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1.
准确而可靠地预测地下水埋深对生态环境保护和水资源规划管理具有重要意义。针对吉林西部浅层地下水位动态变化的复杂性和非线性,提出了基于小波分析与人工神经网络相结合的预测方法小波神经网络(WA-ANN)模型。将研究区2002年1月2009年12月当月降水量、蒸发量、人工开采量和前月平均地下水埋深4个参数作为输入,当月平均地下水埋深作为输出,建立浅层地下水埋深预测模型,并与BP神经网络(BP-ANN)模型和自回归移动平均(ARIMA)模型进行比较,对比分析了三者的建模过程及其模拟精度。结果显示:相比两种ANN模型,ARIMA模型建模过程更为简单,计算效率更高;但WA-ANN模型的拟合精度高于BP-ANN和ARIMA模型,预测效果更好。总体来看,WA-ANN模型在浅层地下水埋深预测中具有一定的应用推广价值。 相似文献
2.
Summary This paper focuses on short-range modelling and forecasting of aggregate US monthly coal production. The 1976–83 time-series data suggest a multiplicative autoregressive integrated moving average (ARIMA) model to replicate national level monthly coal production. The identified model required 12-month seasonal differencing and has an autoregressive component of lag 1 and a moving average component of lag 12. Model predictions for 1984 were very reasonable when compared with actual production: cyclical patterns were correctly replicated and the deterministic increasing trend was properly identified. The estimated model was enhanced by updating it with data for 1984. Intervention analysis was used to determine the impact of labour negotiations in coal production. Information relative to the identified ARIMA model was then used to model the intervening event of labour negotiations. Intervention modelling produced forecasts for 1984 superior to those identified by the ARIMA model. The mean predicted 1984 US monthly coal production of 1976–84 ARIMA and intervention models were 96.05 and 99.65% of the observed value of 74 178 thousand short tons per month, respectively. Simplicity of the ARIMA and intervention models, the realiability of their predictions, and the ease of updating make them very attractive when compared with large scale econometric models for use in short-term coal production forecasting. 相似文献
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The shortage of surface water in arid and semiarid regions has led to the more use of the groundwater resources. In these areas, the groundwater is essential for activities such as water supply and irrigation. One of the most important stages in sustainable yield of groundwater resources is awareness of groundwater level. In this study, we have applied artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models for groundwater level forecasting to 4 months ahead in Shiraz basin, southwestern Iran. Time series analysis was conducted according to the Box–Jenkins method. Meanwhile, gamma and M-test were considered for determining the optimal input combination and length of training and testing data in the ANN model. The results indicated that performance of multilayer perceptron neural network (4, 14, 1) and ARIMA (2, 1, 2) is satisfactory in the groundwater level forecasting for one month ahead. The performance comparison shows that the ARIMA model performs appreciably better than the ANN. 相似文献
4.
In this study, we successfully present the analysis and forecasting of Caspian Sea level pattern anomalies based on about 15 years of Topex/Poseidon and Jason-1 altimetry data covering 1993–2008, which are originally developed and optimized for open oceans but have the considerable capability to monitor inland water level changes. Since these altimetric measurements comprise of a large datasets and then are complicated to be used for our purposes, principal component analysis is adopted to reduce the complexity of large time series data analysis. Furthermore, autoregressive integrated moving average (ARIMA) model is applied for further analyzing and forecasting the time series. The ARIMA model is herein applied to the 1993–2006 time series of first principal component scores (sPC1). Subsequently, the remaining data acquired from sPC1 is used for verification of the model prediction results. According to our analysis, ARIMA (1,1,0)(0,1,1) model has been found as optimal representative model capable of predicting pattern of Caspian Sea level anomalies reasonably. The analysis of the time series derived by sPC1 reveals the evolution of Caspian Sea level pattern can be subdivided into five different phases with dissimilar rates of rise and fall for a 15-year time span. 相似文献
5.
Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models 总被引：4，自引：0，他引：4
In this study, multi-linear regression (MLR) approach is used to construct intermittent reservoir daily inflow forecasting
system. To illustrate the applicability and effect of using lumped and distributed input data in MLR approach, Koyna river
watershed in Maharashtra, India is chosen as a case study. The results are also compared with autoregressive integrated moving
average (ARIMA) models. MLR attempts to model the relationship between two or more independent variables over a dependent
variable by fitting a linear regression equation. The main aim of the present study is to see the consequences of development
and applicability of simple models, when sufficient data length is available. Out of 47 years of daily historical rainfall
and reservoir inflow data, 33 years of data is used for building the model and 14 years of data is used for validating the
model. Based on the observed daily rainfall and reservoir inflow, various types of time-series, cause-effect and combined
models are developed using lumped and distributed input data. Model performance was evaluated using various performance criteria
and it was found that as in the present case, of well correlated input data, both lumped and distributed MLR models perform
equally well. For the present case study considered, both MLR and ARIMA models performed equally sound due to availability
of large dataset. 相似文献
6.
