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三层BP神经网络地震灾害人员伤亡预测模型 总被引：13，自引：0，他引：13
选择地震发生时刻、震级、震中烈度、建筑物倒塌和严重破坏率、抗震设防水准、人口密度、地震预报等7个评价指标,以20次严重地震灾害为示例（其中,17个作训练样本,3个作验证样本）,建立了三层BP神经网络地震灾害人员伤亡预测模型。基于MATLAB6,5BP神经网络训练,得出的预测结果与各个示例的实际数值比较吻合。验证样本的训练结果表明,该模型适用于地震灾害人员伤亡评估。通过对评价指标的权重计算,确认人口密度、建筑物倒塌与严重破坏率、震中烈度是影响地震灾害人员伤亡的主要因素,地震预报、抗震设防水准、地震发生时刻和震级次之。作为人为可控预测指标,减少人口密度特别是城市人口密度,提高建（构）筑物抗震能力及预测预报水平,对于减少地震灾害人员伤亡起更重要的作用。 相似文献
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储层渗透率预测和评价是油气藏勘探与开发急需突破的瓶颈技术之一,BP神经网络预测储层渗透率的研究在行业中已有一定的应用,但受限于数据规模、参数调整及模型评价方法,该方法预测结果不稳定,且不能准确给出全井段储层的连续渗透率的预测质量,在油田现场并未大规模推广应用.本文针对传统BP神经网络预测储层渗透率方法中存在的问题,在对机器学习的数据处理、参数选择系统考察的基础上,定量分析了不同输入曲线、网络结构、样本大小对渗透率预测模型精度的影响,总结了BP神经网络预测渗透率模型的参数优选方案;并提出了一种基于模型森林的预测曲线质量逐点评价方法,实现了对全井段渗透率预测的质量评价.实际应用表明,本研究提出的储层渗透率预测及质量评价方法与实际岩心渗透率吻合度高,推广应用前景良好. 相似文献
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基于ArcGIS的地震灾害应急决策支持系统的设计与实现 总被引：4，自引：5，他引：4
在新型GIS平台软件ArcGIS（包括ArcObjects和AwIMS）的基础上，利用COM／DCOM、ActiveX、ASP等网络开发技术及OO4O、ArcSDE等空间数据库开发技术，论述了地震灾害损失评估模型的空间集成方案和可视化表达技术。同时。以软件工程为指导，提出了一种建立基于ArcGIS技术的地震灾害应急指挥系统的技术思路和解决方案，该方案阐述了城市地震应急决策支持系统的体系结构、技术路线以及功能构建等。最后给出了试验系统的界面。 相似文献
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为贯彻“以预防为主”的地震工作方针和“经济建设要依靠科学技术,科学技术工作要面向经济建设”的方针,国家地震局除负责全国的地震监视、预报,以及建设规划区和工程建设场地的地震危险性分析工作之外,对地震灾害的预防工作也一直非常重视,并把地震灾害预测视为地震灾害预防的依据,将地震灾害预测研究列入了地震局的基本工作范围。地震灾害预测是地震工作的基本任务之一,目的在于有效地减轻地震灾害。震害预测是在地震烈度区划图、地震危险性分析和地震小区划的基础上,根据人口和设施(包括建筑物、生命线设施和各种设备)的分布情况以及各类设施的易损性,预测位于具有潜在地震危险地区的城市、乡村和工矿企业由地震造成的 相似文献
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将地震灾害等级划分为巨灾、大灾、中灾、小灾、微灾等5个等级,给出每个灾害等级的灾度下限Di。选取地震灾害的死亡人数和直接经济损失相对值作为计算灾度的2个指标,采用“直接经济损失/年人均GDP”处理方法对直接经济损失进行无量纲化,为不同年代、不同地区的地震灾害灾度大小对比奠定基础。采用椭圆方程作为相邻灾害等级之间的分界线,给出相应参数以确定分界线方程表达式,在此基础上给出合理的地震灾度模型计算公式。利用假设值和中国部分实际地震灾例验证了本文给出的地震灾度模型的科学性、适用性和可比性,给出的地震灾害灾度计算模型可以推广到其他灾害。最后,比较唐山地震和汶川地震的计算灾度值,认为唐山地震的灾度大于汶川地震,并对这一结果的合理性进行了讨论。 相似文献
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在进行大规模城乡震害预测工作中, 需要使用与传统预测方式不同的新模型及新方法, 以便实现震害快速预测. 利用容易得到的人口统计数据中的人口及建筑抽样信息,通过建筑物分类,在已有的城市建筑震害基础上采用类比方法进行建筑物易损性分析,给出了人口数据及灾害损失的关系模型. 利用该模型建立福建省区域范围的建筑物不同结构平均易损性矩阵,按经济条件给出结构不同年代易损性矩阵调整系数,并建立地震灾害快速评估系统. 应用结果表明, 基于人口统计数据方法进行城乡区域尺度的地震震害评估模型, 具有投入少、 数据自动预测、定期更新且易于获取等优点. 相似文献
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江苏高邮-宝应交界4.9级地震震害分析 总被引：4，自引：2，他引：2
本文根据台网监测资料及现场调查结果,介绍了2012年7月20日江苏高邮、宝应交界4.9级地震的基本特征,给出了本次地震的震源机制解,结合区域地质构造背景与现场宏观调查结果分析认为,本次地震的发生可能与杨汉苍-桑树头断裂有关.以现场调查资料为基础,对震害进行分析,提出了本地区地震烈度评价标志,并给出了本次地震的烈度分布图.根据砖混结构、砖木结构房屋的震害特点,认为水网地区地基条件差、抗震能力差、建筑质量差和年旧失修是房屋严重破坏的主要原因.针对地震灾害的地区性差异,给出了加快推进新农村建设、提高农村民居抗御地震灾害能力和加强中强地震孕震发震研究等建议,以期为震害调查和地震灾害的预测预防提供参考. 相似文献
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《国际地震动态》2020,(3)
我国城市化进程的加快使人口与财富高度集中,城市向大型化、复杂化发展,在地震面前变得越发脆弱,而我国多数城市位于地震高危险区,灾害风险迅速攀升。充分借鉴国际减轻地震灾害风险先进理念,结合当今智能技术,开展地震风险评估与监测技术研究,已成为我国当前防震减灾工作的重中之重。国家重点研发计划项目"区域与城市地震风险评估与监测技术研究"以研发高性能区域与城市地震灾害监测及组网观测技术为手段,建立融合工程结构性态、社会和经济等多元信息的区域与城市大震风险动态评价指标体系、评估技术和软件系统平台,并开展应用示范,实现区域与城市地震灾害风险科学化、精准化和动态化评估,为显著提升我国抗御地震灾害风险能力提供关键技术支撑。经过两年的研究,设计并生产了MEMS加速度计样品,提出了观测网络优化布置方法、典型结构台阵优化布设方案和改进的数据多跳路由算法数据传输模式;构建了RC构件可视损伤识别的卷积神经网络Damage-Net,引入强跟踪滤波算法,实现了建筑结构体系时变物理参数的有效追踪,并建立了建筑抗震韧性评价方法;提出了基于计算机视觉的数据异常探测方法、桥梁结构基于弹塑性耗能差率的损伤指数模型和基于卷积神经网络和递归图的桥梁损伤识别方法,建立了桥梁地震破坏监测和性态评估标准Benchmark模型;分别建立了基于遥感数据的建筑物提取技术、单体建筑结构和区域建筑群结构性能水平恢复函数模型和结构恢复能力计算方法,构建了区域和城市大震风险评估指标体系和风险动态评价模型;提出了基于物联网大震灾害监测系统总体架构、考虑多损伤状态的参数化桥梁地震灾害风险评估模型,开发了建筑群地震灾害仿真系统;初步完成了示范建筑地震监测方案设计,完成了示范桥梁地震监测网络建设和三河市多元信息的数据库建设;初步设计了三河市区域地震灾害监测网络。 相似文献
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Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting. 相似文献
