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1.
Vietnam is in the process of introducing adaptive management based on indicators and time-series to manage its marine fisheries. The development of indicator-based frameworks for fisheries management has accelerated in recent years. This article describes the suite of indicators being suggested or used in Vietnam including the data-collection system. The Vietnamese approach is supported by an institutional structure consisting of a multi-disciplinary advisory system to facilitate the process of using indicators and to introduce knowledge-based management. The article concludes by identifying current problems and weaknesses and by providing suggestions for future improvement of the system.  相似文献   
2.
Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.  相似文献   
3.
To mitigate the impacts of impervious surfaces in urban areas, structures such as bioretention systems and permeable pavements have been installed to enhance infiltration in many countries. However, relatively little knowledge is available regarding the performance of such infiltration‐based structures in humid tropical and highly urbanized areas. This study investigates the feasibility of enhancing the infiltration of stormwater in tropical urbanized areas using Singapore as a case study. It first shows that the rainfall depth and intensity are both high, but the time interval between consecutive rainfall event is long in Singapore. It then numerically simulates single‐event local infiltration and finds that the fraction of infiltrated rainfall is actually high. It finally performs catchment‐scale simulations and finds that bioretention systems can enhance infiltration and groundwater recharge particularly during wet periods. However, local mounding of groundwater can be significant and can hinder the performance of those structures. Furthermore, with 5% of catchment area being converted to such structures, the infiltration of the entire catchment is enhanced but still not yet up to the natural level. To increase the overall effectiveness, future studies can look into bioretention systems with underdrain systems. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   
4.
The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years.  相似文献   
5.
The combined effect of periodic water impoundment and seasonal natural flood events has created a 30 m high water-level fluctuation zone(WLFZ) around the Three Gorges Reservoir(TGR), China, forming a unique eco-landscape. Siltation, eutrophication, enrichment of heavy metals, and methane emissions in the WLFZ have been widely associated with sediment and soil particles generated from the upstream catchment or upland slopes. However, little attention has been paid to the complexity of sediment particle-size distributions in the WLFZ. In the present study, core samples(from a 345 cm thick sediment core from the base of the WLFZ), slope transect surface samples(across/up a WLFZ slope), and along-river/longitudinal surface samples(from the reservoir reaches) were collected. Laser granulometry and a volume-based fractal model were used to reveal the characteristics of sediment particle-size distributions. Results indicate that the alternation of coarse and fine particles in the sedimentary core profile is represented as a fluctuation of low and high values of fractal dimension(D), ranging from 2.59 to 2.77. On the WLFZ slope transect, surface sediment particles coarsen with increasing elevation, sand content increases from 3.3% to 78.5%, and D decreases from 2.76 to 2.53. Longitudinally, surface sediments demonstrate a downstream-fining trend, and D increases gradually downstream. D is significantly positively correlated with the fine particle content. We conclude that D is a useful measure for evaluating sediment particle-size distribution.  相似文献   
6.
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7.
The main objective of the study was to evaluate and compare the overall performance of three methods, frequency ratio (FR), certainty factor (CF) and index of entropy (IOE), for rainfall-induced landslide susceptibility mapping at the Chongren area (China) using geographic information system and remote sensing. First, a landslide inventory map for the study area was constructed from field surveys and interpretations of aerial photographs. Second, 15 landslide-related factors such as elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to faults, distance to rivers, distance to roads, landuse, NDVI, lithology and rainfall were prepared for the landslide susceptibility modelling. Using these data, three landslide susceptibility models were constructed using FR, CF and IOE. Finally, these models were validated and compared using known landslide locations and the receiver operating characteristics curve. The result shows that all the models perform well on both the training and validation data. The area under the curve showed that the goodness-of-fit with the training data is 79.12, 80.34 and 80.42% for FR, CF and IOE whereas the prediction power is 80.14, 81.58 and 81.73%, for FR, CF and IOE, respectively. The result of this study may be useful for local government management and land use planning.  相似文献   
8.
Rainfall replenishes surface and subsurface water but is partially intercepted by a canopy. However, it is challenging to quantify the rainfall passing through the canopy (i.e. throughfall). This study derives simple‐to‐use empirical equations relating throughfall to canopy and rainfall characteristics. Monthly throughfall is calculated by applying a mass balance model on weather data from Singapore; Vancouver, Canada; and Stanford, USA. Regression analysis is then performed on the calculated throughfall with three dependent variables (i.e. maximum canopy storage, average rainfall depth and time interval between two consecutive rainfall in a month) to derive the empirical equations. One local equation is derived for each location using data from that particular location, and one global equation is derived using data from all three locations. The equations are further verified with calculated monthly throughfall from other weather data and actual throughfall field measurements, giving an accuracy of about 80–90%. The global equation is relatively less accurate but is applicable worldwide. Overall, this study provides a global equation through which one can quickly estimate throughfall with only information on the three variables. When additional weather data are available, one can follow the proposed methodology to derive their own equations for better estimates. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   
9.
Natural Resources Research - Ground vibration (PPV) is one of the hazard effects induced by blasting operations in open-pit mines, which can affect the surrounding structures, particularly the...  相似文献   
10.

Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R2) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R2. Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R2 compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO.

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