Semi Automatic Road Extraction by Fusion of High Resolution Optical and Radar Images

Two new methods for fusion of high-resolution optical and radar satellite images have been proposed to extract roads in high quality in this paper. Two fusion methods, including neural network and knowledge-based fusion are introduced. The first proposed method consists of two stages: (i) separate road detection using each dataset and (ii) fusion of the results obtained using a neural network. In this method, the neural networks are separately applied on high-resolution IKONOS and TerraSAR-X images for road detection, using a variety of texture parameters. The outputs of two neural networks, as well as the spectral features of optical image, are used in a third neural network as inputs. The second method is a knowledge-based fusion using thresholds of narrow roads and vegetation gray levels. First roads are extracted from each source separately. The outputs are then compared and advantages and disadvantages of each data source are investigated . The results obtained from accuracy assessment show the efficiency of the proposed methods. Furthermore, the comparison of the results showed the superiority of the first algorithm.     

Discussion of ‘Soil creep effects on ground lateral deformation and pore water pressure under embankments’

Settlement analysis of field cases is normally studied based on parameters interpreted from laboratory samples influenced by varying degrees of sample disturbance. Such disturbance is more pronounced in natural soft clays and could significantly affect the engineering properties of the soil, e.g. the over consolidation ratio (OCR) and compressibility index (C_c). Hence, it is vital to understand the role of sample quality in relation to soil characterisation for long-term settlement analyses. In this work, this is numerically illustrated by use of a simple creep model along with realistic parameter selection. This work takes its starting point on critical discussion of the work presented by Fatahi et al. (2013) and uses the opportunity to further clarify some important aspects of settlement/creep analyses in light of sample quality and parameter interpretation valid for the corresponding constitutive model.     

Inverse problem theory to chemo-poroelastic parameters estimation: an analytical/experimental investigation

Shale formation swelling is one of the main factors affecting wellbore instability and associated problems in drilling operation. In order to eliminate these problems, it is important to investigate formation characteristics and understand mechanisms of rock-fluid interaction, from chemical/mechanical point of view. Shale membrane efficiency is known as an important parameter affecting wellbore instability. In order to measure this parameter, many mathematical models and experimental efforts have been carried out which consider mechanical-chemical processes for rock-fluid interactions. In this study, the field equations governing the problem have been derived based on the linear chemo-poroelastic theory and solved using analytical/numerical methods. Afterward, a comprehensive workflow to characterize the chemo-poroelastic parameters of illite-rich shale is conducted in the laboratory. In fact, mineralogical and apparent properties of shale sample have been described and some setups were performed such as triaxial test and membrane efficiency. Then genetic algorithm has been applied to solve an inverse problem and get a match between experimental data and modeling results. Ultimately, the three important properties in shale-fluid interactions, i.e., shale membrane efficiency, hydraulic, and chemical diffusivity coefficient have been estimated. Comparing the simulation results with the experimental data indicates that the simulation model can appropriately simulate the pore pressure transmission test. With this approach, the required parameters can be estimated with good accuracy without using time-consuming and costly tests.     

Evaluation of rainfall spatial correlation effect on rainfall-runoff modeling uncertainty,considering 2-copula

Lack of accuracy of rainfall-runoff simulation (RRS) remains critical for some applications. Among various sources of uncertainty, precipitation plays a particular role. Rainfall rates as the main input data of RRS are of the first factors controlling the accuracy. In addition to the depth, spatial and temporal distributions of rainfall impact the flood discharge. Most of the previous studies on RRS uncertainty have ignored rainfall spatial distribution, where in large catchments, it is necessary to be modeled explicitly. Karoon III is one most important basin of the Iran because of the Karoon III dam in the outlet. In the present work, effect of spatial correlation of rainfall on HEC-HMS (SMA) continuous RRS uncertainty is evaluated using 2variate copula (2copula). Monte Carlo simulation (MCS) approach was used to consider the rainfall spatial dependence. To reduce the computational expense, sampling efficiency and convergence for MCS, Latin hypercube sampling (LHS) was used. Copula functions consider wide range of marginal probability distribution functions (PDFs), eliminating limits of regular join PDFs. For this aim, two scenarios were investigated. In the first scenario, sub-basin rainfall was considered independent, and in the second scenario, 2copula was adopted to model spatial correlation of rainfall. Dimensionless rainfall depths were calculated for each sub-basin, and the PDFs were determined. The generated random dimensionless rainfalls were reweighted and multiplied by watershed’s mean rainfall value. Stochastic Climate Library was used to generate continuous daily rainfalls. Sampling from dimensionless rainfalls using LHS algorithm, 100 runs of calibrated model-simulated 100 flows for each day following MCS, and 80 % certainty bound was calculated. Results showed that considering dependence decreased 18 % of the maximum uncertainty bound width, so the methodology could be recommended for decreasing predicted runoff error.     

Reservoir operation using system dynamics under climate change impacts: a case study of Yamchi reservoir,Iran

This paper proposes a decision support system for Yamchi reservoir operation in semi-arid region of Iran. The paper consists of the following steps: Firstly, the potential impacts of climate change on the streamflow are predicted. The study then presents the projections of future changes in temperature and precipitation under A2 scenario using the LARS-WG downscaling model and under RCP2.6, RCP4.5, and RCP8.5 using the statistical downscaling model (SDSM) in the northwestern of Iran. To do so, a general circulation model of HadCM3 is downscaled by using the LARS-WG model. As a result, the average temperature, for the horizon 2030 (2011–2030), will increase by 0.77 °C and precipitation will decrease by 11 mm. Secondly, the downscaled variables are used as input to the artificial neural network to investigate the possible impact of climate change on the runoffs. Thirdly, the system dynamics model is employed to model different scenarios for reservoir operation using the Vensim software. System dynamics is an effective approach for understanding the behavior of complex systems. Simulation results demonstrate that the water shortage in different sectors (including agriculture, domestic, industry, and environmental users) will be enormously increased in the case of business-as-usual strategy. In this research, by providing innovative management strategies, including deficit irrigation, the vulnerability of reservoir operation is reduced. The methodology is evaluated by using different modeling tests which then motivates using the methodology for other arid/semi-arid regions.