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.     

Municipal solid waste landfill siting by using GIS and analytical hierarchy process (AHP): a case study in Qom city,Iran

Qom is the eighth most populated city in center of Iran, and its population growth rate is among the highest in this country. Th presence of a Great Salt Lake, petroleum potential and tourism attractions in this city sheds light on the importance of how solid waste landfill locations should be disposed, located and managed as an environmental issue. Considering the key parameters in landfill site selection, in this study a series of location analysis have been conducted to locate optimum regions for municipal solid waste disposal, using analytical hierarchy process (AHP) and geographical information system (GIS). The main factors in selecting the suitable location for waste disposal include geomorphology–hydrography, environmental–social factors and design criteria, each of which are subdivided into several categories. Criteria are selected according to the regional condition; therefore, important factors such as distance from sea and forested areas were not considered. In the next step, digital layers are weighted and classified according to the available standards and expert judgment. Then, analytical multi-criteria decision-making algorithms as AHP and weighted linear combination are applied upon existing layers in GIS. The results show that by implementing the AHP method in this region only 7% of the study area has a very good and appropriate condition for landfill location and the field observation confirms them. Finally, considering the environmental effects of landfill, appropriate locations are suggested.     

A Modular Support Vector Machine for Active Learning of Urban Remote Sensing Images Classification in Algeria

In this work, we present a new strategy of active learning, based on a modular version of support vector machine (MSVM) applied to urban remote sensing images in Algeria. In general, the training set is highly imbalanced, which gives more complex models; this difficulty is solved by dividing the problem at hand into a set of sub-problems, where each sub-model could be simpler to solve. The support vector machine is introduced to solve the problem of classification based on image remote sensing data related to atmospheric conditions and illumination reflectance. The aim of the proposed method is to improve the accuracy in order to understand the correlated elements of urban structures (the site, the built, the parcels, the network, the space), to generate the final classification result. In particular, we propose a new method based on the modular support vector machine (MSVM) adopted to active learning method, using three different clustering methods (i) k-means, (ii) fuzzy c-means (FCM), and (iii) Gustafson–Kessel (GKclust). Experimental results obtained on two QuickBird multispectral images of Sétif and Batna cities in the eastern of Algeria confirm the capabilities of the proposed methods based on the ensemble of model trained with different task decomposition compared to a traditional model using active learning. This method improves each class presents a main register in urban structure tissues.     

Inverse modelling for freshwater lens in small islands: Kish Island,Persian Gulf

A number of challenges including instability, nonconvergence, nonuniqueness, nonoptimality, and lack of a general guideline for inverse modelling have limited the application of automatic calibration by generic inversion codes in solving the saltwater intrusion problem in real‐world cases. A systematic parameter selection procedure for the selection of a small number of independent parameters is applied to a real case of saltwater intrusion in a small island aquifer system in the semiarid region of the Persian Gulf. The methodology aims at reducing parameter nonuniqueness and uncertainty and the time spent on inverse modelling computations. Subsequent to the automatic calibration of the numerical model, uncertainty is analysed by constrained nonlinear optimization of the inverse model. The results define the percentage of uncertainty in the parameter estimation that will maintain the model inside a user‐defined neighbourhood of the best possible calibrated model. Sensitivity maps of both pressure and concentration for the small island aquifer system are also developed. These sensitivity maps indicate higher sensitivity of pressure to model parameters compared with concentration. These sensitivity maps serve as a benchmark for correlation analysis and also assist in the selection of observations points of pressure and concentration in the calibration process. Copyright © 2012 John Wiley & Sons, Ltd.     

Automated numerical analysis tool for assessing potential bank failures during flooding

River embankments failure due to severe flooding is an extremely complex phenomena triggering permanent or temporary modification to the river morphology, river flow and sediment movement. Reliable and automatic prediction of these movements is crucial to properly identify the protective measures for residents living within the inundation flood zones. In this regard, BISHOP, a decision tool to automatically predict, at multiple river cross-sections, the slope failure circle with the minimum safety factor has been developed. In this paper, the computer tool BISHOP, named after the simplified Bishop method, is presented. Its applications have proven to be highly efficient in real case studies, where the stability of multiple slope profiles, at different river cross-sections, must be analyzed to establish spatial and temporal evolution of the river banks failures. The integration of the proposed methodology within a comprehensive flow hydrodynamic, sediment transport and landslide calculation has particularly enhanced the evaluation of the flood-risk zone during major flooding. Typical results demonstrating the effectiveness of the developed methodology are demonstrated during the analysis of the evolution of a river reach downstream of a dam a dam break scenario.     