Assessment of prediction performances of stochastic models: Monthly groundwater level prediction in Southern Italy 
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下载免费PDF全文 Stochastic modelling of hydrological time series with insufficient length and data gaps is a serious challenge since these problems significantly affect the reliability of statistical models predicting and forecasting skills. In this paper, we proposed a method for searching the seasonal autoregressive integrated moving average(SARIMA) model parameters to predict the behavior of groundwater time series affected by the issues mentioned. Based on the analysis of statistical indices, 8 stations among 44 available within the Campania region(Italy) have been selected as the highest quality measurements. Different SARIMA models, with different autoregressive, moving average and differentiation orders had been used.By reviewing the criteria used to determine the consistency and goodness-of-fit of the model, it is revealed that the model with specific combination of parameters, SARIMA(0,1,3)(0,1,2) _(12), has a high R~2 value,larger than 92%, for each of the 8 selected stations. The same model has also good performances for what concern the forecasting skills, with an average NSE of about 96%. Therefore, this study has the potential to provide a new horizon for the simulation and reconstruction of groundwater time series within the investigated area. 相似文献
7.
Hossein Mojaddadi Rizeei Biswajeet Pradhan Maryam Adel Saharkhiz 《Arabian Journal of Geosciences》2018,11(3):53
The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff. 相似文献
8.
The present article reports studies to develop a univariate model to forecast the summer monsoon (June–August) rainfall over India. Based on the data pertaining to the period 1871–1999, the trend and stationarity within the time series have been investigated. After revealing the randomness and non-stationarity within the time series, the autoregressive integrated moving average (ARIMA) models have been attempted and the ARIMA(0,1,1) has been identified as a suitable representative model. Consequently, an autoregressive neural network (ARNN) model has been attempted and the neural network has been trained as a multilayer perceptron with the extensive variable selection procedure. Sigmoid non-linearity has been used while training the network. Finally, a three-three-one architecture of the ARNN model has been obtained and after thorough statistical analysis the supremacy of ARNN has been established over ARIMA(0,1,1). The usefulness of ARIMA(0,1,1) has also been described. 相似文献
9.
The transfer function of time-dependent models is classically inferred by the ordinary least squares (OLS) techniques. This OLS technique assumes independence of the residuals with time. However, in practical cases, this hypothesis is often not justified producing inefficient estimation of the transfer function. When the residuals constitute an autoregressive process, we propose to apply the Box-Jenkins' method to model the residuals, and to modify in a simple manner the primary convolution equation. Then, a multivariate regression technique is used to infer the transfer function of the new equation producing time-independent residuals. This three-step autoregressive deconvolution technique is particularly efficient for time series analysis. The reconstitution and the forecasting of real data are improved efficiently. Theoretically, the proposed method can be extended to the convolution equations for which the residuals follow a moving average or an autoregressive-moving average process, but the mathematical formulation is no longer direct and explicit. For this general case, we propose to approximate the moving average or the autoregressive-moving average process by an autoregressive process of sufficient order, and then the transfer function. Two case studies in hydrogeology will be used to illustrate the procedure. 相似文献
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自适应神经模糊推理系统(ANFIS)在水文模型综合中的应用 总被引：1，自引：0，他引：1
由于目前已有很多比较成熟的流域水文模型,因此我们可以选用几个流域水文模型进行并行运算,来同时模拟流域降雨—径流关系。在相同的降雨输入情况下,不同模型得到的模拟流量必然会有所不同,模型效率系数和模拟精度也会不同。因此,如何将不同模型的模拟结果进行综合以进一步提高流量模拟精度是一个关键问题。本文选用自适应神经模糊推理系统(ANFIS)作为水文模型综合平台,以牧马河流域为试验区域,对两个并行运算水文模型(三水源新安江模型和总径流响应模型)的结果进行综合处理,得到了更稳健的流量模拟结果,大大提高了模型效率和模拟精度。该方法值得在实践中借鉴。 相似文献
12.
流域输沙的估算是水资源管理中广泛面临的问题。基于时间序列自回归滑动平均(ARIMA)预测模型,分别对补远江曼安水文站1993～2008年雨季、旱季月平均含沙量资料进行建模拟合。综合AIC值、相对误差,确定模型的阶数,运用Marquardt非线性最小二乘法估计模型参数,建立ARIMA预测模型。经检验,雨季AIC=-61.046,旱季AIC=-131.785,相对误差低于20%的合格率分别为92.1%、76.9%,残差序列均为白噪声序列,表明旱季ARIMA(1,1,1)、雨季ARIMA(1,1,2)模型较为合理。应用模型对2009～2011年曼安水文站的雨季、旱季平均含沙量进行了预测,实现了河流输沙状况的短期预报。 相似文献
13.
针对目前长期径流预报中物理成因考虑较少的问题,以丹江口水库为例,在分析影响径流物理背景的基础上,研究前期气象因子与水库秋汛期入库径流过程的相关关系,识别影响径流的大气环流与海温等物理因子,利用主成分分析法提取主要预报信息,建立了包含大气环流因子、海温因子等气象物理信息以及前期降雨、径流等水文信息作为预报因子集的三层BP神经网络预报模型.利用1956~2008年秋汛期9、10月入库径流量进行模拟与试报,并与仅采用前期降雨径流的预测模型进行了比较,结果显示基于物理成因分析的预测模型稳定性良好,模拟及试报精度较高,9、10月试报精度平均提高约30%,分别达到87.5%和75%,并对预报年份中的丰枯特征有较好的体现. 相似文献
14.
