed for 1?year in a circle with R?=?50?km around its epicenter. The spatial distribution map of the standard deviate, Z, also exhibits an obvious precursory seismic quiescence region before the 2008 Qeshm event around the epicenter of this event. Interestingly, the precursory seismic quiescence region is approximately consistent with low b value anomaly region, and both have E–W to NE–SW trend. These two precursory anomalies took place in relatively large regions, which were possibly relevant to the preparation zone of the 2008 Qeshm event.     

Site selection for managed aquifer recharge using fuzzy rules: integrating geographical information system (GIS) tools and multi-criteria decision making

One of the most important water-resources management strategies for arid lands is managed aquifer recharge (MAR). In establishing a MAR scheme, site selection is the prime prerequisite that can be assisted by geographic information system (GIS) tools. One of the most important uncertainties in the site-selection process using GIS is finite ranges or intervals resulting from data classification. In order to reduce these uncertainties, a novel method has been developed involving the integration of multi-criteria decision making (MCDM), GIS, and a fuzzy inference system (FIS). The Shemil-Ashkara plain in the Hormozgan Province of Iran was selected as the case study; slope, geology, groundwater depth, potential for runoff, land use, and groundwater electrical conductivity have been considered as site-selection factors. By defining fuzzy membership functions for the input layers and the output layer, and by constructing fuzzy rules, a FIS has been developed. Comparison of the results produced by the proposed method and the traditional simple additive weighted (SAW) method shows that the proposed method yields more precise results. In conclusion, fuzzy-set theory can be an effective method to overcome associated uncertainties in classification of geographic information data.     

A robust feldspar luminescence dating method for Middle and Late Pleistocene sediments

Luminescence dating is used extensively to provide absolute chronologies for Late Pleistocene sediments. Nowadays, most optical dates are based on quartz optically stimulated luminescence (OSL). However, the application of this signal is usually limited to the last ～100 ka because of saturation of the quartz luminescence signal with dose. In contrast, the feldspar infrared stimulated luminescence (IRSL) dose–response curve grows to much higher doses; this has the potential to extend the datable age range by a factor of 4–5 compared with quartz OSL. However, it has been known for several decades that this IRSL signal is unstable, and this instability often gives rise to significant age underestimation. Here we test against independent age control the recently developed feldspar post‐IR IRSL approach to the dating of sediments, which appears to avoid signal instability. A physical model explaining our observations is discussed, and the method is shown to be accurate back to 600 ka. The post‐IR IRSL signal is reduced by exposure to daylight more slowly than that from quartz and low‐temperature IRSL, preventing its general application to young (e.g. Holocene) sediments. Nevertheless, this new approach is widely applicable (feldspar of appropriate luminescence behaviour is even more ubiquitous than quartz). These characteristics make this a method of great importance for the dating of Middle and Late Pleistocene deposits.