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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.
P. D. Sreekanth P. D. Sreedevi Shakeel Ahmed N. Geethanjali 《Environmental Earth Sciences》2011,62(6):1301-1310
Prediction of water level is an important task for groundwater planning and management when the water balance consistently
tends toward negative values. In Maheshwaram watershed situated in the Ranga Reddy District of Andhra Pradesh, groundwater
is overexploited, and groundwater resources management requires complete understanding of the dynamic nature of groundwater
flow. Yet, the dynamic nature of groundwater flow is continually changing in response to human and climatic stresses, and
the groundwater system is too intricate, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models
are introduced into groundwater science as a powerful, flexible, statistical modeling technique to address complex pattern
recognition problems. This study presents the comparison of two methods, i.e., feed-forward neural network (FFNN) trained
with Levenberg–Marquardt (LM) algorithm compared with a fuzzy logic adaptive network-based fuzzy inference system (ANFIS)
model for better accuracy of the estimation of the groundwater levels of the Maheshwaram watershed. The statistical indices
used in the analysis were the root mean square error (RMSE), regression coefficient (R
2) and error variation (EV).The results show that FFNN-LM and ANFIS models provide better accuracy (RMSE = 4.45 and 4.94, respectively,
R
2 is 93% for both models) for estimating groundwater levels well in advance for the above location. 相似文献
3.
M. Irfan Yesilnacar Erkan Sahinkaya Muhsin Naz Bestamin Ozkaya 《Environmental Geology》2008,56(1):19-25
Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from
the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation
schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN)
model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain.
The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples
to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial
fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity,
groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP)
algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm
was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental
data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed
model application. 相似文献
4.
Accurate and reliable prediction of shallow groundwater level is a critical component in water resources management. Two nonlinear models, WA–ANN method based on discrete wavelet transform (WA) and artificial neural network (ANN) and integrated time series (ITS) model, were developed to predict groundwater level fluctuations of a shallow coastal aquifer (Fujian Province, China). The two models were testified with the monitored groundwater level from 2000 to 2011. Two representative wells are selected with different locations within the study area. The error criteria were estimated using the coefficient of determination (R 2), Nash–Sutcliffe model efficiency coefficient (E), and root-mean-square error (RMSE). The best model was determined based on the RMSE of prediction using independent test data set. The WA–ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ITS models. The results of the study indicate the potential of WA–ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies. 相似文献
5.
Monitoring of regional groundwater levels provides important information for quantifying groundwater depletion and assessing impacts on the environment Historically,groundwater level monitoring wells in Beijing Plain,China,were installed for assessing groundwater resources and for monitoring the cone of depression.Monitoring wells are clustered around well fields and urban areas.There is urgent need to upgrade the existing monitoring wells to a regional groundwater level monitoring network to acquire information for integrated water resources management.A new method was proposed for designing a regional groundwater level monitoring network.The method is based on groundwater regime zone mapping.Groundwater regime zone map delineates distinct areas of possible different groundwater level variations and is useful for locating groundwater monitoring wells.This method was applied to Beijing Plain to upgrade a regional groundwater level monitoring network. 相似文献
6.
Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment 总被引：4，自引：3，他引：1
The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool. 相似文献
7.
区域地下水位监测网优化设计方法 总被引：8，自引：4，他引：8
区域地下水位监测提供了定量评价含水层地下水位持续下降及其对环境影响必不可少的信息。历史上的地下水位监测网是为了评价地下水资源或监测水源地降落漏斗而设立的,目前它们已经不能适应为流域水资源综合管理提供必需的信息。本文在综述国际地下水位监测现状的基础上,介绍了区域地下水位监测网优化设计的方法。采用地理信息系统编制的地下水动态类型图为地下水位监测井位置的选择提供了坚实的水文地质基础;克里金插值法能定量评价监测网观测值绘制的地下水位等高线的精度,因而可以用来定量设计地下水位监测网;时间序列分析和统计检验提供了优化地下水位监测频率的定量标准。这些方法已被应用于北京平原、乌鲁木齐河流域和济南岩溶泉域,其成果将在本刊分期发表。 相似文献
8.
9.
吉林省西部是我国主要粮食产区,但区内农业水利规划管理同时面临潜水资源与生态环境双重风险。近20年来,区内曾尝试多种水资源利用模式,但缺少不同模式应用效果的定量化对比。文章建立了不同水资源利用模式,对比分析各模式的水资源与次生盐碱化风险。以洮儿河流域为例,采用循环神经网络预测2019—2023年该地区大气降水和地表水对地下水补给量;通过随机数值模拟预测现状开采、连续干旱、无序开采、地下水库建设、节水灌溉、旱田改水田6种情形下,区内潜水水位空间分布特征。以防止次生盐碱化为目标,定义水位埋深上限为1 m;以含水介质厚度为参考,定义水位埋深下限为12 m。遴选适合吉林省西部地区地下水资源可持续利用模式。结果显示:无序开采是导致区内水资源枯竭的主要诱因;地下水库建设和旱改水工程有助于潜水资源维护,但长期运行可加剧生态环境风险。节水灌溉（净采强度为2.0×108～3.0×108 m3/a）是降低区内水资源风险和生态环境风险的最佳方式。文章采用的神经网络—随机模拟分析方法成功预测了地下水位变化驱动因子和地下水位中长期变化趋势,为我国干旱半干旱地区潜水资源利用方案制定提供了新方法。 相似文献
10.