为分析黔中水利枢纽工程的水源区——三岔河上游的水资源演变规律,利用常规线性回归和滑动平均法、Pettitt法以及Morlet小波分析等方法,对该流域近50年降水和径流水资源要素进行趋势性、突变性和周期性等多维特征分析。研究表明:降水和径流整体减少或增加趋势不明显,但其丰枯年份交替频繁,起伏波动较大;突变检验结果显示该区水文序列均在2003年存在突变年份,且均为不显著性突变年份;Morlet小波周期性分析可以看出研究区水文序列都存在一个14a左右的周期变化。上述分析成果一定程度上可为该区域水资源开发提供依据。 相似文献
15.
不同时间尺度的中长期水文预报研究 总被引：1，自引：0，他引：1
为研究中长期水文预报时间尺度对预报精度的影响,选取最近邻抽样回归模型与基于小波分析的组合模型对长江干流典型断面不同时间尺度的径流序列进行中长期径流预报。将1980~2012年的逐日径流资料经过时间聚集方法转换成三天、周、旬、半月、月、双月、季、半年、九月、年等10个不同时间尺度,对高场、寸滩、宜昌、螺山、汉口、大通6个典型断面的径流进行拟合和预报。结果表明:随着预报时间尺度增加,预报精度呈现先降低后提高的趋势,其中,在月时间尺度上预报效果最差,三天和年尺度上预报效果相对较好。 相似文献
16.
回顾了中国水文预报技术的发展历程,阐述了中国水文预报技术从经验相关法、流域水文模型,到预报系统发展的整个过程。较详细地介绍了中国常用的水文预报方法、模型和预报系统,及其各自特点和应用条件等,客观评价了中国水文预报技术的水平,深入分析了高强度人类活动引起的下垫面变化和以全球变暖为主要特征的气候变化对流域产流和汇流机制的影响,分析了变化环境下水文预报面临的问题和挑战。展望了中国水文预报技术的发展方向,指出了中国未来水文预报技术的研究重点。 相似文献
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概念性水文模型数量众多,判断模型是否适合研究流域可以通过模拟结果来体现,但是熟悉流域的产汇流特性可以筛选模型,从根源上大量减少工作量,也可以解决相似流域无资料的问题。选取6种概念性水文模型,以马渡王、板桥和志丹这3个半湿润与半干旱流域为研究区域,探讨流域特性与模型结构之间的关系,并通过降雨和地形地貌分析其对模型模拟结果的影响。研究结果表明,流域地形及植被对产汇流过程有重要影响,由于局部产流现象严重,河道坡度影响大于流域平均坡度,当区域气候条件相差不大时,地形地貌比降雨对流域产汇流特性影响更大。因此对于水文模型的选择,可以在熟悉流域产汇流特性的基础上因地制宜,必要时可以增加适合研究流域的模块来获得更好的预报,在半干旱与半湿润流域,同时具有蓄满和超渗机制的模型能得到更好的应用。 相似文献
19.
Seiyed Mossa Hosseini Najmeh Mahjouri Sajjad Bagheri 《Environmental Earth Sciences》2014,72(9):3531-3548
Analyzing groundwater hydrologic equations related to karstic aquifers and spring hydrograph simulation have become the focus of many researches. Having double or triple porosity structure, mixed flow nature, and varying conduit permeability have made these formations become complex heterogenic systems with great temporal and spatial hydrodynamic variability. In this paper, a conditional sequential gaussian simulation (SGS) is used to simulate monthly flow data of five karstic springs with different hydrogeological properties, located in Zagros Mountain Chain, in western Iran. To evaluate the performance of the SGS algorithm, the results are compared with those of an autoregressive integrated moving average (ARIMA) model. The results demonstrate the efficiency of the SGS model in simulation of monthly flows compared to the ARIMA model. They also show the suitability of this model for handling uncertainty associated with karstic spring flows through generation of several equally probable stochastic realizations. 相似文献
20.
水文模型的参数优化率定一直以来是水文预报领域的重要研究内容,当水文模型的结构确定后,水文模型参数的选择对水文模型整体性能和水文预报结果的好坏有着至关重要的影响.针对传统水文模型参数优选采用单一目标不能充分全面挖掘水文观测资料中蕴含的水文特征信息的缺陷,本文以新安江三水源模型为例,尝试采用多目标优化算法优化率定水文模型,算例应用分析表明,通过合理的选择目标函数的种类和数目,采用多目标进化算法优化率定模型参数,可以获得相对于单目标率定模型参数更优的结果.进一步,研究工作针对模型参数优化的结果进行分析,可以明显看出模型参数优化中存在“异参同效”现象,为后续模型参数不确定性分析等相关研究工作的开展做好了铺垫. 相似文献