Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin,Egypt

Natural Hazards - Floods represent catastrophic environmental hazards that have a significant impact on the environment and human life and their activities. Environmental and water management in...     

Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).     

Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study was to investigate the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. The landslide conditioning factors considered for the study area were slope gradient, slope aspect, altitude, lithology, land use, distance from streams, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index and plan curvature. For validation of the produced landslide susceptibility maps, the results of the analyses were compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all the landslide susceptibility models were constructed and the areas under the curves were calculated. The landslide locations were used to validate results of the landslide susceptibility maps. The verification results showed that the weights-of-evidence model (79.87%) performed better than certainty factor (72.02%) model with a standard error of 0.0663 and 0.0756, respectively. According to the results of the area under curve evaluation, the map produced by weights-of-evidence exhibits satisfactory properties.     

Geospatial modeling to identify the effects of anthropogenic processes on landscape pattern change and biodiversity

This research used geospatial data to quantify biodiversity changes and landscape pattern change to track anthropogenic impacts of such changes at the Mouteh Wildlife Refuge (MWR), Isfahan, Iran. Satellite image duration of four decades, LandSat_1-5, and IRS-P6 data were used to develop land cover classification maps for 1971, 1987, 1998, and 2011. The number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated and compared for a 40-year period. The results showed an increasing concern with regard to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. The number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated. Overall changes in natural land use between 1971 and 1998 at MWR showed that the number of patches for natural land use has increased, but it also showed a decrease in 2011. Similar changes were observed for seminatural land use. Within the artificial classes, the number and area of patches were higher and the largest patch occurred in 2011. The maximum variation of diversity is related to the year 2011. The results showed an increasing concern with regard to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. Remote sensing and geographic information system offers an important means of detecting and analyzing temporal changes occurring in our landscape.     

A review on the effect of rubber membrane in triaxial tests

This paper presents a review on the effect of rubber membranes on the measured stress and volume change data during the triaxial tests. In many instants, the error associated due to membrane penetration is measured to be close to the total sample volumetric strains. Hence, in addition to precise measurements, adopting an appropriate correction to the measured data is pivotal. This paper provides a detailed review on the methods used to estimate and reduce the influence of rubber membrane on the measured triaxial test results. The discussion therefore clearly highlights the stiffness, thickness, and diameter of the membrane used, to have significant influence on the measured deviatoric stress and the volume change data. The mean grain size of sample has an equally significant influence on the volume change. The methods in general are limited to test conditions, and therefore, it is practically uncertain to have a generalized correction procedure. Effectively, the outcome from this review work enhances the understanding of the reader towards the effect, methods, and corrections to be considered in triaxial testing due to rubber membrane and membrane penetration effects.     

Mg VIII DIAGNOSTIC LINE RATIOS IN SKYLAB SOLAR OBSERVATIONS

Recent calculations of Mgviii electron and proton impact excitations rates are used to derive theoretical electron temperature (T _e)- and density (N _e)-sensitive emission line ratios involving transitions in the 315–782 Å wavelength range. Some of these ratios are presented in the form of ratio–ratio diagrams, which should in principle allow both N _e and T _e to be deduced. These results are compared with solar observational data from Skylab, but agreement between theory and observation is very poor, probably due to blending.     

On the importance of satellite lines to the helium-like Kα complex and the G ratio for calcium, iron and nickel

New, more detailed calculations of the emission spectra of the He-like Kα complex of calcium, iron and nickel have been carried out using data from both distorted-wave and R-matrix calculations. The value of the GD ratio (an extended definition of the G ratio that accounts for the effect of resolved and unresolved satellite lines) is significantly enhanced at temperatures below the temperature of He-like maximum abundance. Furthermore, it is shown that satellite lines are important contributors to the GD ratio such that   GD / G > 1  at temperatures well above the temperature of maximum abundance. These new calculations demonstrate, with an improved treatment of the KLn     satellite lines, that Kα satellite lines need to be included in models of He-like spectra even at relatively high temperatures. The excellent agreement between spectra and line ratios calculated from R-matrix and distorted-wave data also confirms the validity of models based on distorted-wave data for highly charged systems, provided the effect of resonances is taken into account as independent processes.     

A Novel Approach to Estimate Diffuse Attenuation Coefficients for QuickBird Satellite Images: A Case Study at Kish Island, the Persian Gulf

Diffuse attenuation coefficient (k _d ) is a critical parameter for benthic habitat mapping using remotely sensed data. This research attempted to develop a new approach to estimate k _d in blue and green bands of QuickBird satellite image based on the integration of Lyzenga’s method and updated NASA-k _d ⁴⁹⁰ algorithm. To do this, the Lyzenga’s method was utilized to determine the ratio of k _d in different bands of QuickBird satellite image. Additionally, NASA-k _d ⁴⁹⁰ algorithm was applied to determine k _d ⁴⁹⁰ by using remotely sensed reflectance values of blue (R _rs ^Blue ) and green (R _rs ^Green ) bands in each pixel of QuickBird satellite image. Since the aforementioned algorithm has been developed for other types of sensors, an approach using weighted mean value of parameters for SeaWiFS, MERIS, VIIRS, and OCTS sensors were employed to estimate parameter values for QuickBird image. After determining the k _d ⁴⁹⁰ values as k _d for blue band, the k _d values for green and red bands were subsequently obtained by using Lyzenga’s method. Then, Mumby and Edwards’ method was employed as evidence to evaluate the accuracy of the results achieved from newly developed approach. Eventually, the maximum likelihood classifier was implemented during pre and post correction steps to examine the capability of the proposed approach. The final results proved to be consistent in the areas deeper than 2 m between estimated k _d values using the proposed approach and the results obtained from Mumby and Edwards’ method. On the other hand, the values estimated for extremely shallow areas seem to be overestimated. Furthermore, results demonstrated an increment of ~16 % in the overall accuracy of the classification.