Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions

This study developed a methodology for formulating water level models to forecast river stages during typhoons, comparing various models by using lazy and eager learning approaches. Two lazy learning models were introduced: the locally weighted regression (LWR) and the k-nearest neighbor (kNN) models. Their efficacy was compared with that of three eager learning models, namely, the artificial neural network (ANN), support vector regression (SVR), and linear regression (REG). These models were employed to analyze the Tanshui River Basin in Taiwan. The data collected comprised 50 historical typhoon events and relevant hourly hydrological data from the river basin during 1996–2007. The forecasting horizon ranged from 1 h to 4 h. Various statistical measures were calculated, including the correlation coefficient, mean absolute error, and root mean square error. Moreover, significance, computation efficiency, and Akaike information criterion were evaluated. The results indicated that (a) among the eager learning models, ANN and SVR yielded more favorable results than REG (based on statistical analyses and significance tests). Although ANN, SVR, and REG were categorized as eager learning models, their predictive abilities varied according to various global learning optimizers. (b) Regarding the lazy learning models, LWR performed more favorably than kNN. Although LWR and kNN were categorized as lazy learning models, their predictive abilities were based on diverse local learning optimizers. (c) A comparison of eager and lazy learning models indicated that neither were effective or yielded favorable results, because the distinct approximators of models that can be categorized as either eager or lazy learning models caused the performance to be dependent on individual models.     

四川盆地含膏盐红层特征及坝基工程地质问题(Ⅱ)

本文探讨了四川盆地含膏盐红层类岩溶作用引起的坝基工程地质问题,首先是地下水化学成分的改变及其对水工结构混凝土的腐蚀作用及坝基渗漏问题,然后确定了表征类岩溶作用强度的类岩溶强度因子(k).最后,通过类岩溶岩体Vp与主要岩体工程力学指标,诸如:允许承载力fak、抗剪(断)摩擦系数f(f')、变形模量(E0)的回归关系,探讨了类岩溶作用对坝基岩体弱化的作用.     

石羊河流域水资源承载能力研究

基于石羊河流域经济社会发展和水资源利用状况,在分析水资源承载能力内涵的基础上,建 立了水资源承载能力计算方法,分析了石羊河流域的水资源承载状态,指出石羊河流域在当前和今后 一段时期仍将处于超载状态,需要继续加大水资源保护力度,促进实现经济社会可持续发展。     

长江流域水资源开发生态与环境制约问题研究

从全球意义、国家意义、流域意义及区域意义的角度,研究了长江流域内分布与存在的自然保护区、风景名胜区、地质公园、森林公园、世界自然文化遗产、水土流失重点预防区等重要生态与环境敏感区;从长江流域分布的珍稀动植物栖息地、重要和特有鱼类的洄游通道、重要珍稀陆生动物的迁徙通道等生态敏感区域等,辨识了长江流域重要陆生生境和水生生境以及重要陆生和水生生态敏感区。在辨识自然保护区、风景名胜区、森林公园、地质公园等环境敏感区、重要陆生生境和水生生境以及重要陆生和水生生态敏感区的基础上,结合流域水资源开发利用现状和规划,研究了长江流域不同区域、不同河流存在的生态与环境制约因素。     

2010年长江流域暴雨洪水初步分析

分析2010年长江流域暴雨洪水,有利于更全面认识长江流域暴雨洪水特性,可为今后更好开展长江流域防汛水情保障工程。从主汛期长江流域暴雨及气候特征、干支流主要洪水过程及洪水特性等方面,进行了初步的总结和分析。2010年长江主汛期洪水发生范围广、持续时间长且局部地区洪水量级大,通过与三峡水库蓄水前后几次大的历史洪水分析对比,初步认为,2010年长江流域主汛期出现的洪水为长江上中游区域性较大洪水。     

东江流域基于水质的水资源有偿使用与生态补偿机制

回顾了东江流域生态补偿的政策与实践,认为基于发展机会损失下的生态补偿机制有悖于我国水资源国家所有的基本国情,是导致东江流域生态补偿进展缓慢的根本原因。提出建立基于水资源有偿使用的生态补偿机制:通过"使用者向国家付费,国家向保护者转移",实现对水源保护区的生态补偿。     

Spatial variability of thermal regimes and other environmental determinants of stream fish communities in the Great Lakes Basin,Ontario, Canada

