Merging multiple precipitation sources for flash flood forecasting

We investigated the effectiveness of combining gauge observations and satellite-derived precipitation on flood forecasting. Two data merging processes were proposed: the first one assumes that the individual precipitation measurement is non-bias, while the second process assumes that each precipitation source is biased and both weighting factor and bias parameters are to be calculated. Best weighting factors as well as the bias parameters were calculated by minimizing the error of hourly runoff prediction over Wu-Tu watershed in Taiwan. To simulate the hydrologic response from various sources of rainfall sequences, in our experiment, a recurrent neural network (RNN) model was used.

The results demonstrate that the merged method used in this study can efficiently combine the information from both rainfall sources to improve the accuracy of flood forecasting during typhoon periods. The contribution of satellite-based rainfall, being represented by the weighting factor, to the merging product, however, is highly related to the effectiveness of ground-based rainfall observation provided gauged. As the number of gauge observations in the basin is increased, the effectiveness of satellite-based observation to the merged rainfall is reduced. This is because the gauge measurements provide sufficient information for flood forecasting; as a result the improvements added on satellite-based rainfall are limited. This study provides a potential advantage for extending satellite-derived precipitation to those watersheds where gauge observations are limited.     

Probabilistic Visibility Forecasting Using Neural Networks

Statistical methods are widely applied in visibility forecasting. In this article, further improvements are explored by extending the standard probabilistic neural network approach. The first approach is to use several models to obtain an averaged output, instead of just selecting the overall best one, while the second approach is to use deterministic neural networks to make input variables for the probabilistic neural network. These approaches are extensively tested at two sites and seen to improve upon the standard approach, although the improvements for one of the sites were not found to be of statistical significance.     

Variable-density groundwater flow and solute transport in irregular 2D fracture networks

Numerical simulations of variable-density flow and solute transport have been conducted to investigate dense plume migration for various configurations of 2D fracture networks. For orthogonal fractures, simulations demonstrate that dispersive mixing in fractures with small aperture does not stabilize vertical plume migration in fractures with large aperture. Simulations in non-orthogonal 2D fracture networks indicate that convection cells form and that they overlap both the porous matrix and fractures. Thus, transport rates in convection cells depend on matrix and fracture flow properties. A series of simulations in statistically equivalent networks of fractures with irregular orientation show that the migration of a dense plume is highly sensitive to the geometry of the network. If fractures in a random network are connected equidistantly to the solute source, few equidistantly distributed fractures favor density-driven transport. On the other hand, numerous fractures have a stabilizing effect, especially if diffusive transport rates are high. A sensitivity analysis for a network with few equidistantly distributed fractures shows that low fracture aperture, low matrix permeability and high matrix porosity impede density-driven transport because these parameters reduce groundwater flow velocities in both the matrix and the fractures. Enhanced molecular diffusion slows down density-driven transport because it favors solute diffusion from the fractures into the low-permeability porous matrix where groundwater velocities are smaller. For the configurations tested, variable-density flow and solute transport are most sensitive to the permeability and porosity of the matrix, which are properties that can be determined more accurately than the geometry and hydraulic properties of the fracture network, which have a smaller impact on density-driven transport.     

Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks

By taking advantage of the close relationship between quality and quantity of water, we investigated the potential improvements of the in-reservoir water quality through the optimization of reservoir operational strategies. However, the few available techniques for optimization of reservoir operational strategies present some limitations, such as restrictions on the number of state/decision variables, the impossibility considering stochastic characteristics and difficulties for considering simulation/prediction models. One technique which presents great potential for overcoming some of these limitations is applied here and investigated for the first time in such complex system. The method, named stochastic fuzzy neural network (SFNN), can be defined as a fuzzy neural network (FNN) model stochastically trained by a genetic algorithm (GA) based model to yield a quasi optimal solution. The term “stochastically trained” refers to the introduction of a new loop within the training process which accounts for the stochastic variable of the system and its probabilities of occurrence. The SFNN was successfully applied to the optimization of the monthly operational strategies considering maximum water utilization and improvements on water quality simultaneous. Results showed the potential improvements on the water quality through means of hydraulic control.     

用上海强震土层垂直观测系统研究软土层对地震动的放大作用

利用上海曹杨公园土层垂直数字强震观测系统提供的井下基岩和地面自由场三分向数字地震波资料,使用经验转换函数方法,研究了该处软土层对地震动的放大作用。该场地软土层厚达310m。利用2002~2005年记录到的5次地震的数字波形进行的研究结果表明,经验转换函数方法描述场地效应是可靠的。东西、南北、上下三个分量的共振频率分别为5.4Hz、7.5Hz和10.9Hz,对应不同的共振振型;相应的放大倍数依次为20.2±7.1,27.8±2.9和20.7±9.6。并从理论上初步探讨了软土层对地震动放大作用的机理。软土层对地震动的放大作用取决于软土层的厚度,软土层及其下卧基岩的密度、波速,以及地震波的入射角。理论研究表明上海曹杨公园软土层的基础自振频率为0.28Hz。由于整个上海市坐落在软土层上,几百公里外发生强震,都可能在上海市造成灾害,必须引起重视。上海市的防震减灾规划除了需要考虑未来本区内发生破坏性地震的危险之外,还必须考虑到行政边界以外几百公里以内发生强烈地震带来的灾害。高层楼房和大跨度桥梁的设计必须考虑远距离地震传来的长周期地震动的影响。     