SOM-RBF神经网络模型在地下水位预测中的应用 总被引：1，自引：0，他引：1
利用自组织映射(SOM)聚类模型优化径向基函数神经网络(RBFN)隐层节点的方法,减小了RBFN由于自身结构问题在地下水水位预测中产生的误差。采用SOM对已有样本进行聚类,利用聚类后的二维分布图确定隐层节点的数目,并根据聚类结果计算径向基函数的宽度,确定径向基函数的中心,由此建立SOM-RBFN模型。以吉林市丰满区二道乡为例,采用2000—2009年观测的地下水位动态资料,利用SOM-RBFN模型对地下水位进行预测,验证其准确性,并分别以5、7、10a的地下水位动态数据为研究样本建立模型,考查样本数量对预测结果的影响。研究结果表明:SOM-RBFN模型预测地下水水位过程中,均方根误差(RMSE)的均值为0.43,有效系数(CE)的均值为0.52,均达到较高标准,因此SOM-RBFN模型可以作为有效而准确的地下水水位预测方法;同时RBF7的RMSE和CE均值分别为0.38和0.68,结果优于RBF5和RBF10,这就意味着在模型计算中样本数量不会直接影响预测结果的精度。 相似文献
11.
以贵阳市地铁2号线三桥站主体结构基坑抗浮为研究对象,根据长观孔3~5年地下水位与降雨量关系对地下水位动态变化进行分析,提出一种定量计算抗浮水位的取值方法:抗浮水位值包括三个部分,勘察期间场区地下水最高水位（Hkmax）、可能的意外补给造成该层地下水位的上升值（ΔH0）及该层地下水相对勘察时的最大变幅值（ΔHe）;长观孔地下水位呈雨季升高、枯季下降的变化规律,最高水位出现在6、7月份;通过对4、5、6月份的降雨量与观测孔水位进行线性拟合,得到地下水位变化量与月降雨量变化量的线性变化关系;结合历史降雨量推测场区地下水位的最大升幅为2.26 m,进而计算场区的抗浮水位为1 128.46 m。 相似文献
12.
M. J. Alizadeh A. Shabani M. R. Kavianpour 《International Journal of Environmental Science and Technology》2017,14(11):2399-2410
A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R 2 and RMSE. 相似文献
13.
In many rural communities, groundwater is used to meet the water demand of the community and for the irrigation of cultivating areas. The quality of groundwater can be adversely affected by agricultural activities and finally groundwater quality may become unsuitable for human consumption and irrigation, as in the Harran Plain. Hence, monitoring groundwater quality by cost-effective techniques is necessary, as especially unconfined aquifers are vulnerable to contamination. This study presents an artificial neural network model predicting sodium adsorption ratio (SAR) and sulfate concentration in the unconfined aquifer of the Harran Plain. Samples from 24 observation wells were analyzed monthly for 1?year. Electrical conductivity, pH, groundwater level, temperature, total hardness and chloride were used as input parameters in the predictions. The best back-propagation (BP) algorithm and neuron numbers were determined for the optimization of the model architecture. The Levenberg?CMarquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 20 for both parameters. The model tracked the experimental data very closely both for SAR (R?=?0.96) and sulfate (R?=?0.98). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application. 相似文献
14.
砂土地震液化预测的人工神经网络模型 总被引：13，自引：6，他引：7
在简要分析BP算法的基础上,应用BP网络的理论与方法,选取烈度、震中距、平均粒径、不均匀系数、地下水埋深、砂层埋深、标贯击数、剪应力比等8个实测指标,建立了砂土液化预测的神经网络模型。通过实例计算与模型评价、验证了该模型的科学性、高效性并较规范法、Seed简化法等传统方法具有更高的预测精度,说明人工神经网络是解决非线性问题的有效方法之一。 相似文献
15.
Groundwater being an important component of the hydrological cycle as it sustains the streamflow during precipitation free
periods and is a major source of water supply. The dependence on the groundwater has increased drastically over the years
leading to over exploitation of the aquifers. Therefore, it is imperative to assess the extent of exploitation and analyse
the groundwater level scenarios in the area of interest. The existence of a trend in a hydrological time series can be detected
by statistical tests. The present study investigates the application of various methods for identification of trends in groundwater
levels in few blocks of Sagar district, which faces severe water scarcity owing to the declining groundwater levels. The non-parametric
Kendal rank correlation test as well as the parametric linear regression test has been used for trend detection based on the
analysis of the seasonal groundwater levels. Kendal’s rank correlation test, has been applied to identify the trend persisting
in the data and the linear regression test is used to identify the significance of the slope. The analysis indicates that
the time series of groundwater levels are cyclical with characteristics of seasonal variation in all the blocks coupled with
a declining trend at Sagar, Khurai and Bina. 相似文献
16.
