基于极限学习机的堤防工程多元风险指标评价方法

为提高堤防工程风险评价的准确性,提出了基于极限学习机的堤防工程多元风险指标评价方法。首先,综合考虑影响堤防风险的28个评价指标,利用层次分析法从预警系统、堤防工程系统、环境系统和社会经济系统这4个方面建立了堤防工程多元风险评价指标体系。接着,基于极限学习机算法对28个指标进行标准化处理及分级标准构建,以风险指标作为输入量,分级隶属度作为输出量,划分风险等级,量化评价指标,估计多元风险评价指标值和判断风险的严重程度。最后,依托鄱阳湖重点堤防——康山大堤,构建多元风险评价指标体系,运用极限学习机算法计算多元风险评价指标值。评价结果表明:康山大堤目前处于基本安全水平,符合康山大堤经过两次加固后的工程实际情况,并与其他方法进行对比,验证了提出方法的可靠性和有效性。该方法可拓展应用到其他重要水工结构工程风险评估中。     

基于IFMOP的玉环市区域水资源配置

引入无概率分布的区间数表示不确定性影响范围,选取经济效益、供需水效率和COD排放量分别构建经济目标、社会目标和环境目标优化函数,构建了玉环市不确定性模糊多目标规划(IFMOP)水资源配置模型,并计算了不确定性解集。结果表明:IFMOP配置计算成果可实现玉环市区域水量水质协调高效利用和社会-经济-生态系统均衡发展;在75%来水保证率下各行业配置总水量不确定性范围为14 375万~15 154万m³。IFMOP水资源配置模型在信息不充分的条件下能够全面使用各类数据信息进行水资源配置,更好地满足水资源在各行业配置过程中的动态管理需求。     

用水竞争力指数评价方法及其应用研究

水资源短缺加剧了区域、行业间水资源竞争的程度,影响了水资源的演变格局,也对水资源供需管理提出了新的要求。为了定量化描述用水竞争现象,辨析了用水竞争力概念、内涵及其驱动因素,引入了用水竞争力指数（WCI）,并采用驱动增长率、刚性需水量、用水效益和用水紧缺程度四类指标构建了用水竞争力评价模型,以京津冀地区为例,定量分析得到京津冀地区2001—2015年农业、工业和生活分行业多年平均用水竞争力指数分别为0.09、0.17和0.56,其中生活用水竞争力指数最大并长期保持增长趋势,成为影响京津冀地区用水竞争力的主要行业,其次是工业用水竞争力指数,但呈波动性变化特征,农业用水竞争力指数最小但呈持续增加的趋势。     

基于SPI的辽宁省庄河市近35年干旱演变特性研究

庄河市是辽南地区的重要水源地,分析其干旱演变特性有利于该地区水资源的合理开发与利用,对于防旱减灾、合理布局产业结构具有重要的现实意义。本文根据庄河市1984年1月1日至2018年12月31日的逐日降水数据计算了1个月、3个月、6个月、12个月共4个不同时间尺度下的标准化降水指数(SPI),并基于SPI对该地区近35年的年际与季节干旱演变特征进行了分析。结果表明:在年际变化方面,庄河市的SPI在2000年以前波动幅度较小,说明该地区的降水在时间上分布较为均匀;但自2000年以来,SPI序列出现较大的波峰与波谷,说明降雨的分布不均匀性有所增强;SPI的时间序列趋势线斜率为负值,说明该地区整体上呈现偏旱趋势,因此其水资源压力不断增大。在季节演变特征方面,夏季干旱和秋季干旱呈现增强趋势;秋旱的发生频率最高,而夏旱的出现频率最低。本研究结果可为庄河地区的抗旱减灾与水资源规划提供科学依据。     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

NDN数据提供者移动支持策略能耗建模与分析

基于数据提供者的移动节点在网络中的不同位置及不同移动速度,结合命名数据网络（NDN）请求者驱动的数据传输模式,提出了3类NDN移动支持策略的网络能耗解析表达式,为NDN移动网络的研究提供了形式化的建模方法.对这3类移动支持策略在不同场景下能耗的分析与比较结果表明,数据流行度分布、移动节点所处位置及移动速度对策略的能耗影响不尽相同,为后续探求最优能效的移动支持策略提供了理论参考.     

