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杭州市第二水源千岛湖配水工程（简称配水工程）的实施将引起富春江水库下泄流量及过程改变,从而对钱塘江河口盐水入侵产生影响。建立考虑涌潮作用的二维盐度数值模型,在验证钱塘江河口潮位和盐度的基础上,预测配水工程实施对河口盐水入侵距离和重要取水口含氯度超标时间的影响。研究表明:配水工程实施后,上游富春江水库若按现状调度方案,对枯水年影响大,咸水上溯距离增加3.7 km,沿岸取水口的可取水时间缩短0.2~3.6 d,丰、平水年盐水入侵和引水前相当;通过水库的优化调度,可基本消除枯水年引水造成的盐水入侵影响。为减少配水工程实施的盐水入侵影响,采用水库的优化调度模式是必要的。 相似文献
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杭州城市供水85%取自钱塘江河口段,取水水质在枯水大潮期都不同程度地受到盐水入侵的威胁,分析钱塘江河口盐水入侵时空变化及研制二维数值预测模型对保障城市供水安全十分必要。根据钱塘江河口段实测水文氯度资料,分析了强潮作用下盐水入侵的时空变化特征;据此构建考虑斜压作用的二维水流、盐度输移的耦合数学模型,计算格式采用守恒性较好的有限体积法;在模型验证的基础上,数值分析了径流和潮汐对钱塘江河口段盐水入侵的影响,结果表明河口段的盐水入侵明显地受径流和潮汐的影响,据此可通过增大上游新安江水库的下泄流量抑制盐水入侵上溯以减小取水口氯度及超标时间,确保用水安全。 相似文献
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为提高洪水预报的精度以及修正的稳定性,在产流误差动态系统响应曲线修正方法的基础上,提出了产流比例系数的系统响应修正方法.将产流系列按照一定原则分成若干组,假定每组存在系统误差,通过引入一个比例系数来表示,应用系统响应理论,选择适当的参数率定方法确定最优比例系数,进而对时段产流量分组进行修正.将产流误差比例系数的系统响应修正方法应用于滩坑流域,并与产流误差动态系统响应曲线修正方法相比较,结果显示,对于流域的17场历史洪水,二者均能提高洪水预报的精度,但前者的修正效果更好,修正稳定性更强,适用范围更广. 相似文献
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珠江河口区枯季咸潮入侵与盐度输运机理分析 总被引：3，自引：0，他引：3
以河口区物质平衡原理为基础,应用物质输运机理对珠江三角洲河口区盐度净输运进行分析,对比各动力因子对盐度净输运贡献的大小.结果表明:珠江三角洲各河口盐淡水混合以缓混合型为主,分层系数均在0.01～1.0之间:盐度净输运主要是由斯托克斯输运和平均流输运控制,潮抽吸作用也不容忽视,其中,斯托克斯输运是导致咸潮上溯的最主要动力因素;珠江三角洲各口门径潮流情况及动力条件各有不同,除黄金站和挂定角V6站外,盐度输运以向上游为主,咸潮上溯明显. 相似文献
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由于预报模型的局限性和实时信息的不完善,洪水预报过程存在许多误差,而基于图形交互式修正技术是消除预报误差的有效手段。分析了水文预报过程交互式修正技术在洪水预报工作中的重要性,介绍了过程拟合平滑技术和样条插值技术,基于此基础上研究实现了以橡皮筋形式交互式修正水文预报过程的技术,并应用于洪水预报系统中。研究实例表明,该技术使用方便,有效地提高了洪水预报精度。 相似文献
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在气候变暖、海平面上升和人类活动等因素的作用下,海水入侵问题日益突出。盐度的变化是判定海水入侵严重程度的一个重要依据,因此,海水盐度的原位监测显得尤为重要。目前,海水盐度的传统监测方法缺少原位、实时、长距离的功能,而近年来有关长周期光纤光栅（LPG）技术测量液体折射率的研究为海水入侵过程中的盐度监测提供了一个新的视角。本文以海水入侵过程中盐度监测为研究背景,设计了基于LPG技术测量海水盐度可行性试验,并将测量结果与理论值进行了比较,论证了该技术用于海水盐度测量的可行性。试验结果表明:LPG谐振峰中心波长与盐度之间存在较好的线性关系,且测量值与理论值的线性相关程度较高,通过标定可实现对盐度的测量,LPG技术对海水盐度测量具有可行性。研究成果为研发下一代海水入侵LPG原位实时监测技术奠定了基础。 相似文献
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以延长洪水预见期、提高预报精度为目标,研究气象水文耦合机制,利用数值天气预报模式WRF(Weather Research and Forecasting)驱动分布式VIC(Variable Infiltration Capacity)水文模型,构建三峡库区陆气耦合洪水预报系统,并对2007~2008年期间四场暴雨洪水进行日滚动预报试验。结果表明,WRF模式在三峡库区内有着良好的短期降水预报精度,基于数值天气预报模式和分布式水文模型的陆气耦合洪水预报系统能有效延长三峡入库洪水预见期、提高洪水预报精度,具有较大的应用潜力。 相似文献
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In real-time flood warning systems, sufficient lead-time is important for people to take suitable actions. Rainfall forecasting is one of the ways commonly used to extend the lead-time for catchments with short response time. However, an accurate forecast of rainfall is still difficult for hydrologists using the present deterministic model. Therefore, a probability-based rainfall forecasting model, based on Markov chain, was proposed in this study. The rainfall can be forecast one to three hours in advance for a specified nonexceeding probability using the transition probability matrix of rainfall state. In this study, the nonexceeding probability, which was hourly updated on the basis of development or decay of rainfall processes, was taken as a dominant variable parameter. The accuracy of rainfall forecasting one to three hours in advance is concluded from the application of this model to four recording rain gauges. A lumped rainfall-runoff forecasting model derived from a transfer function was further applied in unison with this rainfall forecasting model to forecast flows one to four hours in advance. The results of combination of these two models show good performance with agreement between the observed and forecast hydrographs. 相似文献