Temperature is one of the most important environmental variables in stream ecosystems because it affects the growth, survival and distribution of stream biota. This study examined if the spatial variability of thermal regimes and 18 other environmental variables were associated with fish communities in watersheds throughout the Great Lakes Basin (GLB), Ontario. The thermal regimes were defined as regimes 1, 2 and 3 and had maximum water temperatures of 26.4, 28.4 and 23.5°C, and spring warming rates of 0.20, 0.12 and 0.10 °C d^?1, respectively. The spatial variability of the thermal regimes (VTR) within the watersheds was summarized into four VTR groups: S1, S2, M23 and M123. Stream sites in S1 watersheds had temperatures characteristic of regime 1 whereas stream sites in S2 watersheds followed regime 2. M23 watersheds had sites with a mix of regimes 2 and 3 whereas M123 watersheds had all three thermal regimes at sites throughout watersheds. Canonical correspondence analysis (CCA) indicated that 16% of the variation in fish communities was related to the spatial VTR in the watersheds. Forward selection CCA indicated that elevation, the S1 VTR group, sparse forest cover, wetland area, base flow index (groundwater discharge potential), flow and industrial stress explained 42% of the variance in the fish communities. Simplified indicator species analysis (ISA) showed that different species could be used as indicators for each of the VTR groups. Human activities such as industrial development, deforestation, groundwater withdrawal and flow alteration all may affect the environmental variables related to stream fish communities. Copyright © 2010 John Wiley & Sons, Ltd.     

Gamma-ray remote sensing of aeolian salt sources in the Murray-Darling Basin, Australia

This paper presents a new technique for mapping regional salt sources that has major implications for salinity management in southeastern Australia. This was achieved by analyzing a regional mosaic of airborne gamma-ray emission derivatives and verified by existing airborne electromagnetic and drilling data. A significant correlation was found between aeolian (windblown) materials, upland salts and gamma-ray signatures. This is consistent with the conceptual model that much of the salt in the upland areas of the Murray-Darling Basin is sourced from deposited aeolian materials that have been derived from deflationary events in salt-bearing landscapes in the western arid part of the basin. From gamma-ray emissions, and based on an observed relationship with borehole salinity, concentrated aeolian salt source deposits contained about 0.7% potassium and 10 ppm thorium. Using this signature on normalized data, an Euclidean distance algorithm provided mapping and information relating to salt-mobility pathways over a wide region. The resulting gamma-ray salt source model (GSM) facilitates focussed management of salinity infiltration zones in catchments across the basin.     

柴达木盐湖产业生态文明建设战略研究

盐湖产业事关我国粮食安全,也是新能源和新材料产业的重要支撑。柴达木盆地是中国盐湖产业的核心,建设其产业生态文明重要而迫切。为推进柴达木盐湖产业生态文明建设,做强中国盐湖产业,本文从柴达木盐湖产业生态文明建设的现状出发,辨识产业生态文明建设面临的严峻挑战,提出了盐湖产业生态文明建设的四大战略。     

Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia

The impact of mineral aerosol (dust) in the Earth's system depends on particle characteristics which are initially determined by the terrestrial sources from which the sediments are entrained. Remote sensing is an established method for the detection and mapping of dust events, and has recently been used to identify dust source locations with varying degrees of success. This paper compares and evaluates five principal methods, using MODIS Level 1B and MODIS Level 2 aerosol data, to: (a) differentiate dust (mineral aerosol) from non-dust, and (2) determine the extent to which they enable the source of the dust to be discerned. The five MODIS L1B methods used here are: (1) un-processed false colour composite (FCC), (2) brightness temperature difference, (3) Ackerman's (1997: J.Geophys. Res., 102, 17069-17080) procedure, (4) Miller's (2003:Geophys. Res. Lett. 30, 20, art.no.2071) dust enhancement algorithm and (5) Roskovensky and Liou's (2005: Geophys. Res. Lett. 32, L12809) dust differentiation algorithm; the aerosol product is MODIS Deep Blue (Hsu et al., 2004: IEEE Trans. Geosci. Rem. Sensing, 42, 557-569), which is optimised for use over bright surfaces (i.e. deserts). These are applied to four significant dust events from the Lake Eyre Basin, Australia. OMI AI was also examined for each event to provide an independent assessment of dust presence and plume location. All of the techniques were successful in detecting dust when compared to FCCs, but the most effective technique for source determination varied from event to event depending on factors such as cloud cover, dust plume mineralogy and surface reflectance. Significantly, to optimise dust detection using the MODIS L1B approaches, the recommended dust/non-dust thresholds had to be considerably adjusted on an event by event basis. MODIS L2 aerosol data retrievals were also found to vary in quality significantly between events; being affected in particular by cloud masking difficulties. In general, we find that OMI AI and MODIS AQUA L1B and L2 data are complementary; the former are ideal for initial dust detection, the latter can be used to both identify plumes and sources at high spatial resolution. Overall, approaches using brightness temperature difference (BT10-11) are the most consistently reliable technique for dust source identification in the Lake Eyre Basin. One reason for this is that this enclosed basin contains multiple dust sources with contrasting geochemical signatures. In this instance, BTD data are not affected significantly by perturbations in dust mineralogy. However, the other algorithms tested (including MODIS Deep Blue) were all influenced by ground surface reflectance or dust mineralogy; making it impossible to use one single MODIS L1B or L2 data type for all events (or even for a single multiple-plume event). There is, however, considerable potential to exploit this anomaly, and to use dust detection algorithms to obtain information about dust mineralogy.