Coupling of DEM and remote-sensing-based approaches for semi-automated detection of regional geostructural features in Zagros mountain, Iran

In recent years, remote-sensing data have increasingly been used for the interpretation of objects and mapping in various applications of engineering geology. Digital elevation model (DEM) is very useful for detection, delineation, and interpretation of geological and structural features. The use of image elements for interpretation is a common method to extract structural features. In this paper, linear features were extracted from the Landsat ETM satellite image and then DEM was used to enhance those objects using digital-image-processing filtering techniques. The extraction procedures of the linear objects are performed in a semi-automated way. Photographic elements and geotechnical elements are used as main keys to extract the information from the satellite image data. This paper emphasizes on the application of DEM and usage of various filtering techniques with different convolution kernel size applied on the DEM. Additionally, this paper discusses about the usefulness of DEM and satellite digital data for extraction of structural features in SW of Zagros mountain, Iran.     

An optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data

This study proposed a workflow for an optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data. The method is validated on a set of data captured over a part of Selangor located in the Peninsular Malaysia. The method comprised four components including image segmentation, Taguchi optimization, attribute selection using random forest, and rule-based feature extraction. Results indicated the robustness of the proposed approach as the area under curve of forest; grassland, old oil palm, rubber, urban tree, and young oil palm were calculated as 0.90, 0.89, 0.87, 0.87, 0.80, and 0.77, respectively. In addition, results showed that SAR data is very useful for extracting rubber and young oil palm trees (given by random forest importance values). Finally, further research is suggested to improve segmentation results and extract more features from the scene.     

Tectonic process analysis in Zagros Mountain with the aid of drainage networks and topography maps dated 1950–2001 in GIS

In this study, a digital elevation model was used for hydrological study/watershed management, topography, geology, tectonic geomorphology, and morphometric analysis. Geographical information system provides a specialized set of tools for the analysis of topography, watersheds, and drainage networks that enables to interpret the tectonic activities of an area. The drainage system maps of Zagros Mountains in southwest Iran have been produced using multi-temporal datasets between 1950 and 2001 to establish the changes between geomorphic signatures and geomorphic aspect during time and to correlate them with recent neo-tectonics. This paper discusses the role of drainage for interpreting the scenario of the tectonic processes as one of important signatures. The study shows variation in drainage network derived from topography maps. Thus, changes in drainage pattern, stream length, stream gradient, and the number of segment drainage order from 1950 to 2001 indicate that Zagros Mountain has been subjected to recent neo-tectonic processes and emphasized to be a newly active zone.     

Sediment yield assessment at basin scale using geospatial technique

Soil erosion and sediment yield from catchments are key limitations to achieving sustainable land use and maintaining water quality in nature. One of the important aspects in protecting the watershed is evaluation of sediment produced by statistical methods. Controlling sediment loading in protecting the watershed requires knowledge of soil erosion and sedimentation. Sediment yield is usually not available as a direct measurement but is estimated using geospatial models. One of the geospatial models for estimating sediment yield at the basin scale is sediment delivery ratio (SDR). The present study investigates the spatial SDR model in determining the sediment yield rate considering climate and physical factors of basin in geographic information system environment. This new approach was developed and tested on the Amammeh catchments in Iran. The validation of the model was evaluated using the Nash Sutcliffe efficiency coefficient. The developed model is not only conceptually easy and well suited to the local data needs but also requires less parameter, which offers less uncertainty in its application while meeting the intended purpose. The model is developed based on local data. The results predict strong variations in SDR from 0 in to 70 % in the uplands of the Basin.     

Earthquake risk assessment in NE India using deep learning and geospatial analysis

Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 km²), vulnerability (480.98 km²), and risk (34,586.10 km²) was estimated.     

Dynamic Response of Machine Foundation on Layered Soil: Cone Model Versus Experiments

This paper presents the experimental validation of analytical solution based on cone model for machine foundation vibration analysis on layered soil. Impedance functions for a rigid massless circular foundation resting on a two layered soil system subjected to vertical harmonic excitation are found using cone model. Linear hysteretic material damping is introduced using correspondence principle. The frequency-amplitude response of a massive foundation is then computed using impedance functions. To verify the solution field experiments are conducted in two different layered soil systems such as gravel layer over in situ soil and gravel layer over concrete slab (rigid base). A total 72 numbers of vertical vibration tests on square model footing were conducted using Lazan type mechanical oscillator, varying the influencing parameters such as depth of top layer, static weight of foundation and dynamic force level. The frequency-amplitude response in general and in particular the resonant frequencies and resonant amplitudes predicted by cone model is compared with the results of experimental investigation, which shows a close agreement. Thus the cone model is reliable in its application to machine foundation vibration on layered soil.     

Simulation of rain-bearing summer monsoon systems along the west coast of India by use of ARMEX re-analysis

Re-analysis, using surface, upper-air, and satellite observations specially collected during the Arabian Sea Monsoon Experiment-I (ARMEX-I), has been performed with a global data assimilation system at T-80/L18 resolution. Re-analysis was performed for the entire ARMEX-I period (15th June–16th August 2002). In this paper we discuss the results based on re-analysis and subsequent forecasts for two successive intensive observation periods associated with heavy rainfall along the west coast of India during 2–12 August, 2002. Results indicate that the re-analysed fields can bring out better synoptic features, for example troughs along the west coast and mid tropospheric circulation over the Arabian Sea. Simulated rainfall distribution using re-analysis as initial condition also matches observed rainfall better than data from the initial analysis.     

Comparative assessment of water surface level using different discharge prediction models

Natural Hazards - Discharge is traditionally measured at gauge stations located at discrete positions along the river course. When the volume of water discharge is higher than the river bank,...     

Flood susceptibility mapping using integrated bivariate and multivariate statistical models

Flooding can have catastrophic effects on human lives and livelihoods and thus comprehensive flood management is needed. Such management requires information on the hydrologic, geotechnical, environmental, social, and economic aspects of flooding. The number of flood events that took place in Busan, South Korea, in 2009 exceeded the normal situation for that city. Mapping the susceptible areas helps us to understand flood trends and can aid in appropriate planning and flood prevention. In this study, a combination of bivariate probability analysis and multivariate logistic regression was used to produce flood susceptibility maps of Busan City. The main aim of this research was to overcome the weakness of logistic regression regarding bivariate probability capabilities. A flood inventory map with a total of 160 flood locations was extracted from various sources. Then, the flood inventory was randomly split into a testing dataset 70 % for training the models and the remaining 30 %, which was used for validation. Independent variables datasets included the rainfall, digital elevation model, slope, curvature, geology, green farmland, rivers, slope, soil drainage, soil effect, soil texture, stream power index, timber age, timber density, timber diameter, and timber type. The impact of each independent variable on flooding was evaluated by analyzing each independent variable with the dependent flood layer. The validation dataset, which was not used for model generation, was used to evaluate the flood susceptibility map using the prediction rate method. The results of the accuracy assessment showed a success rate of 92.7 % and a prediction rate of 82.3 %.     

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.