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ABSTRACT A forecasting model is developed using a hybrid approach of artificial neural network (ANN) and multiple regression analysis (MRA) to predict the total typhoon rainfall and groundwater-level change in the Zhuoshui River basin. We used information from the raingauge stations in eastern Taiwan and open source typhoon data to build the ANN model for forecasting the total rainfall and the groundwater level during a typhoon event; then we revised the predictive values using MRA. As a result, the average accuracy improved up to 80% when the hybrid model of ANN and MRA was applied, even where insufficient data were available for model training. The outcome of this research can be applied to forecasts of total rainfall and groundwater-level change before a typhoon event reaches the Zhuoshui River basin once the typhoon has made landfall on the east coast of Taiwan. 相似文献
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Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feed‐forward, back‐propagation network structure with an optimized conjugated training algorithm. Using long‐term observations of rainfall and river flow during 1939–2000, the ANN model was satisfactorily trained and verified. Model predictions of river flow match well with the observations. The correlation coefficients between forecasting and observation for daily, monthly, quarterly and yearly flow forecasting are 0·98, 0·95, 0·91 and 0·83, respectively. Results of the forecasted flow rates from the ANN model were compared with those from a traditional autoregressive integrated moving average (ARIMA) forecasting model. Results indicate that the ANN model provides better accuracy in forecasting river flow than does the ARIMA model. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
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Two models, one linear and one non‐linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non‐linear model is based on a multilayer feed‐forward back propagation (FFBP) artificial neural network (ANN) and uses flow‐stage data measured at the upstream and downstream stations. ANN predicted the real‐time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow‐stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4‐h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8‐h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement. 相似文献
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A. O. Pektas 《水文科学杂志》2017,62(14):2415-2425
This study examines the employment of two methods, multiple linear regression (MLR) and an artificial neural network (ANN), for multistep ahead forecasting of suspended sediment. The autoregressive integrated moving average (ARIMA) model is considered for one-step ahead forecasting of sediment series in order to provide a comparison with the MLR and ANN methods. For one- and two-step ahead forecasting, the ANN model performance is superior to that of the MLR model. For longer ranges, MLR models provide better accuracy, but there is an important assumption violation. The Durbin-Watson statistics of the MLR models show a noticeable decrease from 1.3 to 0.5, indicating that the residuals are not dependent over time. The scatterplots of the three methods (MLR, ARIMA and ANN) for one-step ahead forecasting for the validation period illustrate close fits with the regression line, with the ANN configuration having a slightly higher R2 value. 相似文献
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Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models 总被引：1，自引：1，他引：0
Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes. 相似文献
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Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis 总被引：3，自引：0，他引：3
In this study, a new method of stand-alone short-term spring snowmelt river flood forecasting was developed based on wavelet and cross-wavelet analysis. Wavelet and cross-wavelet analysis were used to decompose flow and meteorological time series data and to develop wavelet based constituent components which were then used to forecast floods 1, 2, and 6 days ahead. The newly developed wavelet forecasting method (WT) was compared to multiple linear regression analysis (MLR), autoregressive integrated moving average analysis (ARIMA), and artificial neural network analysis (ANN) for forecasting daily stream flows with lead-times equal to 1, 2, and 6 days. This comparison was done using data from the Rideau River watershed in Ontario, Canada. Numerical analysis was performed on daily maximum stream flow data from the Rideau River station and on meteorological data (rainfall, snowfall, and snow on ground) from the Ottawa Airport weather station. Data from 1970 to 1997 were used to train the models while data from 1998 to 2001 were used to test the models. The most significant finding of this research was that it was demonstrated that the proposed wavelet based forecasting method can be used with great accuracy as a stand-alone forecasting method for 1 and 2 days lead-time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year-to-year, and that there is a relatively stable phase shift between the flow and meteorological time series. The best forecasting model for 1 day lead-time was a wavelet analysis model. In testing, it had the lowest RMSE value (13.8229), the highest R2 value (0.9753), and the highest EI value (0.9744). The best forecasting model for 2 days lead-time was also a wavelet analysis model. In testing, it had the lowest RMSE value (31.7985), the highest R2 value (0.8461), and the second highest EI value (0.8410). It was also shown that the proposed wavelet based forecasting method is not particularly accurate for longer lead-time forecasting such as 6 days, with the ANN method providing more accurate results. The best forecasting model for 6 days lead-time was an ANN model, with the wavelet model not performing as well. In testing, the wavelet model had an RMSE of 57.6917, an R2 of 0.4835, and an EI of 0.4366. 相似文献
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Drought is one of the most devastating climate disasters. Hence, drought forecasting plays an important role in mitigating some of the adverse effects of drought. Data-driven models are widely used for drought forecasting such as ARIMA model, artificial neural network (ANN) model, wavelet neural network (WANN) model, support vector regression model, grey model and so on. Three data-driven models (ARIMA model; ANN model; WANN model) are used in this study for drought forecasting based on standard precipitation index of two time scales (SPI; SPI-6 and SPI-12). The optimal data-driven model and time scale of SPI are then selected for effective drought forecasting in the North of Haihe River Basin. The effectiveness of the three data-models is compared by Kolmogorov–Smirnov (K–S) test, Kendall rank correlation, and the correlation coefficients (R2). The forecast results shows that the WANN model is more suitable and effective for forecasting SPI-6 and SPI-12 values in the north of Haihe River Basin. 相似文献