The inclusive and simplified forms of Bayesian interpolation for general and monotonic models using Gaussian and Generalized Beta distributions with application to Monte Carlo simulations

A recently developed Bayesian interpolation method (BI) and its application to safety assessment of a flood defense structure are described in this paper. We use a one-dimensional Bayesian Monte Carlo method (BMC) that has been proposed in (Rajabalinejad 2009) to develop a weighted logical dependence between neighboring points. The concept of global uncertainty is adequately explained and different uncertainty association models (UAMs) are presented for linking the local and global uncertainty. Based on the global uncertainty, a simplified approach is introduced. By applying the global uncertainty, we apply the Guassian error estimation to general models and the Generalized Beta (GB) distribution to monotonic models. Our main objective in this research is to simplify the newly developed BMC method and demonstrate that it can dramatically improve the simulation efficiency by using prior information from outcomes of the preceding simulations. We provide theory and numerical algorithms for the BI method geared to multi-dimensional problems, integrate it with a probabilistic finite element model, and apply the coupled models to the reliability assessment of a flood defense for the 17th Street Flood Wall system in New Orleans.     

Flooding of the Saguenay region in 1996. Part 2: Aux Sables River flood mitigation and environmental impact assessment

Natural Hazards - In 2008, Wenchuan earthquake shook Sichuan Province in China. The questions of how to value the economic loss that would result if a catastrophe were to occur in Capital Beijing...     

(235)U, (238)U, (232)Th, (40)K and (137)Cs activity concentrations in marine sediments along the northern coast of Oman Sea using high-resolution gamma-ray spectrometry

The natural radioactivity levels in sediment samples of the northern coast of Oman Sea, covering the coastal strip from Hormoz canyon to Goatr seaport, as the first time has been determined. The results of measurements will serve as background reference level for Oman Sea coastlines. Sediments from 36 coastal and near shore locations were collected for analysis. Analysis on the collected samples were carried out to determine (235)U, (238)U, (232)Th, (40)K and (137)Cs using two high purity germanium detectors with 38.5% and 55% relative efficiencies. The concentration of (235)U, (238)U, (232)Th, (40)K and (137)Cs in sediment samples ranged between 1.01 and 2.87Bq/kg, 11.83 and 22.68Bq/kg, 10.7 and 25.02Bq/kg, 222.89 and 535.07Bq/kg and 0.14 and 2.8Bq/kg, respectively. The radium equivalent activity was well below the defined limit of 370Bq/kg. The external hazard indices were found to be less than 1, indicating a low dose.     

Wave dispersion in high‐rise buildings due to soil–structure interaction

Nonparametric techniques for estimation of wave dispersion in buildings by seismic interferometry are applied to a simple model of a soil–structure interaction (SSI) system with coupled horizontal and rocking response. The system consists of a viscously damped shear beam, representing a building, on a rigid foundation embedded in a half‐space. The analysis shows that (i) wave propagation through the system is dispersive. The dispersion is characterized by lower phase velocity (softening) in the band containing the fundamental system mode of vibration, and little change in the higher frequency bands, relative to the building shear wave velocity. This mirrors its well‐known effect on the frequencies of vibration, i.e. reduction for the fundamental mode and no significant change for the higher modes of vibration, in agreement with the duality of the wave and vibrational nature of structural response. Nevertheless, the phase velocity identified from broader band impulse response functions is very close to the superstructure shear wave velocity, as found by an earlier study of the same model. The analysis reveals that (ii) the reason for this apparent paradox is that the latter estimates are biased towards the higher values, representative of the higher frequencies in the band, where the response is less affected by SSI. It is also discussed that (iii) bending flexibility and soil flexibility produce similar effects on the phase velocities and frequencies of vibration of a building. Copyright © 2014 John Wiley & Sons, Ltd.