宁夏引黄灌区末段区域农业水资源配置研究

为探索农业水资源配置的新模式,以"自然—人工"二元水循环理论为基础,以宁夏引黄灌区末段区域(石嘴山市)为研究区,分析农田水循环过程,提出"引沟济渠"农田灌溉水源调配新思路。以农田灌溉水质要求、灌溉水量需求以及水资源管理用水红线为约束,借助MATLAB优化工具进行模拟计算,结果表明:在满足农田灌溉水质的前提下,取用惠农渠水4.83亿m~3、第五排水沟水0.92亿m~3、唐徕渠水0.82亿m~3、第三排水沟水0.31亿m~3,可节约黄河水1.23亿m~3;在上游排水沟水质较差情况下水质是主要约束条件,在上游排水沟水质较好情况下排水量是主要约束条件;实施"引沟济渠"模式应本着"差水控量,好水用尽"的原则,充分利用上游排水沟的来水。该农业水资源利用模式可节约渠灌水,做到"一水多用、多水联用",为干旱区灌区水资源配置提供借鉴。     

陕北无定河流域土壤侵蚀与植被覆盖和降雨关系研究

基于2000—2014年陕北无定河流域日降水、DEM、土壤类型、MODIS NDVI等数据,利用修正通用土壤流失方程(RUSLE)估算了流域土壤侵蚀情况,定量分析了植被覆盖和降雨在土壤侵蚀中的作用,结果表明:(1)无定河流域土壤侵蚀以微度侵蚀为主,平均占流域面积的88.35%,各土壤侵蚀等级面积比例随等级的升高而降低。2000—2014年微度侵蚀面积比例为下降趋势,轻度及以上等级面积比例均为上升趋势。(2)不考虑降雨因子影响时,无定河流域仍以微度侵蚀为主,且为增加趋势,而其他土壤侵蚀等级均为下降趋势。不考虑植被覆盖因子时,流域土壤侵蚀与模型计算结果的变化趋势基本一致。(3)植被覆盖对土壤侵蚀面积比例和变化速率的贡献率分别为13.67%和24.55%,而降雨作用达到86.33%和75.44%,表明降雨是流域土壤侵蚀的主要动力和控制因子,降雨变化主导着流域土壤侵蚀变化过程。     

安徽省农业旱灾脆弱性综合分析与评价

旱灾是影响安徽省农业特别是粮食生产可持续发展的重要制约因素。在相同致灾强度下,灾情会随灾害脆弱性的增强而加重,因此,研究农业旱灾脆弱性对安徽省防灾减灾和农业可持续发展具有特别重要意义。根据影响旱灾脆弱性的因素和指标体系选取的原则,选择了旱灾面积比、森林覆盖率、农村人均收入等9个指标,采用加权综合评价方法对各市旱灾脆弱性进行分析,并利用聚类分析方法进行旱灾脆弱性区划。结果表明,除合肥市、阜阳市、池州市、宿州市和黄山市5个市需要采取措施降低旱灾脆弱度外,安徽省总体上农业旱灾脆弱性较弱,有较强的抗旱承灾能力。     

Effects of extreme floods on macroinvertebrate assemblages in tributaries to the Mohawk River,New York,USA

Climate change is forecast to bring more frequent and intense precipitation to New York which has motivated research into the effects of floods on stream ecosystems. Macroinvertebrate assemblages were sampled at 13 sites in the Mohawk River basin during August 2011, and again in October 2011, following historic floods caused by remnants of Hurricane Irene and Tropical Storm Lee. The annual exceedance probabilities of floods at regional flow‐monitoring sites ranged from 0.5 to 0.001. Data from the first 2 surveys, and from additional surveys done during July and October 2014, were assessed to characterize the severity of flood impacts, effect of seasonality, and recovery. Indices of total taxa richness; Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness; Hilsenhoff's biotic index; per cent model affinity; and nutrient biotic index‐phosphorus were combined to calculate New York State Biological Assessment Profile scores. Analysis of variance tests were used to determine if the Biological Assessment Profile, its component metrics, relative abundance, and diversity differed significantly (p ≤ .05) among the four surveys. Only total taxa richness and Shannon–Wiener diversity increased significantly, and abundance decreased significantly, following the floods. No metrics differed significantly between the July and August 2014 surveys which indicates that the differences denoted between the August and October 2011 surveys were caused by the floods. Changes in taxa richness, EPT richness, and diversity were significantly correlated with flood annual exceedance probabilities. This study increased our understanding of the resistance and resilience of benthic macroinvertebrate communities by showing that their assemblages were relatively impervious to extreme floods across the region.