用BP网络求解土体的导热系数

以人工神经网络为基本工具,利用其强大的非线性映射能力,综合考虑土体的物理性质指标对其导热性能的影响,为求解土体的工程参数提供了一条新的途径。结果表明,ＢＰ网络能够充分体现土体物理指标之间的非线性关系和隐含关系,具有较高的求解能力,所求出的土体的导热系数与实测值接近,其精度远高于线性回归,且简单实用。     

Remote sensed data for automatic detection of land-use changes due to human activity in support to landslide studies

Slope instability studies appear to recognize a number of potential superficial slide-producing agents, which may be directly detected and monitored with Earth Observation (EO) data. The main objective of this work is to use conventional EO data and automatic techniques for providing land-use change maps useful in landslide prevention. The idea is to use the detection of changes in areas already involved in landslide events as a precursory sign of variations in the equilibrium status of the slope, independently from other natural triggering events, such as rain and seismic events. Attention is focused on man-induced surface changes, such as deforestation, urban expansion and construction of artificial structures. A historical set of 20 multi-temporal Landsat TM images, covering the period 1987–2000, was analyzed using a supervised change detection technique on a test site affected by slope instability phenomena located in the Abruzzo region in Southern Italy. A change image is obtained by comparing year-specific thematic map pairs. It contains useful information not only on the place where a transition occurred, but also on the specific classes involved in the transitions between two different years. The full set of change images is used to extract class-conditional transition probabilities, to evaluate variations in specific class distribution and the total number of changed pixels in time. Four classes and their transitions were considered in the analysis: (1) arboreous land, (2) agricultural land, (3) barren land, and (4) artificial structures. The quantitative analysis of the class-joint transition probability values of some specific class-transitions that may worsen slope stability showed that in an area prone to landslides the probability of landslide re-activation or first activation is higher where changes have occurred. Although based on a limited number of known events, such a result encourages extensive experimentation of the proposed technique on better documented landslide test sites.     

Sea-Ice Freeze-up Forecasts with an Operational Ocean Observatory

ABSTRACT

The evaluation of a new method for forecasting freeze-up at the eastern end of the Northwest Passage, a principal gateway into the Canadian High Arctic, is presented. The technique uses real-time data from a novel ocean observatory that combines acoustic, cable, and satellite communications technology to provide year-round bihourly data. The basis for the predictive capability comes from a previous analysis of a decade-long time series of instrumented mooring data collected in the area, which demonstrated a strong link between late summer upper ocean salinity and the timing of freeze-up there. Using real-time data from the observatory and this previously established relationship, accurate predictions of the timing of freeze-up in 2012 and 2013 with lead times of four weeks and two weeks, respectively, are presented. This unique forecasting capability points to the enhanced value of real-time data systems when these systems are located where previously collected long time series monitoring has revealed key relationships in the marine environment.     

中国定量遥感发展的一些思考

中国定量遥感始于20世纪80年代,在过去10多年得到了空前的发展。在2012年左右中国首次超越美国成为世界上在英文遥感刊物上发表文章最多的国家,特别是近几年文章数已经是美国的两倍左右。尽管在遥感数据的定量反演方法等研究方面,中国已经处于世界的前沿,是定量遥感的大国,但总体上尚不是定量遥感强国,在绝大多数的分支领域上还没有处于世界引领的地位。本文在回顾世界和中国定量遥感发展历史的基础上,从遥感数据获取与处理,前向辐射传输建模,变量反演,高级产品生产,地面观测,遥感云平台建设,遥感教育和遥感应用等8个方面,简要地讨论了中国定量遥感发展的一些问题和挑战,并提出了一些期望与初步建议。     

基于多模态的音乐推荐系统

使用传统协同过滤的方式进行推荐往往会忽视音乐底层特征.通过将音乐的音频特征与歌词信息进行多模态融合,并将融合后的特征信息作为协同过滤推荐的补充,提出了一种基于多模态的音乐推荐系统.主要探讨了音频特征与歌词信息的提取,并在提取歌词信息时利用LDA主题模型进行特征降维.针对多模态融合问题,使用一种特征级联早融合法（EFFC）融合方式,并将多模态融合后的结果与单模态结果进行了比较.对于结果的推荐,以多模态特征信息为依据建立用户兴趣模型,并将该模型通过LSTM神经网络,以过滤与优化协同推荐的用户组.结果表明,基于多模态的音乐推荐系统将推荐结果的误差项平方和（SSE）由传统的2.009降至0.388 6,验证了该方法的有效性.