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提出了地下工程裂隙岩体注浆量预测的遗传支持向量机方法,通过支持向量机对实际注浆数据样本进行学习,建立注浆量及其影响因素之间的非线性映射关系,基于这种关系实现注浆量的预测。模型建立过程中,考虑到支持向量机惩罚因子和核参数对预测精度的影响,以预测误差为适应度,采用遗传算法对最佳参数进行搜索。结果表明,本文方法计算快速,预测精度高,是一种注浆量预测的好方法。 相似文献
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大数据及机器学习技术在解决各行各业的复杂非线性关系问题方面已经体现出巨大的优势。本文尝试将随机森林(RF)算法引入三维成矿预测领域来开展研究,以胶东大尹格庄金矿为研究对象,在构建招平断裂(地质体)三维模型的基础上,通过各种空间分析方法提取控制矿体形成的若干控矿地质因素特征值,进而获取成矿空间中控矿地质因素分布值,最后将矿区钻孔立体单元化形成采样数据集并利用RF算法对矿区开展三维矿体定位预测,结果表明:决策树棵数M=800、属性个数K=7是最优参数,能获得总体精度97.32%和kappa系数0.6292的综合分类精度;RF算法的分类精度要优于支持向量机(SVM)算法和多层感知器(MP)算法。RF算法对大尹格庄金矿开展的三维矿体定位预测取得了较好效果,并在矿区深边部预测了7个三维找矿靶区,证明大数据技术在矿产资源定位预测方面具有巨大的应用前景。 相似文献
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支持向量机在砂土液化预测中的应用研究 总被引：4，自引：0，他引：4
介绍了人工智能领域最新的基于结构风险最小化原理的数据挖掘算法——支持向量机算法。根据支持向量机线性分类和可以具有不同核函数的非线性分类两种算法，建立了砂土液化预测模型，并且运用Matlab语言编写了程序。通过试算和分析比较得到了最佳模型，最佳模型的预测结果与实际液化情况基本上一致。认为支持向量机算法无论在学习或者预测精度方面都有很大的优越性，而基于支持向量机理论建立的砂土液化预测模型是可行的，且可以较为准确地实现砂土液化的预测。 相似文献
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在现阶段的岩土工程中,通常采用人工识别的方法来判别岩样种类,不仅耗时长、专业性强,还易受主观因素影响,准确率不理想。随着计算机技术的发展,机器学习逐渐被应用于岩性的自动识别,开启了岩样分类的新路径。本文以重庆市主城区4种典型岩样（泥岩、砂质泥岩、泥质砂岩和砂岩）的细观图像为研究对象,基于Inception V3卷积网络模型和迁移学习算法,建立了岩样细观图像深度学习模型,并完成了训练学习。结果显示：模型在训练1 000次后,训练集中的分类准确率达到92.77%,验证集中的分类准确率为76.31%。其中,验证集中的砂岩识别准确率为97.28%,泥岩识别准确率为81.85%,泥质砂岩识别准确率为72.59%,砂质泥岩识别准确率为72.35%。与现有的机器学习方法相比,本识别模型不仅可以自动识别岩性极为相近的岩样,而且具有较好的识别准确率、鲁棒性和泛化能力。 相似文献
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如何准确预测和控制基坑变形是基坑工程的一个难点,提出了一种基于小波变换、粒子群优化的最小二乘支持向量机（PSO-LSSVM）和自回归移动平均模型（ARMA）的基坑变形时间序列预测方法。首先,利用小波变换将基坑变形时间序列分解和重构为2个子序列--趋势时间序列和随机时间序列,在该基础上,采用PSO-LSSVM模型与ARMA模型分别预测趋势时间序列与随机时间序列未来值,将2个子序列的预测值求和作为最终预测结果。最后,将该方法应用于昆明某基坑工程的深层水平位移预测,不断地利用前期工况的最新实测数据建模,对后期工况未来变形量进行滚动预测,获得了令人满意的结果。 相似文献
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Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments and individuals. Although many methods have been developed for predicting oil prices, it remains one of the most challenging forecasting problems due to the high volatility of oil prices. In this paper, we propose a novel approach for crude oil price prediction based on a new machine learning paradigm called stream learning. The main advantage of our stream learning approach is that the prediction model can capture the changing pattern of oil prices since the model is continuously updated whenever new oil price data are available, with very small constant overhead. To evaluate the forecasting ability of our stream learning model, we compare it with three other popular oil price prediction models. The experiment results show that our stream learning model achieves the highest accuracy in terms of both mean squared prediction error and directional accuracy ratio over a variety of forecast time horizons. 相似文献
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Contreras Diana Wilkinson Sean Alterman Evangeline Hervás Javier 《Natural Hazards》2022,113(1):403-421
Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.
相似文献20.
《地学前缘(英文版)》2020,11(3):871-883
Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions. 相似文献