GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.     

The effects of incubation on phosphorus desorption properties, phosphorus availability, and salinity of biosolids-amended soils

Phosphorus (P) desorption characteristics may be altered due to the biosolids decomposition during the incubation period. In our previous work we studied the phosphorus release kinetics in biosolids-amended calcareous soils with no prior incubation. The objectives of this work were (1) to assess the phosphorus desorption behavior in soils as influenced by biosolids after 5 months of incubation and (2) to evaluate the influences of six levels of the biosolids on phosphorus availability and salinity of soil. The results showed that the biosolids addition significantly increased the soil available P and salinity. The P availability and salinity of the soils increased as level of the biosolids application increased. However, there was no significant difference between some application rates for some soils. The results indicated that the incubation can affect the factors controlling the P release rate. Also, the results showed that the soil organic matter negatively affected the P desorption rate in the biosolids-treated soils.     

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.     

A new indicator of elastic spectral shape for the reliable selection of ground motion records

How to select a limited number of ground motion records (GMRs) is an important challenge for the non‐linear analysis of structures. Since epsilon (ε_Sa) is an indicator of spectral shape, which has a significant correlation with the non‐linear response of a structure, the selection of GMRs based on the hazard‐related target ε_Sa is a reasonable approach. In this paper, an alternative indicator of spectral shape is proposed, which results in a more reliable prediction of the non‐linear response for the structures with the natural period of 0.25 to 3.0 s. This new parameter, named eta (η), is a linear combination of ε_Sa and the peak ground velocity epsilon (ε_PGV). It is shown that η, as a non‐linear response predictor, is remarkably more efficient than the well‐known and convenient parameter ε_Sa. The influence of η‐filtration in the collapse analysis of an eight‐story reinforced concrete structure with special moment‐resisting frames was studied. Statistical analysis of the results confirmed that the difference between ε‐filtration and η‐filtration can be very significant at some hazard levels. In the case of this structure, the resulting annual frequency of collapse was found to be lower in the case of η‐based record selection, in comparison with the ε‐based record‐selection approach. Copyright © 2011 John Wiley & Sons, Ltd.     

Land transformation assessment using the integration of remote sensing and GIS techniques: a case study of Al-Anbar Province,Iraq

Human activities and climate changes significantly affect our environment, altering hydrologic cycles. Several environmental, social, political, and economical factors contribute to land transformation as well as environmental changes. This study first identified the most critical factors that affect the environment in Al-Anbar city including population growth, urbanization expansion, bare land expansion, and reduction in vegetation cover. The combination of remote sensing data and fuzzy analytic hierarch process (Fuzzy AHP) enabled exploration of land transformations and environmental changes in the study area during 2001 to 2013 in terms of long and short-term changes. Results of land transformation showed that the major changes in water bodies increased radically (94 %) from the long-term change in 2001 to 2013 because of water policies. In addition, the urban class expanded in two short-term periods (2001–2007 and 2007–2013), representing net changes of 46 and 60 %, respectively. Finally, barren land showed 25 % reduction in the first period because of the huge expansion of water in the lake; a small percentage of growth gain was observed in the second period. Based on the land transformation results, the environmental degradation assessment showed that the study area generally had high level of environmental degradation. The degradation was mostly in the center and the north part of the study area. This study suggested for further studies to include other factors that also responsible for environmental degradation such as water quality and desertification threatening.     

Experimental study of the effect of material and arrangement of electrodes and voltage on the electro-remediation of saturated clays containing chloride and sulfate ions

Electro-remediation process (ERP) is used for dewatering and decontaminating of the soil and improving its mechanical properties. In this study, the effect of voltage, electrode materials, and their arrangement on ERP were studied. The saturated clayey soil was remolded in four boxes at a dry density of 16.2 kN/m³. The process was performed in three stages. The chemical tests at the end of ERP indicated that the absorption of chloride ions in the anode may differ from 89 to 642% due to electro-migration phenomenon. The pH of the soil decreases in anode zone and increases towards the cathode zone. Up to 92% of the sulfate ions were transported and accumulated in the anode. However, in highly contaminated soil, the absorption ratio is 5% in the cathode due to the domination of electro-osmosis process. It was indicated that the electro-osmosis permeability coefficient (K_e) depends on the applied voltage, the soil type, and temperature and its reduction versus time is inversely related to applied voltage. Also, sulfate ions reduced by 74% in the anode due to a change in absorption direction. The results showed that the optimum configuration is linear along with a voltage of 18 V.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.