北京平原地下水水位监测网优化 总被引：10，自引：0，他引：10
文章在北京市地下水水位监测现状基础上,分潜水和承压水对北京平原地下水监测网的监测密度和监测频率进行了优化设计。主要采用编制地下水动态类型图的方法进行了地下水水位监测网的优化,克里金插值法能定量评价依据监测网观测值绘制的地下水水位等高线的精度,因而可以用来评价监测优化结果。并根据时间序列分析和统计检验提供的定量标准优化了地下水水位监测频率。优化后,北京平原共有监测孔400眼,其中利用原有监测孔300眼,新设计监测孔100眼,手工监测频率由原来的每月6次优化为每月1次,专项高频率监测可以由地下水自动监测仪实现。文中还对地下水自动监测仪(DIVER)的监测结果和手工监测结果进行了对比评价,提出了地下水水位监测网的维护、管理措施和信息发布方式。 相似文献
17.
Forecasting of groundwater level in hard rock region using artificial neural network 总被引：2，自引：0，他引：2
In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has
already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs
of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has
urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the
aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and
potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a
method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction
tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development
of groundwater resources. 相似文献
18.
The study presented in this paper constitutes an initial approach to the problematic task of evaluating the effects of possible
climate change on natural water recharge to aquifers. To estimate such effects, a purpose-designed mathematical model termed
Estimation of Recharge in Over-exploited Aquifers (ERAS) has been used. It enables to simulate the monthly water recharge
to an aquifer, provided that prior knowledge of the exploitation to which it is subjected and the variation caused by these
two actions on the piezometric level of the aquifer is available. The basic data required for its application are: precipitation,
temperature, groundwater extraction, stored groundwater surface and storage coefficient. The main advantage presented by this
model is its independence of the mechanism by which water is displaced through the ground and within the unsaturated zone.
The ERAS code was applied to four over-exploited karstic aquifers in Alto Vinalopó (Alicante, Spain) with the goal of generating
a synthesized series of values for natural groundwater recharge in each of the aquifers for the 100 years of the twentieth
century. Each series thus obtained after being grouped into decades was subjected to statistical processing, which revealed
that in every case a logarithmically decreasing trend was present. 相似文献
19.
Estimating groundwater discharge into a lake through underwater springs by using GIS technologies 总被引：2，自引：1，他引：1
Estimating groundwater recharge in a freshwater body such as a deep lake is often a problem for hydrologists, since direct measurements are costly and difficult to implement. This study attempts to calculate the groundwater discharge in a lake using a simple water balance model, water level measurements, aerial photographs and GIS technologies. In particular, a Digital Terrain Model (DTM) for the lake has been developed, which in combination with GIS (Geographical Information System) software and water level fluctuations has provided the lake's monthly water storages. Additional hydrologic elements, including land and water uses, overland flow and evapotranspiration, have been estimated and incorporated in a water balance model which after extensive analysis have provided credible monthly values for the groundwater recharge in Lake Trichonis. This particular methodology can be widely applied in catchments with similar hydrologic regimes and can provide reliable, cost-efficient estimations of groundwater recharges into water bodies.Abbreviations GIS Geographical Information Systems - DTM Digital Terrain Model 相似文献
20.
Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas 总被引：2，自引：0，他引：2
V. Uddameri 《Environmental Geology》2007,51(6):885-895
Reliable forecasts of monthly and quarterly fluctuations in groundwater levels are necessary for short- and medium-term planning
and management of aquifers to ensure proper service of seasonal demands within a region. Development of physically based transient
mathematical models at this time scale poses considerable challenges due to lack of suitable data and other uncertainties.
Artificial neural networks (ANN) possess flexible mathematical structures and are capable of mapping highly nonlinear relationships.
Feed-forward neural network models were constructed and trained using the back-percolation algorithm to forecast monthly and
quarterly time-series water levels at a well that taps into the deeper Evangeline formation of the Gulf Coast aquifer in Victoria,
TX. Unlike unconfined formations, no causal relationships exist between water levels and hydro-meteorological variables measured
near the vicinity of the well. As such, an endogenous forecasting model using dummy variables to capture short-term seasonal
fluctuations and longer-term (decadal) trends was constructed. The root mean square error, mean absolute deviation and correlation
coefficient (R) were noted to be 1.40, 0.33 and 0.77 m, respectively, for an evaluation dataset of quarterly measurements and 1.17, 0.46,
and 0.88 m for an evaluative monthly dataset not used to train or test the model. These statistics were better for the ANN
model than those developed using statistical regression techniques. 相似文献